Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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18 pages, 15492 KB  
Article
D3-YOLOv10: Improved YOLOv10-Based Lightweight Tomato Detection Algorithm Under Facility Scenario
by Ao Li, Chunrui Wang, Tongtong Ji, Qiyang Wang and Tianxue Zhang
Agriculture 2024, 14(12), 2268; https://doi.org/10.3390/agriculture14122268 - 11 Dec 2024
Cited by 20 | Viewed by 1990
Abstract
Accurate and efficient tomato detection is one of the key techniques for intelligent automatic picking in the area of precision agriculture. However, under the facility scenario, existing detection algorithms still have challenging problems such as weak feature extraction ability for occlusion conditions and [...] Read more.
Accurate and efficient tomato detection is one of the key techniques for intelligent automatic picking in the area of precision agriculture. However, under the facility scenario, existing detection algorithms still have challenging problems such as weak feature extraction ability for occlusion conditions and different fruit sizes, low accuracy on edge location, and heavy model parameters. To address these problems, this paper proposed D3-YOLOv10, a lightweight YOLOv10-based detection framework. Initially, a compact dynamic faster network (DyFasterNet) was developed, where multiple adaptive convolution kernels are aggregated to extract local effective features for fruit size adaption. Additionally, the deformable large kernel attention mechanism (D-LKA) was designed for the terminal phase of the neck network by adaptively adjusting the receptive field to focus on irregular tomato deformations and occlusions. Then, to further improve detection boundary accuracy and convergence, a dynamic FM-WIoU regression loss with a scaling factor was proposed. Finally, a knowledge distillation scheme using semantic frequency prompts was developed to optimize the model for lightweight deployment in practical applications. We evaluated the proposed framework using a self-made tomato dataset and designed a two-stage category balancing method based on diffusion models to address the sample class-imbalanced issue. The experimental results demonstrated that the D3-YOLOv10 model achieved an mAP0.5 of 91.8%, with a substantial reduction of 54.0% in parameters and 64.9% in FLOPs, compared to the benchmark model. Meanwhile, the detection speed of 80.1 FPS more effectively meets the demand for real-time tomato detection. This study can effectively contribute to the advancement of smart agriculture research on the detection of fruit targets. Full article
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29 pages, 2353 KB  
Article
Does Common Agricultural Policy Influence Regional Disparities and Environmental Sustainability in European Union Countries?
by Alina Georgiana Manta, Nicoleta Mihaela Doran, Roxana Maria Bădîrcea, Gabriela Badareu, Claudia Gherțescu and Cătălin Valentin Mihai Lăpădat
Agriculture 2024, 14(12), 2242; https://doi.org/10.3390/agriculture14122242 - 7 Dec 2024
Cited by 11 | Viewed by 2591
Abstract
This study examines the impact of the European Union’s Common Agricultural Policy (CAP) funds, specifically the European Agricultural Fund for Rural Development (FEADR) and the European Agricultural Guarantee Fund (FEGA), on a range of economic, social, and environmental outcomes across European regions. Utilizing [...] Read more.
This study examines the impact of the European Union’s Common Agricultural Policy (CAP) funds, specifically the European Agricultural Fund for Rural Development (FEADR) and the European Agricultural Guarantee Fund (FEGA), on a range of economic, social, and environmental outcomes across European regions. Utilizing Fully Modified Ordinary Least Squares (FMOLS) estimators, this research analyses 13 equations corresponding to various dependent variables, including employment rates, poverty levels, agricultural productivity, and environmental indicators such as greenhouse gas emissions and renewable energy production from agriculture. This analysis covers the period from 2010 to 2021, and draws upon a balanced sample of 301 observations to ensure robust estimations. Results indicate that both FEADR and FEGA payments significantly influence these regional outcomes, though the effects vary depending on the specific economic or environmental indicator examined. The findings reveal that while FEADR payments positively impact rural employment, agricultural income, and renewable energy production, they are less effective in addressing poverty reduction and productivity enhancement. Conversely, FEGA payments exhibit a stronger influence on agricultural productivity and income, but have mixed effects on environmental sustainability. This study highlights significant regional disparities, suggesting that the allocation of CAP funds is uneven in its impact across regions. The implications for policymakers are clear: a more tailored approach is required to enhance the effectiveness of CAP funds in meeting diverse regional needs, particularly in promoting economic development while minimizing environmental harm. This study also emphasizes the need for further research to explore alternative policy mechanisms and innovative agricultural practices that can bridge the gaps identified in the current CAP framework. Limitations of this study include data availability and the inherent complexity of agricultural systems, which may affect the generalizability of the results across different EU member states. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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17 pages, 8396 KB  
Article
Design and Testing of a Tractor Automatic Navigation System Based on Dynamic Path Search and a Fuzzy Stanley Model
by Bingbo Cui, Xinyu Cui, Xinhua Wei, Yongyun Zhu, Zhen Ma, Yan Zhao and Yufei Liu
Agriculture 2024, 14(12), 2136; https://doi.org/10.3390/agriculture14122136 - 25 Nov 2024
Cited by 30 | Viewed by 1937
Abstract
Smart agriculture development mainly depends on the intelligence and reliability of autonomous agricultural machinery. Automatic navigation systems (ANSs) play a key role in intelligent agricultural machinery design, as they not only reduce farmers’ workloads but also improve their land utilization rates. In this [...] Read more.
Smart agriculture development mainly depends on the intelligence and reliability of autonomous agricultural machinery. Automatic navigation systems (ANSs) play a key role in intelligent agricultural machinery design, as they not only reduce farmers’ workloads but also improve their land utilization rates. In this paper, a tractor ANS based on dynamic path search and a fuzzy Stanley model (FSM) was designed, and its capability for whole-field path tracking was tested. First, the tracking performance of the steering control module was validated after the automatic reconstruction of the tractor platform. Then, a navigation decision system was established based on a unified reference waypoint search framework, where the path generation for whole-field coverage was presented. Finally, the gain coefficient of the Stanley model (SM) was adjusted adaptively according to the tracking error by utilizing the fuzzy logic controller. Subsequently, the developed tractor ANS was tested in the field. The experiment’s results indicate that the FSM outperformed the SM in straight path tracking and whole-field path tracking. When the tractor traveled at a speed of 1 m/s, the maximum lateral tracking error for the straight path was 10 cm, and the average lateral tracking error was 5.2 cm, showing improvements of 16.7% and 10.3% compared to the SM. Whole-field autonomous navigation showed that the maximum lateral tracking error was improved from 34 cm for the SM to 27 cm for the FSM, a reduction of approximately 20.6%, illustrating the superiority of the FSM in the application of whole-field path tracking. As the maximum tracking error of whole-field autonomous navigation appears in the turning stage, where tractors often stop working, the designed ANS satisfies the requirements of a self-driving system for unmanned tractors. Full article
(This article belongs to the Section Agricultural Technology)
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26 pages, 5826 KB  
Review
Neuromorphic Computing for Smart Agriculture
by Shize Lu and Xinqing Xiao
Agriculture 2024, 14(11), 1977; https://doi.org/10.3390/agriculture14111977 - 4 Nov 2024
Cited by 21 | Viewed by 4870
Abstract
Neuromorphic computing has received more and more attention recently since it can process information and interact with the world like the human brain. Agriculture is a complex system that includes many processes of planting, breeding, harvesting, processing, storage, logistics, and consumption. Smart devices [...] Read more.
Neuromorphic computing has received more and more attention recently since it can process information and interact with the world like the human brain. Agriculture is a complex system that includes many processes of planting, breeding, harvesting, processing, storage, logistics, and consumption. Smart devices in association with artificial intelligence (AI) robots and Internet of Things (IoT) systems have been used and also need to be improved to accommodate the growth of computing. Neuromorphic computing has a great potential to promote the development of smart agriculture. The aim of this paper is to describe the current principles and development of the neuromorphic computing technology, explore the potential examples of neuromorphic computing applications in smart agriculture, and consider the future development route of the neuromorphic computing in smart agriculture. Neuromorphic computing includes artificial synapses, artificial neurons, and artificial neural networks (ANNs). A neuromorphic computing system is expected to improve the agricultural production efficiency and ensure the food quality and safety for human nutrition and health in smart agriculture in the future. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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21 pages, 537 KB  
Article
A Study of the Impact of New Quality Productive Forces on Agricultural Modernization: Empirical Evidence from China
by Qingqing Huang, Wenjing Guo and Yanfei Wang
Agriculture 2024, 14(11), 1935; https://doi.org/10.3390/agriculture14111935 - 30 Oct 2024
Cited by 25 | Viewed by 4938
Abstract
New quality productive forces are the fundamental driving force for the progress of human civilization. To deeply explore the relationship between new quality productive forces and agricultural modernization, data from 30 provinces in China from 2011 to 2022 were selected to construct the [...] Read more.
New quality productive forces are the fundamental driving force for the progress of human civilization. To deeply explore the relationship between new quality productive forces and agricultural modernization, data from 30 provinces in China from 2011 to 2022 were selected to construct the index system of new quality productive forces and agricultural modernization, carry out scientific measurement, and conduct empirical analysis using the fixed effect model. The results show that new quality productivity can significantly promote agricultural modernization. The new quality productive force has a significant effect on the modernization of agriculture in the eastern, middle, and western regions of China, but the effect is more prominent in the middle and western areas. New productive forces are significantly and positively associated with agricultural modernization in both main grain-producing and non-main grain-producing areas, but the effect is greater in main grain-producing areas. The upgrading of the agricultural industrial structure plays a mediating effect between new productive forces and agricultural modernization. There is a single-threshold effect of the new productive forces empowering agricultural modernization. Accordingly, to better utilize new productive forces to empower agricultural modernization, we should fully activate the talent engine and cultivate modern “new farmers”; strive to build efficient agriculture by taking scientific and technological innovation as the driving force; and promote the sustainable development of agriculture by taking agricultural green production as the orientation. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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16 pages, 5829 KB  
Article
Fruit Distribution Density Estimation in YOLO-Detected Strawberry Images: A Kernel Density and Nearest Neighbor Analysis Approach
by Lili Jiang, Yunfei Wang, Chong Wu and Haibin Wu
Agriculture 2024, 14(10), 1848; https://doi.org/10.3390/agriculture14101848 - 19 Oct 2024
Cited by 17 | Viewed by 1791
Abstract
Precise information on strawberry fruit distribution is of significant importance for optimizing planting density and formulating harvesting strategies. This study applied a combined analysis of kernel density estimation and nearest neighbor techniques to estimate fruit distribution density from YOLOdetected strawberry images. Initially, an [...] Read more.
Precise information on strawberry fruit distribution is of significant importance for optimizing planting density and formulating harvesting strategies. This study applied a combined analysis of kernel density estimation and nearest neighbor techniques to estimate fruit distribution density from YOLOdetected strawberry images. Initially, an improved yolov8n strawberry object detection model was employed to obtain the coordinates of the fruit centers in the images. The results indicated that the improved model achieved an accuracy of 94.7% with an mAP@0.5~0.95 of 87.3%. The relative error between the predicted and annotated coordinates ranged from 0.002 to 0.02, demonstrating high consistency between the model predictions and the annotated results. Subsequently, based on the strawberry center coordinates, the kernel density estimation algorithm was used to estimate the distribution density in the strawberry images. The results showed that with a bandwidth of 200, the kernel density estimation accurately reflected the actual strawberry density distribution, ensuring that all center points in high-density regions were consistently identified and delineated. Finally, to refine the strawberry distribution information, a comprehensive method based on nearest neighbor analysis was adopted, achieving target area segmentation and regional density estimation in the strawberry images. Experimental results demonstrated that when the distance threshold ϵ was set to 600 pixels, the correct grouping rate exceeded 94%, and the regional density estimation results indicated a significant positive correlation between the number of fruits and regional density. This study provides scientific evidence for optimizing strawberry planting density and formulating harvesting sequences, contributing to improved yield, harvesting efficiency, and reduced fruit damage. In future research, this study will further explore dynamic models that link fruit distribution density, planting density, and fruit growth status. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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57 pages, 1526 KB  
Review
Sustainable Poultry Feeding Strategies for Achieving Zero Hunger and Enhancing Food Quality
by Petru Alexandru Vlaicu, Arabela Elena Untea and Alexandra Gabriela Oancea
Agriculture 2024, 14(10), 1811; https://doi.org/10.3390/agriculture14101811 - 14 Oct 2024
Cited by 20 | Viewed by 12316
Abstract
As global demand increases for poultry products, innovative feeding strategies that reduce resource efficiency and improve food safety are urgently needed. This paper explores the potential of alternative sustainable poultry feeding strategies aimed at achieving SDG2 (Zero Hunger) while increasing production performance and [...] Read more.
As global demand increases for poultry products, innovative feeding strategies that reduce resource efficiency and improve food safety are urgently needed. This paper explores the potential of alternative sustainable poultry feeding strategies aimed at achieving SDG2 (Zero Hunger) while increasing production performance and food quality, focusing on the potential recycling of by-products, plants, and food waste derived from fruits, vegetables, and seeds, which account for up to 35% annually. The paper provides a review analysis of the nutritional (protein, fat, fiber, and ash) and minerals (i.e., calcium, phosphorus, zinc, manganese, copper, and iron) content as well as the bioactive compounds (polyphenols, antioxidants, carotenoids, fatty acids, and vitamins) of alternative feed ingredients, which can contribute to resource efficiency, reduce dependency on conventional feeds, and lower production costs by 25%. The nutritional benefits of these alternative feed ingredients, including their effects on poultry production and health, and their potential for improving poultry product quality, are presented. Carrot, paprika, rosehip, and some berry waste represent a great source of carotenoids, polyphenols, and vitamins, while the seed meals (flax, rapeseed, and sea buckthorn) have been reported to enhance the essential fatty acid composition in eggs and meat. Numerous plants (basil, sage, rosemary, and lettuce) are natural reservoirs of bioactive compounds with benefits for both animal and food products. Some challenges in implementing these alternative sustainable feeding strategies, including inconsistencies in quality and availability, the presence of anti-nutrients, and regulatory barriers, are also explored. In conclusion, future research directions in sustainable poultry feeding with alternative feed ingredients should be considered to achieve SDG2. Full article
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27 pages, 2140 KB  
Review
Applications of Plant Essential Oils in Pest Control and Their Encapsulation for Controlled Release: A Review
by Rocío Ayllón-Gutiérrez, Laura Díaz-Rubio, Myriam Montaño-Soto, María del Pilar Haro-Vázquez and Iván Córdova-Guerrero
Agriculture 2024, 14(10), 1766; https://doi.org/10.3390/agriculture14101766 - 6 Oct 2024
Cited by 19 | Viewed by 10332
Abstract
Essential oils (EOs) are volatile products derived from the secondary metabolism of plants with antioxidant, antimicrobial, and pesticidal properties. They have traditionally been used in medicine, cosmetics, and food additives. In agriculture, EOs stand out as natural alternatives for pest control, as they [...] Read more.
Essential oils (EOs) are volatile products derived from the secondary metabolism of plants with antioxidant, antimicrobial, and pesticidal properties. They have traditionally been used in medicine, cosmetics, and food additives. In agriculture, EOs stand out as natural alternatives for pest control, as they show biocidal, repellent, and antifeedant effects. However, they are highly volatile compounds and susceptible to oxidation, which has limited their use as pesticides. This has led to exploring micro- and nano-scale encapsulation to protect these compounds, improving their stability and allowing for a controlled release. Various encapsulation techniques exist, such as emulsification, ionic gelation, and complex coacervation. Nanoemulsions are useful in the food industry, while ionic gelation and complex coacervation offer high encapsulation efficiency. Materials such as chitosan, gelatin-gum-Arabic, and cyclodextrins are promising for agricultural applications, providing stability and the controlled release of EOs. Encapsulation technology is still under development but offers sustainable alternatives to conventional agrochemicals. This article reviews the potential of EOs in pest management and encapsulation techniques that enhance their efficacy. Full article
(This article belongs to the Special Issue Preparation, Function and Application of Agrochemicals)
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18 pages, 19579 KB  
Review
Fermented Feed in Broiler Diets Reduces the Antinutritional Factors, Improves Productive Performances and Modulates Gut Microbiome—A Review
by Nicoleta Corina Predescu, Georgeta Stefan, Mihaela Petronela Rosu and Camelia Papuc
Agriculture 2024, 14(10), 1752; https://doi.org/10.3390/agriculture14101752 - 4 Oct 2024
Cited by 16 | Viewed by 8556
Abstract
The aim of this review is to highlight the most beneficial effects of dietary fermented feed in correlation with decreasing the antinutrient concentration in vegetal matrices usually used for broiler nutrition. Rational feed formulation is critical for animals because it improves animal performance, [...] Read more.
The aim of this review is to highlight the most beneficial effects of dietary fermented feed in correlation with decreasing the antinutrient concentration in vegetal matrices usually used for broiler nutrition. Rational feed formulation is critical for animals because it improves animal performance, and provides the animal with the necessary nutrients to develop strong bones, muscles and tissues, and a properly functioning immune system. Fermentation of animal feed is useful as compounds with high molecular mass are converted into energy and compounds with lower molecular mass in the presence of enzymes produced mainly by bacteria and yeasts. Fermentation products contain probiotic compounds with beneficial effects on the health of the animal microbiome. Feed fermentation has other roles such as converting antinutrients into beneficial substances for animal organisms, and some studies have shown that fermentation of feed decreases the risk of antinutrient components presence. For the bibliographic research, different platforms were used (PubMed, Science Direct, MDPI resources), and numerous words or combinations of terms were used to find the latest information. Fermented feed utilization has been shown to enhance growth performance while promoting a healthier gut microbiome in animals. Full article
(This article belongs to the Special Issue Rational Use of Feed to Promote Animal Healthy Feeding)
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20 pages, 5666 KB  
Article
Design and Testing of an Electric Side-Mounted Cabbage Harvester
by Ze Liu, Enguang Wang, Hanping Mao, Zhiyu Zuo, Haitao Peng, Mingxue Zhao, Yongsheng Yu and Zhikang Li
Agriculture 2024, 14(10), 1741; https://doi.org/10.3390/agriculture14101741 - 2 Oct 2024
Cited by 13 | Viewed by 1669
Abstract
To address the limitations of current cabbage harvesters in China, which are often designed for a single variety and lack adaptability to different cabbage varieties, we developed an electric side-mounted cabbage harvester suitable for field operations in the Jiangsu region of China. This [...] Read more.
To address the limitations of current cabbage harvesters in China, which are often designed for a single variety and lack adaptability to different cabbage varieties, we developed an electric side-mounted cabbage harvester suitable for field operations in the Jiangsu region of China. This design is informed by the statistical analysis of the physical and agronomic parameters of major cabbage varieties. The harvester consists of key components, including an extraction device, a leaf-stripping device, a clamping and conveying device, and a root-cutting device. Powered by a 120 Ah direct current (DC) power source, it is capable of performing cabbage extraction, feeding, clamping, conveying, root cutting, and boxing in a single operation for three hours. Through theoretical analysis of the key components, specific parameters were determined, and field tests were conducted to verify the design. The results of the field experiments indicate that all components of the cabbage harvester operated effectively. Optimal performance was observed when the extraction roller speed was set between 100 and 110 RPM, the conveyor belt speed at 60 RPM, and the cutter speed between 160 and 220 RPM, resulting in a low cabbage harvest loss rate. The harvest loss rates from the three experiments were 11.3%, 13.3%, and 12%, respectively, which meets the mechanical harvesting requirements for cabbage. Full article
(This article belongs to the Section Agricultural Technology)
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25 pages, 6876 KB  
Article
An Ultrasonic Ridge-Tracking Method Based on Limiter Sliding Window Filter and Fuzzy Pure Pursuit Control for Ridge Transplanter
by Wei Liu, Jinhao Zhou, Yutong Liu, Tengfei Zhang, Meng Yan, Ji Chen, Chunjian Zhou, Jianping Hu and Xinxin Chen
Agriculture 2024, 14(10), 1713; https://doi.org/10.3390/agriculture14101713 - 29 Sep 2024
Cited by 14 | Viewed by 1411
Abstract
There are various types of fruits and vegetables that need to be planted on ridges. In order to allow for seedlings with a certain row space and seedling space, the ridge transplanter should be able to track along the ridge. Therefore, an ultrasonic [...] Read more.
There are various types of fruits and vegetables that need to be planted on ridges. In order to allow for seedlings with a certain row space and seedling space, the ridge transplanter should be able to track along the ridge. Therefore, an ultrasonic ridge-tracking method and system were developed to let the ridge transplanter track the ridge accurately. The ultrasonic ridge-tracking method mainly contains a limiter sliding window filtering algorithm and a fuzzy look-ahead distance decision model. The limiter sliding window filtering algorithm was proposed to filter the abnormal measuring results to avoid disoperation of the steering mechanism. Moreover, the fuzzy look-ahead distance decision model was proposed to determine the optimal look-ahead distance in order to obtain a desirable tracking performance. Additionally, a comparison experiment of the proposed ultrasonic ridge-tracking method and the universal pure pursuit method was conducted. The experimental results show that the greatest mean absolute errors of the lateral deviations of the ultrasonic ridge-tracking method and universal pure pursuit were 10.56 mm and 13.11 mm. The greatest maximum absolute errors of the lateral deviations of the ultrasonic ridge-tracking method and universal pure pursuit were 18.87 mm and 23.23 mm. In addition, the greatest root mean square error of the lateral deviation of the ultrasonic ridge-tracking method and the universal pure pursuit method were 13.52 mm and 15.66 mm. According to the ridge-tracking performance of the proposed ultrasonic ridge-tracking method, it can be used in practical transplanting conditions. Moreover, in other fields, robots or intelligent machinery can also apply the proposed ultrasonic ridge-tracking method to track objects similar to ridges. Full article
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20 pages, 4757 KB  
Article
Combining Transfer Learning and Ensemble Algorithms for Improved Citrus Leaf Disease Classification
by Hongyan Zhu, Dani Wang, Yuzhen Wei, Xuran Zhang and Lin Li
Agriculture 2024, 14(9), 1549; https://doi.org/10.3390/agriculture14091549 - 7 Sep 2024
Cited by 13 | Viewed by 2113
Abstract
Accurate categorization and timely control of leaf diseases are crucial for citrus growth. We proposed the Multi-Models Fusion Network (MMFN) for citrus leaf diseases detection based on model fusion and transfer learning. Compared to traditional methods, the algorithm (integrating transfer learning Alexnet, VGG, [...] Read more.
Accurate categorization and timely control of leaf diseases are crucial for citrus growth. We proposed the Multi-Models Fusion Network (MMFN) for citrus leaf diseases detection based on model fusion and transfer learning. Compared to traditional methods, the algorithm (integrating transfer learning Alexnet, VGG, and Resnet) we proposed can address the issues of limited categories, slow processing speed, and low recognition accuracy. By constructing efficient deep learning models and training and optimizing them with a large dataset of citrus leaf images, we ensured the broad applicability and accuracy of citrus leaf disease detection, achieving high-precision classification. Herein, various deep learning algorithms, including original Alexnet, VGG, Resnet, and transfer learning versions Resnet34 (Pre_Resnet34) and Resnet50 (Pre_Resnet50) were also discussed and compared. The results demonstrated that the MMFN model achieved an average accuracy of 99.72% in distinguishing between diseased and healthy leaves. Additionally, the model attained an average accuracy of 98.68% in the classification of multiple diseases (citrus huanglongbing (HLB), greasy spot disease and citrus canker), insect pests (citrus leaf miner), and deficiency disease (zinc deficiency). These findings conclusively illustrate that deep learning model fusion networks combining transfer learning and integration algorithms can automatically extract image features, enhance the automation and accuracy of disease recognition, demonstrate the significant potential and application value in citrus leaf disease classification, and potentially drive the development of smart agriculture. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring)
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24 pages, 8395 KB  
Article
Linear Active Disturbance Rejection Control System for the Travel Speed of an Electric Reel Sprinkling Irrigation Machine
by Lingdi Tang, Wei Wang, Chenjun Zhang, Zanya Wang, Zeyu Ge and Shouqi Yuan
Agriculture 2024, 14(9), 1544; https://doi.org/10.3390/agriculture14091544 - 6 Sep 2024
Cited by 21 | Viewed by 1285
Abstract
The uniformity of the travel speed of electric reel sprinkling irrigation machines is a key factor affecting irrigation quality. However, conventional PID control is susceptible to sudden disturbances under complex farmland conditions, leading to reduced speed uniformity. To enhance the robustness of the [...] Read more.
The uniformity of the travel speed of electric reel sprinkling irrigation machines is a key factor affecting irrigation quality. However, conventional PID control is susceptible to sudden disturbances under complex farmland conditions, leading to reduced speed uniformity. To enhance the robustness of the control system, it is necessary to investigate new disturbance rejection control algorithms and their effects. Therefore, a kinematic model of the reel sprinkling irrigation machine and a brushless DC (BLDC) motor model were established, and a linear active disturbance rejection control (LADRC) strategy based on improved particle swarm optimization (IPSO) was proposed. The simulation results show that under variable speed conditions, the system exhibits no overshoot, with an adjustment time of 0.064 s; under variable load conditions, the speed vibration amplitude is less than 0.3%. The field test results indicate that at travel speeds of 10 m/h and 30 m/h, the maximum absolute deviation rate under IPSO-LADRC control is reduced by 27.07% and 13.98%, respectively, compared to PID control. The control strategy based on IPSO-LADRC effectively improves the control accuracy and robustness under complex farmland conditions, providing a reference for enhancing the control performance of other electric agricultural machinery. Full article
(This article belongs to the Special Issue Application of Modern Agricultural Equipment in Crop Cultivation)
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20 pages, 1575 KB  
Review
From Waste to Value in Circular Economy: Valorizing Grape Pomace Waste through Vermicomposting
by Georgiana-Diana Gabur, Carmen Teodosiu, Daniela Fighir, Valeriu V. Cotea and Iulian Gabur
Agriculture 2024, 14(9), 1529; https://doi.org/10.3390/agriculture14091529 - 5 Sep 2024
Cited by 15 | Viewed by 4362
Abstract
From the vineyard to the bottle, the winemaking process generates a variety of by-products, such as vinasses, spent filter cakes, grape pomace, grape lees, and vine shoots. To avoid damaging the environment and to reduce economic impacts, the by-products and wastes must be [...] Read more.
From the vineyard to the bottle, the winemaking process generates a variety of by-products, such as vinasses, spent filter cakes, grape pomace, grape lees, and vine shoots. To avoid damaging the environment and to reduce economic impacts, the by-products and wastes must be handled, disposed of, or recycled properly. This review focuses on an environmentally friendly approach to the management and added value of winemaking by-products, such as grape pomace or grape marc, by using vermicomposting. Vermicompost is a well-known organic fertilizer with potential uses in soil bioremediation and the conservation of soil health. To achieve environmental neutral agriculture practices, vermicomposting is a promising tool for resilient and sustainable viticulture and winemaking. Vermicomposting is a simple, highly beneficial, and waste-free method of converting organic waste into compost with high agronomic value and a sustainable strategy in line with the principles of the circular economy. Full article
(This article belongs to the Section Agricultural Soils)
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14 pages, 1654 KB  
Review
Crop Rotation and Diversification in China: Enhancing Sustainable Agriculture and Resilience
by Yuzhu Zou, Zhenshan Liu, Yan Chen, Yin Wang and Shijing Feng
Agriculture 2024, 14(9), 1465; https://doi.org/10.3390/agriculture14091465 - 28 Aug 2024
Cited by 36 | Viewed by 11242
Abstract
Crop rotation and diversification (CRD) are crucial strategies in sustainable agriculture, offering multiple benefits to both farmers and the environment. By alternating crops or introducing diverse plant species, CRD practices improve soil fertility, reduce pest populations, and enhance nutrient availability. For example, legume-based [...] Read more.
Crop rotation and diversification (CRD) are crucial strategies in sustainable agriculture, offering multiple benefits to both farmers and the environment. By alternating crops or introducing diverse plant species, CRD practices improve soil fertility, reduce pest populations, and enhance nutrient availability. For example, legume-based rotations increase soil nitrogen levels through biological nitrogen fixation, reducing the need for synthetic fertilizers. Moreover, these practices promote more efficient water and nutrient use, reducing the reliance on synthetic fertilizers and minimizing the risk of pests and diseases. This review synthesizes findings from recent research on the role of CRD in enhancing sustainable agriculture and resilience, highlighting the potential contributions of these practices towards climate change mitigation and adaptation. Specific crop rotation systems, such as the cereal–legume rotation in temperate regions and the intercropping of maize with beans in tropical environments, are reviewed to provide a comprehensive understanding of their applicability in different agroecological contexts. The review also addresses the challenges related to implementing CRD practices, such as market demand and knowledge transfer, and suggests potential solutions to encourage broader adoption. Lastly, the potential environmental benefits, including carbon sequestration and reduced greenhouse gas emissions, are discussed, highlighting the role of CRD in building resilient agricultural systems. Collectively, this review paper emphasizes the importance of CRD methods as sustainable agricultural practices and provides key insights for researchers and farmers to effectively integrate these practices into farming systems. Full article
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24 pages, 6908 KB  
Article
LP-YOLO: A Lightweight Object Detection Network Regarding Insect Pests for Mobile Terminal Devices Based on Improved YOLOv8
by Yue Yu, Qi Zhou, Hao Wang, Ke Lv, Lijuan Zhang, Jian Li and Dongming Li
Agriculture 2024, 14(8), 1420; https://doi.org/10.3390/agriculture14081420 - 21 Aug 2024
Cited by 13 | Viewed by 3216
Abstract
To enhance agricultural productivity through the accurate detection of pests under the constrained resources of mobile devices, we introduce LP-YOLO, a bespoke lightweight object detection framework optimized for mobile-based insect pest identification. Initially, we devise lightweight components, namely LP_Unit and LP_DownSample, to serve [...] Read more.
To enhance agricultural productivity through the accurate detection of pests under the constrained resources of mobile devices, we introduce LP-YOLO, a bespoke lightweight object detection framework optimized for mobile-based insect pest identification. Initially, we devise lightweight components, namely LP_Unit and LP_DownSample, to serve as direct substitutes for the majority of modules within YOLOv8. Subsequently, we develop an innovative attention mechanism, denoted as ECSA (Efficient Channel and Spatial Attention), which is integrated into the network to forge LP-YOLO(l). Moreover, assessing the trade-offs between parameter reduction and computational efficiency, considering both the backbone and head components of the network, we use structured pruning methods for the pruning process, culminating in the creation of LP-YOLO(s). Through a comprehensive series of evaluations on the IP102 dataset, the efficacy of LP-YOLO as a lightweight object detection model is validated. By incorporating fine-tuning techniques during training, LP-YOLO(s)n demonstrates a marginal mAP decrease of only 0.8% compared to YOLOv8n. However, it achieves a significant reduction in parameter count by 70.2% and a remarkable 40.7% increase in FPS, underscoring its efficiency and performance. Full article
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25 pages, 980 KB  
Review
Drought Stress in Quinoa: Effects, Responsive Mechanisms, and Management through Biochar Amended Soil: A Review
by Muhammad Zubair Akram, Angela Libutti and Anna Rita Rivelli
Agriculture 2024, 14(8), 1418; https://doi.org/10.3390/agriculture14081418 - 21 Aug 2024
Cited by 10 | Viewed by 3736
Abstract
Chenopodium quinoa Willd. (quinoa), a highly nutritious pseudocereal, is a promising crop to address global food insecurity challenges intensified by population growth and climate change. However, drought stress remains a significant constraint for quinoa cultivation. The plant exhibits several morphophysiological adaptations to water [...] Read more.
Chenopodium quinoa Willd. (quinoa), a highly nutritious pseudocereal, is a promising crop to address global food insecurity challenges intensified by population growth and climate change. However, drought stress remains a significant constraint for quinoa cultivation. The plant exhibits several morphophysiological adaptations to water stress conditions, including root system modifications, reduced growth rate, leaf abscission, and stomatal closure. While these adaptations enhance drought tolerance, they can also negatively impact plant growth, potentially through alterations in root architecture, physiological changes, e.g., stomatal regulations, and anatomical changes. Different studies have suggested that soil amendment with biochar, a pyrolyzed organic material, can improve quinoa growth and productivity under drought stress conditions. Biochar application to the soil significantly enhances soil physiochemical characteristics and maintains plant water status, thereby promoting plant growth and potentially mitigating the negative consequences of drought on quinoa production. This review focuses on the current understanding of quinoa behavior under drought stress and the potential of soil amendment with biochar as a management strategy. We summarize existing research on applying biochar-amended soil to alleviate quinoa drought stress. Full article
(This article belongs to the Section Crop Production)
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22 pages, 8540 KB  
Article
Design and Experiment of Planting Mechanism of Automatic Transplanter for Densely Planted Vegetables
by Jiawei Shi, Jianping Hu, Jing Li, Wei Liu, Rencai Yue, Tengfei Zhang and Mengjiao Yao
Agriculture 2024, 14(8), 1357; https://doi.org/10.3390/agriculture14081357 - 14 Aug 2024
Cited by 11 | Viewed by 3179
Abstract
The planting mechanism of existing transplanters cannot meet the agronomic requirements of planting densely planted vegetables with multiple rows, small plant spacing, and small row spacing. In order to solve this current problem, an eight-row duckbill planting mechanism driven by a motor and [...] Read more.
The planting mechanism of existing transplanters cannot meet the agronomic requirements of planting densely planted vegetables with multiple rows, small plant spacing, and small row spacing. In order to solve this current problem, an eight-row duckbill planting mechanism driven by a motor and a cylinder was designed. According to the agronomic guidance and mechanism design requirements for transplanting densely planted vegetable seedlings, this paper analyzes the working principle of the planting mechanism, establishes its kinematic theoretical model, and determines the structural parameters of the driving device and opening and closing device in the planting mechanism. Aimed at the problem of large planting resistance when eight-row planting end effectors of the planting mechanism are planting at the same time, based on the existing research, three duckbill planting end effectors with double incisions, four incisions, and conical structures were selected, and the planting process was simulated using an EDEM 2022-RecurDyn 2024 coupling simulation. The single-factor analysis method and the interactive factor Box–Behnken response surface analysis method were used. It is concluded that the duckbill end effector with double incisions has the smallest planting resistance, and the rationality of the mechanism design is preliminarily verified. A planting resistance measurement platform was built based on the STM32 platform and HX711 module, and a planting resistance test of the duckbill planting end effector was carried out to verify the correctness of the planting mechanism simulation results. The planting mechanism performance test was carried out, and the test results showed that the planting qualification rate of the prototype reached 96.62%, the planting spacing variation coefficient was only 3.55%, and the planting efficiency reached about 7135 plants/h, which met the agronomic requirements of small plant spacing and small row spacing for densely planted vegetables and verified the feasibility and practicality of the planting mechanism. Full article
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24 pages, 1777 KB  
Article
Implementation of Large Language Models and Agricultural Knowledge Graphs for Efficient Plant Disease Detection
by Xinyan Zhao, Baiyan Chen, Mengxue Ji, Xinyue Wang, Yuhan Yan, Jinming Zhang, Shiyingjie Liu, Muyang Ye and Chunli Lv
Agriculture 2024, 14(8), 1359; https://doi.org/10.3390/agriculture14081359 - 14 Aug 2024
Cited by 12 | Viewed by 6745
Abstract
This study addresses the challenges of elaeagnus angustifolia disease detection in smart agriculture by developing a detection system that integrates advanced deep learning technologies, including Large Language Models (LLMs), Agricultural Knowledge Graphs (KGs), Graph Neural Networks (GNNs), representation learning, and neural-symbolic reasoning techniques. [...] Read more.
This study addresses the challenges of elaeagnus angustifolia disease detection in smart agriculture by developing a detection system that integrates advanced deep learning technologies, including Large Language Models (LLMs), Agricultural Knowledge Graphs (KGs), Graph Neural Networks (GNNs), representation learning, and neural-symbolic reasoning techniques. The system significantly enhances the accuracy and efficiency of disease detection through an innovative graph attention mechanism and optimized loss functions. Experimental results demonstrate that this system significantly outperforms traditional methods across key metrics such as precision, recall, and accuracy, with the graph attention mechanism excelling in all aspects, particularly achieving a precision of 0.94, a recall of 0.92, and an accuracy of 0.93. Furthermore, comparative experiments with various loss functions further validate the effectiveness of the graph attention loss mechanism in enhancing model performance. This research not only advances the application of deep learning in agricultural disease detection theoretically but also provides robust technological tools for disease management and decision support in actual agricultural production, showcasing broad application prospects and profound practical value. Full article
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21 pages, 19442 KB  
Article
Pasture Quality Assessment through NDVI Obtained by Remote Sensing: A Validation Study in the Mediterranean Silvo-Pastoral Ecosystem
by João Serrano, Shakib Shahidian, Luís Paixão, José Marques da Silva and Luís Lorenzo Paniágua
Agriculture 2024, 14(8), 1350; https://doi.org/10.3390/agriculture14081350 - 13 Aug 2024
Cited by 10 | Viewed by 3198
Abstract
Monitoring the evolution of pasture availability and quality throughout the growing season is the basis of grazing management in extensive Mediterranean livestock systems. Remote sensing (RS) is an innovative tool that, among many other applications, is being developed for detailed spatial and temporal [...] Read more.
Monitoring the evolution of pasture availability and quality throughout the growing season is the basis of grazing management in extensive Mediterranean livestock systems. Remote sensing (RS) is an innovative tool that, among many other applications, is being developed for detailed spatial and temporal pasture quality assessment. The aim of the present study is to evaluate the potential of satellite images (Sentinel-2) to assess indicators of pasture quality (pasture moisture content, PMC, crude protein, CP and neutral detergent fiber, NDF) using the normalized difference vegetation index (NDVI). Field measurements were conducted over three years at eight representative fields of the biodiversity and variability of dryland pastures in Portugal. A total of 656 georeferenced pasture samples were collected and processed in the laboratory. The results show a significant correlation between pasture quality parameters (PMC, CP and NDF) obtained in standard laboratory methods and NDVI satellite-derived data (R2 of 0.72, 0.75, and 0.50, respectively). The promising findings obtained in this large-scale validation study (three years and eight fields) encourage further research (i) to test and develop other vegetation indexes for monitoring pasture nutritive value; (ii) to extend this research to pastures of the other Mediterranean countries, building large and representative datasets and developing more robust and accurate monitoring models based on freely available Sentinel-2 images; (iii) to implement an extension program for agricultural managers to popularize the use of these technological tools as the basis of grazing and pasture management. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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28 pages, 2192 KB  
Review
Integrated Nutrient Management of Fruits, Vegetables, and Crops through the Use of Biostimulants, Soilless Cultivation, and Traditional and Modern Approaches—A Mini Review
by Awais Ali, Genhua Niu, Joseph Masabni, Antonio Ferrante and Giacomo Cocetta
Agriculture 2024, 14(8), 1330; https://doi.org/10.3390/agriculture14081330 - 9 Aug 2024
Cited by 25 | Viewed by 12316
Abstract
The increasing population, its requirements for food, and the environmental impact of the excessive use of inputs make crop production a pressing challenge. Integrated nutrient management (INM) has emerged as a critical solution by maximizing nutrient availability and utilization for crops and vegetables. [...] Read more.
The increasing population, its requirements for food, and the environmental impact of the excessive use of inputs make crop production a pressing challenge. Integrated nutrient management (INM) has emerged as a critical solution by maximizing nutrient availability and utilization for crops and vegetables. This review paper highlights the potential benefits of INM for various vegetables and field crops and explores the conceptual strategies, components, and principles underlying this approach. Studies have shown that a wide range of vegetables and field crops benefit from INM, in terms of increased yield and improvements in yield attributes, nutrient contents and uptake, growth parameters, and various physiological and biochemical characteristics. This paper discusses biostimulants, their categories, and their impact on plant propagation, growth, photosynthesis, seed germination, fruit set, and quality. Additionally, this review explores modern sustainable soilless production techniques such as hydroponics, aeroponics, and aquaponics. These cultivation methods highlight the advancements of controlled-environment agriculture (CEA) and its contribution to nutrient management, food security and minimizing the environmental footprint. The review concludes by proposing methods and fostering discussions on INM’s future development, while acknowledging the challenges associated with its adoption. Finally, this review emphasizes the substantial evidence supporting INM as a novel and ecologically sound strategy for achieving sustainable agricultural production worldwide. Full article
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28 pages, 1249 KB  
Review
A Review on Mastitis in Dairy Cows Research: Current Status and Future Perspectives
by Piotr Stanek, Paweł Żółkiewski and Ewa Januś
Agriculture 2024, 14(8), 1292; https://doi.org/10.3390/agriculture14081292 - 5 Aug 2024
Cited by 22 | Viewed by 20816
Abstract
One of the most serious diseases affecting dairy cattle, causing significant losses both in breeding and economy, is mastitis, an inflammation of the mammary gland. Due to the economic importance of this issue, many research teams are striving to develop an easy-to-apply and, [...] Read more.
One of the most serious diseases affecting dairy cattle, causing significant losses both in breeding and economy, is mastitis, an inflammation of the mammary gland. Due to the economic importance of this issue, many research teams are striving to develop an easy-to-apply and, most importantly, effective method to prevent mastitis. The use of traditional methods for mastitis detecting and treating, as well as improvement in hygienic conditions, have not yielded the expected results in combating this disease combating. Currently, the main task is to find the tools that would allow for the rapid detection of mastitis and the improvement of udder health in cows while maintaining high milk production, which is essential for the profitability of dairy cattle farming. Accurate and rapid diagnostic tools, with the simultaneous capability of identifying pathogens, may help to reduce losses. Sufficient sensitivity and specificity for tests are required to minimize the number of false-positive and false-negative cases. Efforts are also being made to determine the optimal threshold value for detecting the disease at its earliest possible stage. The estimation of somatic cell count (SCC) as a phenotypic indicator of mastitis is widely used. A more precise parameter for accurately describing udder health is the differential somatic cell count (DSCC). The well-known California Mastitis Test (CMT) is an inexpensive, easy, and rapid method for mastitis detection useful on farms. The latest diagnostic methods for mastitis utilize tests based on the activity of N-acetyl-β-d-glucosaminidase (NAGase) or lactate dehydrogenase (LDH) as well as the determination of acute phase proteins (APPs) in blood serum and milk (such as haptoglobin, serum amyloid A, fibrinogen, and ceruloplasmin). Research also focuses on the genomic improvement of mastitis resistance in successive generations, and for this purpose, many quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs) have been identified. In recent years, immunotherapy has become an increasingly common area of research, including vaccinations, T/B cell immunotherapy, RNA immunotherapy, epigenetic immunotherapy, stem cell therapy, and native secretory factors. An important aspect of the control of mastitis is the implementation of strategies that focus primarily on preventing the disease through appropriate breeding and farm management practices. In the forthcoming years, a significant challenge will be the development of universal diagnostic and therapeutic strategies that can be effectively implemented as alternatives to antibiotic therapy. Future research should prioritize the advancement of preventive and therapeutic techniques, such as immunotherapies, bacteriocins, herbal therapy, and nanoparticle technology. Full article
(This article belongs to the Special Issue Mastitis in Dairy Cattle: Prevention Strategies and Treatment Methods)
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15 pages, 12486 KB  
Article
Rapid and Non-Destructive Geographical Origin Identification of Chuanxiong Slices Using Near-Infrared Spectroscopy and Convolutional Neural Networks
by Yuxing Huang, Yang Pan, Chong Liu, Lan Zhou, Lijuan Tang, Huayi Wei, Ke Fan, Aichen Wang and Yong Tang
Agriculture 2024, 14(8), 1281; https://doi.org/10.3390/agriculture14081281 - 3 Aug 2024
Cited by 10 | Viewed by 1531
Abstract
Ligusticum Chuanxiong, a perennial herb of considerable medicinal value commonly known as Chuanxiong, holds pivotal importance in sliced form for ensuring quality and regulating markets through geographical origin identification. This study introduces an integrated approach utilizing Near-Infrared Spectroscopy (NIRS) and Convolutional Neural Networks [...] Read more.
Ligusticum Chuanxiong, a perennial herb of considerable medicinal value commonly known as Chuanxiong, holds pivotal importance in sliced form for ensuring quality and regulating markets through geographical origin identification. This study introduces an integrated approach utilizing Near-Infrared Spectroscopy (NIRS) and Convolutional Neural Networks (CNNs) to establish an efficient method for rapidly determining the geographical origin of Chuanxiong slices. A dataset comprising 300 samples from 6 distinct origins was analyzed using a 1D-CNN model. In this study, we initially established a traditional classification model. By utilizing the Spectrum Outlier feature in TQ-Analyst 9 software to exclude outliers, we have enhanced the performance of the model. After evaluating various spectral preprocessing techniques, we selected Savitzky–Golay filtering combined with Multiplicative Scatter Correction (S-G + MSC) to process the raw spectral data. This approach significantly improved the predictive accuracy of the model. After 2000 iterations of training, the CNN model achieved a prediction accuracy of 92.22%, marking a 12.09% improvement over traditional methods. The application of the Class Activation Mapping algorithm not only visualized the feature extraction process but also enhanced the traditional model’s classification accuracy by an additional 7.41% when integrated with features extracted from the CNN model. This research provides a powerful tool for the quality control of Chuanxiong slices and presents a novel perspective on the quality inspection of other agricultural products. Full article
(This article belongs to the Section Agricultural Product Quality and Safety)
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16 pages, 2012 KB  
Review
The Impact of Heat Stress on the Physiological, Productive, and Reproductive Status of Dairy Cows
by Dorin Țogoe and Nicoleta Andreea Mincă
Agriculture 2024, 14(8), 1241; https://doi.org/10.3390/agriculture14081241 - 27 Jul 2024
Cited by 12 | Viewed by 9903
Abstract
Climate change is a global problem with an important influence on farm animals, so the entire veterinary medical industry is working to combat the effects of heat stress. In recent years, global warming has been correlated with physiological changes in adaptation that lead [...] Read more.
Climate change is a global problem with an important influence on farm animals, so the entire veterinary medical industry is working to combat the effects of heat stress. In recent years, global warming has been correlated with physiological changes in adaptation that lead to a decrease in milk production and quality. We have chosen to study these mechanisms that are based on hormonal imbalances (LH, TSH, and prolactin) and general imbalances (apathy and lack of appetite). Full article
(This article belongs to the Special Issue The Influence of Environmental Factors on Farming Animals)
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30 pages, 4765 KB  
Review
The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review
by Rui-Feng Wang and Wen-Hao Su
Agriculture 2024, 14(8), 1225; https://doi.org/10.3390/agriculture14081225 - 25 Jul 2024
Cited by 45 | Viewed by 6108
Abstract
The potato is a key crop in addressing global hunger, and deep learning is at the core of smart agriculture. Applying deep learning (e.g., YOLO series, ResNet, CNN, LSTM, etc.) in potato production can enhance both yield and economic efficiency. Therefore, researching efficient [...] Read more.
The potato is a key crop in addressing global hunger, and deep learning is at the core of smart agriculture. Applying deep learning (e.g., YOLO series, ResNet, CNN, LSTM, etc.) in potato production can enhance both yield and economic efficiency. Therefore, researching efficient deep learning models for potato production is of great importance. Common application areas for deep learning in the potato production chain, aimed at improving yield, include pest and disease detection and diagnosis, plant health status monitoring, yield prediction and product quality detection, irrigation strategies, fertilization management, and price forecasting. The main objective of this review is to compile the research progress of deep learning in various processes of potato production and to provide direction for future research. Specifically, this paper categorizes the applications of deep learning in potato production into four types, thereby discussing and introducing the advantages and disadvantages of deep learning in the aforementioned fields, and it discusses future research directions. This paper provides an overview of deep learning and describes its current applications in various stages of the potato production chain. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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25 pages, 3002 KB  
Review
Saline–Alkali Soil Reclamation Contributes to Soil Health Improvement in China
by Wei Zhu, Shiguo Gu, Rui Jiang, Xin Zhang and Ryusuke Hatano
Agriculture 2024, 14(8), 1210; https://doi.org/10.3390/agriculture14081210 - 23 Jul 2024
Cited by 24 | Viewed by 7049
Abstract
Soil salinization is a significant threat to soil health, especially to the agricultural ecosystem; it reduces vegetation biomass, destroys ecosystem diversity, and limits land use efficiency. This area of investigation has garnered extensive attention in China, especially in the arid and semi-arid areas, [...] Read more.
Soil salinization is a significant threat to soil health, especially to the agricultural ecosystem; it reduces vegetation biomass, destroys ecosystem diversity, and limits land use efficiency. This area of investigation has garnered extensive attention in China, especially in the arid and semi-arid areas, totaling 7.66 × 106 ha. A variety of theoretical research and technology developments have contributed to soil water and salt regulation and the screening of salt-tolerant varieties to improve nutrient utilization efficiency and microbial control and reduce ecological problems due to saline-based obstacles. These techniques can be classified into physical treatments, chemical treatments, biological treatments, and combined treatments; these different measures are all aimed at primarily solving saline–alkali stress. In general, the improvement and utilization of saline–alkali soil contribute to soil health improvement, concentrating on high-quality development, food security, ecological security, cultivated land protection, and agricultural upgrading. However, the risks of various technologies in the practical production process should be highlighted; green and healthy measures are still expected to be applied to saline–alkali land. Full article
(This article belongs to the Special Issue Feature Review in Agricultural Soils—Intensification of Soil Health)
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26 pages, 14424 KB  
Article
An Integrated Route and Path Planning Strategy for Skid–Steer Mobile Robots in Assisted Harvesting Tasks with Terrain Traversability Constraints
by Ricardo Paul Urvina, César Leonardo Guevara, Juan Pablo Vásconez and Alvaro Javier Prado
Agriculture 2024, 14(8), 1206; https://doi.org/10.3390/agriculture14081206 - 23 Jul 2024
Cited by 13 | Viewed by 2717
Abstract
This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem [...] Read more.
This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity; and (ii) a local planning strategy using Informed Rapidly-exploring Random Tree (IRRT*) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT* planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrate the effectiveness of the proposed strategy. In particular, the IRRT* algorithm outperformed RRT and RRT* with 96.1% and 97.6% smoother paths, respectively. The IRRT* also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture. Full article
(This article belongs to the Section Agricultural Technology)
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25 pages, 1299 KB  
Review
Effect of Magnetic Field and UV-C Radiation on Postharvest Fruit Properties
by Maciej Gąstoł and Urszula Błaszczyk
Agriculture 2024, 14(7), 1167; https://doi.org/10.3390/agriculture14071167 - 17 Jul 2024
Cited by 15 | Viewed by 4503
Abstract
This review focuses on the recent information on the effect of different types of magnetic fields (MFs) and ultraviolet radiation (UV-C) on the processes that may finally affect fruit quality and its storage potential. Firstly, the biological effect of MFs on every plant’s [...] Read more.
This review focuses on the recent information on the effect of different types of magnetic fields (MFs) and ultraviolet radiation (UV-C) on the processes that may finally affect fruit quality and its storage potential. Firstly, the biological effect of MFs on every plant’s growth and development level is described. The magnetic field interacts with a plant’s metabolism and changes the permeability of membranes affecting cells’ homeostasis. It also could affect early seedling development, stimulating enzyme activity and protein synthesis, and later on nutrient and water uptake of adult plants. In some cases, it makes plants more resilient, increasing their tolerance to environmental stresses. Also, MF treatment could lower the disease index of plants, thus improving the internal and external fruit quality indices. The second part of this review focuses on interesting perspectives of using UV-C radiation to reduce postharvest fruit diseases, but also to delay fruit ripening and senescence. The application of UV-C light to combat postharvest infections is associated with two mechanisms of action, such as direct elimination of microorganisms located on the fruit surface and indirect triggering of the plant’s defense reaction. Moreover, the use of hormetic doses of UV-C can additionally increase the nutritional properties of fresh fruit, lead to the accumulation of desired phytochemicals such as polyphenols, for example, to increase anthocyanin or resveratrol content, or elevate antioxidant activity. Full article
(This article belongs to the Section Agricultural Product Quality and Safety)
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26 pages, 3490 KB  
Review
Ammonia Emissions and Building-Related Mitigation Strategies in Dairy Barns: A Review
by Serena Vitaliano, Provvidenza Rita D’Urso, Claudia Arcidiacono and Giovanni Cascone
Agriculture 2024, 14(7), 1148; https://doi.org/10.3390/agriculture14071148 - 15 Jul 2024
Cited by 11 | Viewed by 2954
Abstract
In this systematic review, the PRISMA method was applied to examine publications from the last two decades that have investigated the noxious gaseous emissions from dairy barns. The aim was to analyse the outcomes from literature studies estimating the quantities of polluting gases [...] Read more.
In this systematic review, the PRISMA method was applied to examine publications from the last two decades that have investigated the noxious gaseous emissions from dairy barns. The aim was to analyse the outcomes from literature studies estimating the quantities of polluting gases produced in dairy barns, with a specific focus on ammonia (NH3) emissions. Various studies, among those reviewed, have used mixed effects models, mass balance approaches and dispersion methods, revealing significant variability due to different experimental protocols and environmental contexts. Key challenges include the lack of standardised measurement techniques and the limited geographical coverage of research, particularly in climatically extreme regions. This review also explores proposed methods to reduce the associated effects through mitigation strategies. Estimation of NH3 emissions is significantly influenced by the complex interactions between several factors; including animal management practices, such as controlling animal behavioural activities; manure management, like utilising practices for floor manure removal; the type of structure housing the animals, whether it is naturally or mechanically ventilated; and environmental conditions, such as the effects of temperature, wind speed, relative humidity, and ventilation rate on NH3 release in the barn. These influential components have been considered by researchers and targeted mitigation strategies have been identified. Despite growing attention to the issue, gaps in the scientific literature were identified and discussed, particularly regarding the analysis of mitigation strategies and their long-term impacts (i.e., environmental, economic and productivity-wise). The purpose of this review is to help improve research into sustainable agricultural practices and technological innovations, which are fundamental to reducing NH3 emissions and improving air quality in agricultural environments. Full article
(This article belongs to the Special Issue Greenhouse Gas Emissions in Livestock Production)
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31 pages, 4772 KB  
Review
Carbon Sequestration by Tropical Trees and Crops: A Case Study of Oil Palm
by Denis J. Murphy
Agriculture 2024, 14(7), 1133; https://doi.org/10.3390/agriculture14071133 - 12 Jul 2024
Cited by 12 | Viewed by 14068
Abstract
Carbon sequestration by photosynthetic organisms is the principal mechanism for the absorption of atmospheric CO2. Since the 1950s, however, the global carbon cycle has been distorted as increased anthropogenic CO2 emissions have greatly outstripped rates of carbon sequestration, with a [...] Read more.
Carbon sequestration by photosynthetic organisms is the principal mechanism for the absorption of atmospheric CO2. Since the 1950s, however, the global carbon cycle has been distorted as increased anthropogenic CO2 emissions have greatly outstripped rates of carbon sequestration, with a 50% increase in atmospheric CO2 levels in less than a century, leading to perturbation of global climate systems and threatening food production and social stability. In order to address the current imbalance in CO2 flux, it is important to both reduce net emissions and promote sequestration. To address the latter issue, we need to better understand the roles of systems, such as natural forests, coastal wetlands, and tropical croplands, in carbon sequestration and devise strategies to facilitate net CO2 uptake. Carbon sequestration by tropical trees and crops already removes in excess of 1000 million tonnes of atmospheric CO2 annually but is threatened by anthropogenic activities such as deforestation and the drainage of carbon-rich peatland. Improvements in carbon sequestration can be achieved by policies such as growing tropical crops as part of agroforestry systems, enforcing limitations on deforestation and the use of peatland, and auditing the carbon impact of major cropping systems in order to focus on those crops that deliver both high yields and carbon efficiency. As an initial step in this process, a detailed case study is presented on the tropical tree crop, the African oil palm, Elaeis guineensis. This analysis includes a comparison of the carbon sequestration potential of oil palm with that of tropical forests and other oil crops, the biomass sequestration potential of oil palm and current and future strategies aimed at achieving net-zero carbon targets for oil palm and related crops. Full article
(This article belongs to the Section Crop Production)
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22 pages, 2581 KB  
Article
Design and Simulation of Intra-Row Obstacle Avoidance Shovel-Type Weeding Machine in Orchard
by Weidong Jia, Kaile Tai, Xiaowen Wang, Xiang Dong and Mingxiong Ou
Agriculture 2024, 14(7), 1124; https://doi.org/10.3390/agriculture14071124 - 11 Jul 2024
Cited by 17 | Viewed by 2542
Abstract
This paper presents the design of an intra-row obstacle avoidance shovel-type weeding machine. Theoretical analysis of intra-row weeding components guided the determination of the structures and parameters for key parts, including the signal acquisition mechanism, automatic obstacle avoidance mechanism, and weeding shovel. Furthermore, [...] Read more.
This paper presents the design of an intra-row obstacle avoidance shovel-type weeding machine. Theoretical analysis of intra-row weeding components guided the determination of the structures and parameters for key parts, including the signal acquisition mechanism, automatic obstacle avoidance mechanism, and weeding shovel. Furthermore, a hydraulic system was designed to support these functions. The design aims to optimize intra-row weeding operations, reduce labor costs, enhance weed control effectiveness, and prevent collisions between weeding equipment and grapevines. Through the construction of a mathematical model, the analysis determined the necessary minimum return speed of the hydraulic cylinder for the intra-row weeding shovel to avoid grapevines. We also established a reasonable range for the extension speed of the hydraulic cylinder to minimize areas missed during weeding. Further analysis showed that using the minimum return speed of the hydraulic cylinder effectively reduced missed weeding areas. A virtual prototype model of the weeding machine was created in ADAMS. Using the coverage rate of weeding operation as the evaluation index, single-factor simulation tests determined that the extension speed of the piston rod in the obstacle avoidance hydraulic cylinder and the forward speed of the weeding machine are the main influencing factors. The preset threshold of the control system, which triggered the automatic obstacle avoidance mechanism when the obstacle avoidance rod reached a specific angle (the “Angle Threshold”), was identified as a secondary influencing factor. Other factors were considered irrelevant. Hydraulic cylinder extension speed, weeding machine forward speed, and angle threshold were chosen as the influencing factors. Following the principles of a Box–Behnken experimental design, a quadratic regression combination experiment was designed using a three-factor, three-level response surface analysis method. The evaluation criterion focused on the coverage rate of weeding operation. A regression model was developed to determine the coverage rate of the weeding operation, identifying the optimal parameters as follows: obstacle avoidance hydraulic cylinder extension speed of 120 mm/s, forward speed of the weeding machine at 0.6 m/s, and an angle threshold of 18°. The optimized coverage rate of the weeding operation achieved 86.1%. This study serves as a reference for further optimization of intra-row weeding machines in vineyards and for other crops. Full article
(This article belongs to the Section Agricultural Technology)
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18 pages, 4303 KB  
Article
The Adsorption Characteristics of Phosphorus-Modified Corn Stover Biochar on Lead and Cadmium
by Lina Zhou, Lin Chen, Yuqing Zhang, Yu Zhang, Zhifan Li, Kun Yang and Limei Chen
Agriculture 2024, 14(7), 1118; https://doi.org/10.3390/agriculture14071118 - 11 Jul 2024
Cited by 14 | Viewed by 2513
Abstract
In order to achieve the purpose of efficiently removing lead and cadmium as the main heavy metals from wastewater, this paper explores the adsorption properties of cadmium ions and lead ions on biochar under different modified conditions prepared from corn stalks as raw [...] Read more.
In order to achieve the purpose of efficiently removing lead and cadmium as the main heavy metals from wastewater, this paper explores the adsorption properties of cadmium ions and lead ions on biochar under different modified conditions prepared from corn stalks as raw materials and potassium phosphate as surface modifiers. Before preparing biochar (BC), the mass ratios of 1:1 and 1:2 (corn stalks to potassium phosphate) were used for pre-modification, and the oxygen-restricted pyrolysis processes of 350 °C, 550 °C and 750 °C were used for treatment. The study discussed the individual and composite adsorption effects of biochar on Pb2+ and Cd2+ under different conditions. The experimental results show that phosphorus modification has changed the physical and chemical properties of the original biochar. Among them, the biochar (2PBC550) with an impregnation ratio of 1:2 and a pyrolysis temperature of 550 °C (2PBC550) exhibits excellent adsorption properties. When the pH of the simulated wastewater is 5 and the amount of adsorbent is 30 mg·L−1, the maximum adsorption capacity of Pb2+ and Cd2+ is 145.48 mg·g−1 and 14.533 mg·g−1, respectively, which are 6.46 times and 3.67 times of the original biochar. The Pb2+ and Cd2+ adsorption data of 2PBC550 fit well with the quasi-secondary dynamics and Langmuir isothermal models, indicating that the adsorption process is controlled by single-layer chemical adsorption. In the composite metal system of Pb2+ and Cd2+, 2PBC550 exhibits a stronger affinity for Pb2+ than Cd2+. Through the analysis of characterization methods such as SEM, FTIR, XRD and XPS, it is proved that the adsorption of Pb2+ and Cd2+ by 2PBC550 is due to precipitation, complexation and π electron interaction. Therefore, 2PBC550 shows great application potential in the repair of wastewater containing Pb2+ or Cd2+. Full article
(This article belongs to the Special Issue Practical Application of Crop Straw Reuse in Agriculture)
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26 pages, 2026 KB  
Review
Meat of Sheep: Insights into Mutton Evaluation, Nutritive Value, Influential Factors, and Interventions
by Wenli Ding, Yanan Lu, Bowen Xu, Pan Chen, Aoyun Li, Fuchun Jian, Guangqing Yu and Shucheng Huang
Agriculture 2024, 14(7), 1060; https://doi.org/10.3390/agriculture14071060 - 30 Jun 2024
Cited by 23 | Viewed by 10791
Abstract
Meat from sheep offers an abundance of essential amino acids and trace elements essential for optimal human health and a delectable culinary delight. Because it has fewer calories and a lower cholesterol content than other meats, this succulent meat is not only delicious [...] Read more.
Meat from sheep offers an abundance of essential amino acids and trace elements essential for optimal human health and a delectable culinary delight. Because it has fewer calories and a lower cholesterol content than other meats, this succulent meat is not only delicious but also a nutritious choice. Globally, discriminating consumers have expressed profound appreciation for its irresistible flavor and nutritious composition. High-quality sheep breeds and lamb quality are in the spotlight as the market for sheep meat grows. Nevertheless, the demand for rapid growth and the use of antibiotics and other drugs have led to a shortage of high-quality mutton on the market. In the face of this emergency phenomenon, people add organic matter to the growth of mutton to improve the quality of mutton. This paper discusses the comprehensive evaluation methods of meat quality; summarizes the relationship between the nutritional components of meat and diet; discusses the genetic factors affecting meat quality attributes; feed nutrition, feeding methods, mutton storage methods, and related measures to improve the quality of mutton; and provides information on the current status of mutton and the challenges of ensuring high-quality meat supply in the future. Full article
(This article belongs to the Section Agricultural Product Quality and Safety)
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20 pages, 1970 KB  
Review
Rubber-Based Agroforestry Systems Associated with Food Crops: A Solution for Sustainable Rubber and Food Production?
by Andi Nur Cahyo, Ying Dong, Taryono, Yudhistira Nugraha, Junaidi, Sahuri, Eric Penot, Aris Hairmansis, Yekti Asih Purwestri, Andrea Akbar, Hajar Asywadi, Risal Ardika, Nur Eko Prasetyo, Dwi Shinta Agustina, Taufan Alam, Fetrina Oktavia, Siti Subandiyah and Pascal Montoro
Agriculture 2024, 14(7), 1038; https://doi.org/10.3390/agriculture14071038 - 28 Jun 2024
Cited by 10 | Viewed by 5661
Abstract
Agroforestry is often seen as a sustainable land-use system for agricultural production providing ecosystem services. Intercropping with food crops leads to equal or higher productivity than monoculture and results in food production for industry and subsistence. Low rubber price and low labor productivity [...] Read more.
Agroforestry is often seen as a sustainable land-use system for agricultural production providing ecosystem services. Intercropping with food crops leads to equal or higher productivity than monoculture and results in food production for industry and subsistence. Low rubber price and low labor productivity in smallholdings have led to a dramatic conversion of rubber plantations to more profitable crops. The literature analysis performed in this paper aimed at better understanding the ins and outs that could make rubber-based agroforestry more attractive for farmers. A comprehensive search of references was conducted in March 2023 using several international databases and search engines. A Zotero library was set up consisting of 415 scientific references. Each reference was carefully read and tagged in several categories: cropping system, country, main tree species, intercrop type, intercrop product, level of product use, discipline of the study, research topic, and intercrop species. Of the 232 journal articles, 141 studies were carried out on rubber agroforestry. Since 2011, the number of studies per year has increased. Studies on rubber-based agroforestry systems are performed in most rubber-producing countries, in particular in Indonesia, Thailand, China, and Brazil. These studies focus more or less equally on perennials (forest species and fruit trees), annual intercrops, and mixed plantations. Of the 47 annual crops associated with rubber in the literature, 20 studies dealt with rice, maize, banana, and cassava. Agronomy is the main discipline in the literature followed by socio-economy and then ecology. Only four papers are devoted to plant physiology and breeding. The Discussion Section has attempted to analyze the evolution of rubber agroforestry research, progress in the selection of food crop varieties adapted to agroforestry systems, and to draw some recommendations for rubber-based agroforestry systems associated with food crops. Full article
(This article belongs to the Section Agricultural Systems and Management)
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13 pages, 1811 KB  
Article
Matrix-Matched Calibration for the Quantitative Analysis of Pesticides in Pepper and Wheat Flour: Selection of the Best Calibration Model
by José Manuel Veiga-del-Baño, José Oliva, Miguel Ángel Cámara, Pedro Andreo-Martínez and Miguel Motas
Agriculture 2024, 14(7), 1014; https://doi.org/10.3390/agriculture14071014 - 27 Jun 2024
Cited by 11 | Viewed by 2700
Abstract
An automated package for calculating the best calibration model for matrix-matched calibration in food pesticide analysis has been developed in this study. The algorithm development in the package is based on three requirements for routine food pesticide analysis: a good working range fitness [...] Read more.
An automated package for calculating the best calibration model for matrix-matched calibration in food pesticide analysis has been developed in this study. The algorithm development in the package is based on three requirements for routine food pesticide analysis: a good working range fitness for samples with high maximum residue limits (MRLs), detection capability for pesticide analysis with MRLs close to the limit of quantitation, and a simple working range problem detection model. The requirements are combined in a simple scoring system above 100. The package has been tested in the analysis of pesticides of pepper and wheat flour. The results show that the package can be used for different pesticides quickly and visually, and also allows evaluation of matrix effects between different matrix calibrations. For the pesticides tested with the package, the weighted linear calibration gave the best score over the simple linear calibration and second-order calibration. Full article
(This article belongs to the Section Agricultural Product Quality and Safety)
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19 pages, 10686 KB  
Article
Analysis of Film Unloading Mechanism and Parameter Optimization of Air Suction-Type Cotton Plough Residual Film Recovery Machine Based on CFD—DEM Coupling
by Weiquan Fang, Xinzhong Wang, Changshun Zhu, Dianlei Han, Nan Zang and Xuegeng Chen
Agriculture 2024, 14(7), 1021; https://doi.org/10.3390/agriculture14071021 - 27 Jun 2024
Cited by 12 | Viewed by 1618
Abstract
The optimization of film-unloading and film–soil separation components can effectively improve the residual film unloading rate and reduce impurity content. So, the DEM models of soil and residual film were established and the suspension and flow characteristics under fluid action were analyzed based [...] Read more.
The optimization of film-unloading and film–soil separation components can effectively improve the residual film unloading rate and reduce impurity content. So, the DEM models of soil and residual film were established and the suspension and flow characteristics under fluid action were analyzed based on the CFD—DEM coupling simulation in this article. The matching parameters of the film-unloading and film-lifting device were optimized with the Box–Behnken test. When the wind velocity was between 1.65 and 10.54 m·s1, the film–soil separation effect was the best, with a film–impurity separation rate of 96.6%. The optimized parameter combination of the film-unloading device and film-lifting device is A = 9°, B = 40 mm, and C = 40 mm (A, B, and C represent the angle between the teeth and the normal of the air inlet, the minimum distance between the teeth and the air inlet, and the width of the air inlet, respectively). With the optimized parameter, the best film unloading effect is achieved, the minimum wind velocity of film unloading is 2.6 m·s1. This article provides theoretical and simulation methods for assessing the flow characteristics of flexible particles and parameter optimization of air suction devices, which is conducive to the high-purity recovery of residual film. Full article
(This article belongs to the Section Agricultural Technology)
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19 pages, 602 KB  
Article
The Influence of New Quality Productive Forces on High-Quality Agricultural Development in China: Mechanisms and Empirical Testing
by Li Lin, Tianyu Gu and Yi Shi
Agriculture 2024, 14(7), 1022; https://doi.org/10.3390/agriculture14071022 - 27 Jun 2024
Cited by 62 | Viewed by 7928
Abstract
Advancing the construction and application of new quality productive forces is an essential prerequisite for achieving high-quality agricultural development and expediting the establishment of agricultural powerhouses. This study aims to elucidate the internal mechanisms through which new quality productivity contributes to high-quality agricultural [...] Read more.
Advancing the construction and application of new quality productive forces is an essential prerequisite for achieving high-quality agricultural development and expediting the establishment of agricultural powerhouses. This study aims to elucidate the internal mechanisms through which new quality productivity contributes to high-quality agricultural development and to explore practical pathways for enhancing agricultural quality through its promotion. Utilizing panel data spanning 2012 to 2021 from 30 provinces and municipalities in mainland China, the entropy method is employed to gauge levels of new quality productivity and high-quality agricultural development. Additionally, employing research methodologies including SYS-GMM and threshold effect models, this study empirically investigates how the advancement of new quality productivity influences high-quality agricultural development. Our research reveals the following key findings: (1) The development of new quality productive forces significantly enhances high-quality agricultural development, exhibiting a heterogeneous distribution pattern favoring the “eastern region > western region > central region” and “northern region > southern region”. (2) New quality productive forces can bolster the level of high-quality agricultural development by fostering innovation, coordination, openness, and shared development within its subsystems. However, they may impede progress by inhibiting improvements in green development within the subsystems. (3) The results of the threshold effect test demonstrate that the promotion effect of the development of new quality productive forces on high-quality agricultural development escalates with the level of high-quality agricultural development. Specifically, as the level of high-quality agricultural development exceeds the first threshold value of 0.1502, the promotion effect becomes significant; crossing the second threshold value of 0.2010 further amplifies this effect. This paper’s primary marginal contribution involves empirically analyzing the potential nonlinear effects of advancing new quality productivity in enhancing the level of high-quality agricultural development. This enriches empirical research on how new quality productivity fosters the development of high-quality agriculture. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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12 pages, 1757 KB  
Article
New Frontiers in the Cultivation of Edible Fungi: The Application of Biostimulants Enhances the Nutritional Characteristics of Pleurotus eryngii (DC.) Quél
by Riccardo Fedeli, Irene Mazza, Claudia Perini, Elena Salerni and Stefano Loppi
Agriculture 2024, 14(7), 1012; https://doi.org/10.3390/agriculture14071012 - 26 Jun 2024
Cited by 13 | Viewed by 3885
Abstract
Fungi, particularly Pleurotus eryngii, emerges as a promising solution for sustainable non-animal protein production, requiring less land and growing on waste materials. In connection with population growth, sustainable solutions must be found to increase yield and product quality without resorting to the [...] Read more.
Fungi, particularly Pleurotus eryngii, emerges as a promising solution for sustainable non-animal protein production, requiring less land and growing on waste materials. In connection with population growth, sustainable solutions must be found to increase yield and product quality without resorting to the use of synthetic chemical fertilizers. Several biobased products are currently on the market; one of the most interesting is wood distillate (WD), derived from the pyrolysis process of the woody material. WD is rich in biologically active substances such as polyphenols, alcohols, acids, and esters, and its use is authorized in organic agriculture. The study investigates the use of WD in cultivating P. eryngii. We tested different concentrations of WD: 0%, 0.1%, 0.2%, 0.5%, and 1% WD on the growth of P. eryngii. Although WD did not significantly affect the yield (fresh weight), it led to a substantial increase in total soluble protein content and antioxidant compounds, such as phenols and vitamin C, and a reduction in glycogen content, especially at 0.2% WD. The results highlight the potential of biostimulants in mushroom cultivation, providing the ground for further research to improve the nutritional properties of cultivated mushrooms through wood distillate. Full article
(This article belongs to the Section Crop Production)
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14 pages, 7401 KB  
Article
Classification of Apple Color and Deformity Using Machine Vision Combined with CNN
by Dekai Qiu, Tianhao Guo, Shengqi Yu, Wei Liu, Lin Li, Zhizhong Sun, Hehuan Peng and Dong Hu
Agriculture 2024, 14(7), 978; https://doi.org/10.3390/agriculture14070978 - 23 Jun 2024
Cited by 26 | Viewed by 3033
Abstract
Accurately classifying the quality of apples is crucial for maximizing their commercial value. Deep learning techniques are being widely adopted for apple quality classification tasks, achieving impressive results. While existing research excels at classifying apple variety, size, shape, and defects, color and deformity [...] Read more.
Accurately classifying the quality of apples is crucial for maximizing their commercial value. Deep learning techniques are being widely adopted for apple quality classification tasks, achieving impressive results. While existing research excels at classifying apple variety, size, shape, and defects, color and deformity analysis remain an under-explored area. Therefore, this study investigates the feasibility of utilizing convolutional neural networks (CNN) to classify the color and deformity of apples based on machine vision technology. Firstly, a custom-assembled machine vision system was constructed for collecting apple images. Then, image processing was performed to extract the largest fruit diameter from the 45 images taken for each apple, establishing an image dataset. Three classic CNN models (AlexNet, GoogLeNet, and VGG16) were employed with parameter optimization for a three-category classification task (non-deformed slice–red apple, non-deformed stripe–red apple, and deformed apple) based on apple features. VGG16 achieved the best results with an accuracy of 92.29%. AlexNet and GoogLeNet achieved 91.66% and 88.96% accuracy, respectively. Ablation experiments were performed on the VGG16 model, which found that each convolutional block contributed to the classification task. Finally, prediction using VGG16 was conducted with 150 apples and the prediction accuracy was 90.50%, which was comparable to or better than other existing models. This study provides insights into apple classification based on color and deformity using deep learning methods. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring)
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28 pages, 8625 KB  
Review
Research Status and Development Trend of Key Technologies for Pineapple Harvesting Equipment: A Review
by Fengguang He, Qin Zhang, Ganran Deng, Guojie Li, Bin Yan, Dexuan Pan, Xiwen Luo and Jiehao Li
Agriculture 2024, 14(7), 975; https://doi.org/10.3390/agriculture14070975 - 22 Jun 2024
Cited by 12 | Viewed by 6233
Abstract
Pineapple harvesting is a key step in pineapple field production. At present, pineapple fruits are usually picked manually. With decreasing labor resources and increasing production costs, machines have been used instead of manual picking approaches in the modern pineapple industry. This paper briefly [...] Read more.
Pineapple harvesting is a key step in pineapple field production. At present, pineapple fruits are usually picked manually. With decreasing labor resources and increasing production costs, machines have been used instead of manual picking approaches in the modern pineapple industry. This paper briefly describes the basic situation of pineapple planting worldwide. Based on the degree of automation of mechanized pineapple harvesting equipment, the main structural forms, core technologies, and operation modes of semi-automatic, automatic, and intelligent pineapple harvesting equipment are summarized. The research status and existing problems of key pineapple fruit picking robots, such as fruit recognition, maturity classification, positioning, and separation of pineapple fruits, are analyzed. Considering the problems of pineapple harvesting equipment, such as difficulty entering the ground, low harvesting efficiency, low picking success rate, and fruit damage, innovative future research directions for mechanized pineapple harvesting technology are proposed, such as combining agricultural machinery and agronomical principles, integrating mechanized, automated, and intelligent technology, and developing modular designs and generalized approaches. Full article
(This article belongs to the Section Agricultural Technology)
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17 pages, 2007 KB  
Article
Can Migrant Workers Returning Home for Entrepreneurship Increase Agricultural Labor Productivity: Evidence from a Quasi-Natural Experiment in China
by Lulin Shen and Fang Wang
Agriculture 2024, 14(6), 905; https://doi.org/10.3390/agriculture14060905 - 7 Jun 2024
Cited by 10 | Viewed by 2016
Abstract
One of the effective ways to crack the “Three Rural Issues” and promote rural revitalization is to improve agricultural labor productivity (ALP). However, at this stage, improving China’s ALP is still facing many obstacles and bottlenecks. Promoting migrant workers returning home for entrepreneurship [...] Read more.
One of the effective ways to crack the “Three Rural Issues” and promote rural revitalization is to improve agricultural labor productivity (ALP). However, at this stage, improving China’s ALP is still facing many obstacles and bottlenecks. Promoting migrant workers returning home for entrepreneurship is an important breakthrough point for solving this problem. This paper regards the pilot policy of migrant workers returning home for entrepreneurship as a quasi-natural experiment and empirically investigates the influence of migrant workers returning home for entrepreneurship on ALP and explores its potential mechanism and heterogeneity using county area panel data from 2011–2019. It found the following: Firstly, the policy of migrant workers returning home for entrepreneurship significantly increases ALP. Secondly, migrant workers returning home for entrepreneurship indirectly leads to a rise in ALP through the promotion of agricultural mechanization production. Thirdly, the heterogeneity test demonstrates that migrant workers returning home for entrepreneurship purposes have a significant influence in enhancing ALP in the eastern areas, plains areas and non-agricultural strong areas. The findings of this paper not only provide an important real-world basis for the government to further support migrant workers returning home for entrepreneurship but also provide useful policy insights for the modernization and development of agriculture and rural areas. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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29 pages, 11211 KB  
Article
Efficient and Low-Loss Cleaning Method for Non-Uniform Distribution of Threshed Materials Based on Multi-Wing Curved Combination Air Screen in Computational Fluid Dynamics/Discrete Element Method Simulations
by Longhai Wang, Xiaoyu Chai, Juan Huang, Jinpeng Hu and Zhihong Cui
Agriculture 2024, 14(6), 895; https://doi.org/10.3390/agriculture14060895 - 5 Jun 2024
Cited by 20 | Viewed by 1693
Abstract
During the operation of the longitudinal axis flow threshing device of a combine harvester, the threshed materials form accumulations and blockages on both sides of the screen surface, severely affecting the harvesting process. To evenly distribute the materials on the screen and solve [...] Read more.
During the operation of the longitudinal axis flow threshing device of a combine harvester, the threshed materials form accumulations and blockages on both sides of the screen surface, severely affecting the harvesting process. To evenly distribute the materials on the screen and solve the blockage issue, a multi-wing curved combination centrifugal fan is designed to match the mass distribution of the threshed materials. The movement mechanism of rice threshed materials in the cleaning shoe of a longitudinal axis flow combine harvester is investigated using the coupled CFD-DEM simulation method. The cleaning efficiency and performance of the traditional straight-blade fan screen device and the newly designed cleaning device are compared and analyzed, and field tests are conducted. The results show that the trajectory of the threshed materials cleaned by the device equipped with the multi-wing curved combination centrifugal fan is consistent with the mass distribution of the materials separated by the longitudinal axis flow threshing device. The absolute value of the centroid velocity of the material group in the X/Y direction is greater than that of the traditional fan, indicating that the movement speed of the particle group in the optimized fan is greater than that of the traditional fan. Therefore, in the actual cleaning process, the optimized fan’s air flow distribution more effectively accelerates the movement speed of the threshed materials, increasing the amount of materials cleaned per unit time, thereby improving the cleaning efficiency. Field comparative tests show that the designed cleaning device reduced the cleaning loss rate by up to 25.00% and the impurity content rate by 32.20%, achieving efficient and low-damage cleaning of the combine harvester. The study demonstrates the effectiveness of the proposed method for evenly distributing the materials and provides important reference for the study of other piled particle distribution systems. Full article
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12 pages, 264 KB  
Article
Effects of Different Essential Oil Blends and Fumaric Acid on In Vitro Fermentation, Greenhouse Gases, Nutrient Degradability, and Total and Molar Proportions of Volatile Fatty Acid Production in a Total Mixed Ration for Dairy Cattle
by Kelechi A. Ike, Oludotun O. Adelusi, Joel O. Alabi, Lydia K. Olagunju, Michael Wuaku, Chika C. Anotaenwere, Deborah O. Okedoyin, DeAndrea Gray, Peter A. Dele, Kiran Subedi, Ahmed E. Kholif and Uchenna Y. Anele
Agriculture 2024, 14(6), 876; https://doi.org/10.3390/agriculture14060876 - 31 May 2024
Cited by 12 | Viewed by 1754
Abstract
The present study evaluated the inclusion of fumaric acid and essential oil blends (EOBs) containing anise, cedarwood, clove, cumin, eucalyptus, garlic, ginger, lavender, lemongrass, nutmeg, oregano, and peppermint at different proportions on in vitro dry matter (DM) disappearance (DMD), fiber fraction disappearance, the [...] Read more.
The present study evaluated the inclusion of fumaric acid and essential oil blends (EOBs) containing anise, cedarwood, clove, cumin, eucalyptus, garlic, ginger, lavender, lemongrass, nutmeg, oregano, and peppermint at different proportions on in vitro dry matter (DM) disappearance (DMD), fiber fraction disappearance, the efficiency of microbial production, and the total volatile fatty acids (VFAs). Ten treatments without (control treatment) or with different EOB/fumaric combinations were used in the study with eight replicates. The EOB inclusion level was 200 μL/g of feed (total mixed ration, (TMR)) while fumaric acid was administered at 3% of the TMR (DM basis). The highest DMD, in vitro true degradable DM, partitioning factor (PF24), and in vitro apparent degradable DM were recorded for the fumaric only treatment and the control. Neutral detergent fiber disappearance was reduced with the inclusion of EOB/fumaric combinations. The production of microbial mass and undegraded DM were higher (p < 0.001) for all EOBs and EOB and fumaric treatments. The inclusion of EOB and fumaric combinations reduced (p < 0.001) the total gas production, methane, and ammonia, with a higher PF24 value noted for EOB3 treatment. The inclusion of individual EOB1 containing garlic, lemongrass, cumin, lavender, and nutmeg in a ratio of 4:2:2:1:1 or combined with fumaric acid yielded the highest propionate concentration across all treatments. We concluded that EOBs decreased methane production and nutrient degradability with better results with the individual EOB1 or EOB1/fumaric combination, which showed a potential enhancement in energy production. Full article
(This article belongs to the Section Farm Animal Production)
14 pages, 5008 KB  
Article
Assessing Contents of Sugars, Vitamins, and Nutrients in Baby Leaf Lettuce from Hyperspectral Data with Machine Learning Models
by Sulaymon Eshkabilov and Ivan Simko
Agriculture 2024, 14(6), 834; https://doi.org/10.3390/agriculture14060834 - 27 May 2024
Cited by 11 | Viewed by 3046
Abstract
Lettuce (Lactuca sativa) is a leafy vegetable that provides a valuable source of phytonutrients for a healthy human diet. The assessment of plant growth and composition is vital for determining crop yield and overall quality; however, classical laboratory analyses are slow [...] Read more.
Lettuce (Lactuca sativa) is a leafy vegetable that provides a valuable source of phytonutrients for a healthy human diet. The assessment of plant growth and composition is vital for determining crop yield and overall quality; however, classical laboratory analyses are slow and costly. Therefore, new, less expensive, more rapid, and non-destructive approaches are being developed, including those based on (hyper)spectral reflectance. Additionally, it is important to determine how plant phenotypes respond to fertilizer treatments and whether these differences in response can be detected from analyses of hyperspectral image data. In the current study, we demonstrate the suitability of hyperspectral imaging in combination with machine learning models to estimate the content of chlorophyll (SPAD), anthocyanins (ACI), glucose, fructose, sucrose, vitamin C, β-carotene, nitrogen (N), phosphorus (P), potassium (K), dry matter content, and plant fresh weight. Five classification and regression machine learning models were implemented, showing high accuracy in classifying the lettuces based on the applied fertilizers treatments and estimating nutrient concentrations. To reduce the input (predictor data, i.e., hyperspectral data) dimension, 13 principal components were identified and applied in the models. The implemented artificial neural network models of the machine learning algorithm demonstrated high accuracy (r = 0.85 to 0.99) in estimating fresh leaf weight, and the contents of chlorophyll, anthocyanins, N, P, K, and β-carotene. The four applied classification models of machine learning demonstrated 100% accuracy in classifying the studied baby leaf lettuces by phenotype when specific fertilizer treatments were applied. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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14 pages, 5103 KB  
Article
Calibration and Testing of Discrete Elemental Simulation Parameters for Pod Pepper Seeds
by Xingye Chen, Jing Bai, Xinzhong Wang, Weiquan Fang, Tianyu Hong, Nan Zang, Liangliang Fang and Gaoliang Wang
Agriculture 2024, 14(6), 831; https://doi.org/10.3390/agriculture14060831 - 26 May 2024
Cited by 15 | Viewed by 3420
Abstract
The discrete elemental parameters of pod pepper seeds were calibrated for future numerical optimization of the pod pepper seed cleaning device. The study concentrates on calibrating the intrinsic and contact parameters of pod pepper seeds utilizing the discrete element method. Compression tests were [...] Read more.
The discrete elemental parameters of pod pepper seeds were calibrated for future numerical optimization of the pod pepper seed cleaning device. The study concentrates on calibrating the intrinsic and contact parameters of pod pepper seeds utilizing the discrete element method. Compression tests were performed to ascertain intrinsic parameters such as Poisson’s ratio and the seeds’ elastic modulus. The static friction coefficient and collision restitution coefficient between the seeds and steel plates were identified through incline and free-fall tests. Plackett–Burman, steepest ascent, and Box–Behnken experiments were performed to establish a second-order regression model correlating significant parameters with the angle of repose. The optimal parameter combination, based on the measured angle of repose (32.45°), yielded static friction coefficients between seeds, rolling friction coefficients between seeds, and static friction coefficients between seeds and steel plates of 0.608, 0.018, and 0.787, respectively. The two-sample t-test of the physical and simulated repose angles yielded p > 0.05, and the relative error of the physical and simulated repose angles was 0.68%, which confirmed the reliability of the calibration parameters. The findings indicate that the calibration method for pod pepper seeds effectively informs the calibration of parameters for other irregular crops. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture—2nd Edition)
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22 pages, 8528 KB  
Article
Vibration Response of Metal Plate and Shell Structure under Multi-Source Excitation with Welding and Bolt Connection
by Zhexuan Ding, Zhong Tang, Ben Zhang and Zhao Ding
Agriculture 2024, 14(6), 816; https://doi.org/10.3390/agriculture14060816 - 24 May 2024
Cited by 19 | Viewed by 1710
Abstract
There are many excitation sources and complex vibration environments in combine harvesters. The coupling and superposition of different vibration signals on the plate and shell seriously affect the working parts of the body. This also reduces the reliability of the whole machine. At [...] Read more.
There are many excitation sources and complex vibration environments in combine harvesters. The coupling and superposition of different vibration signals on the plate and shell seriously affect the working parts of the body. This also reduces the reliability of the whole machine. At present, domestic and foreign research on existing harvesters mainly focuses on harvesting performance, with less research on vibration characteristics. Therefore, in this paper, the vibration response of the metal plate–shell under the two connection modes of bolt connection and welding is studied, in order to optimize the design and structure of the plate–shell structure of the combine harvester and improve the overall performance. First, the welded and bolted plates are numerically modeled using Hypermesh pre-processing functions. Then, the boundary conditions are simulated by continuous variable stiffness elastic constraint experiments. Finally, the intrinsic vibration dynamic model of the four-sided simply supported plate and four-sided solidly supported plate is established using the modal superposition method. By analyzing the modal frequencies and vibration patterns, the following results are obtained. The connection method between the plate and the frame has a significant impact on the inherent vibration characteristics of the plate. The bolt connection will make the plate’s intrinsic vibration frequency higher than that of the welding method, but the effect on the plate’s intrinsic vibration pattern is more minor. At the same time, in order to verify the accuracy of the model, the actual modal vibration patterns and frequencies of the same proportion of plates in the modal test are compared with the results of modal vibration patterns and frequencies obtained by Ansys. The errors of the two dynamic model analytical methods are within 1% and 3%, respectively. This result verifies the accuracy of the dynamic model of the metal plate and shell structure under different connection methods. Full article
(This article belongs to the Section Agricultural Technology)
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21 pages, 5780 KB  
Article
Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery
by Meng Wang, Changhe Niu, Zifan Wang, Yongxin Jiang, Jianming Jian and Xiuying Tang
Agriculture 2024, 14(6), 823; https://doi.org/10.3390/agriculture14060823 - 24 May 2024
Cited by 14 | Viewed by 2210
Abstract
To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models [...] Read more.
To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations. Full article
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29 pages, 12586 KB  
Article
Deep Learning for Multi-Source Data-Driven Crop Yield Prediction in Northeast China
by Jian Lu, Jian Li, Hongkun Fu, Xuhui Tang, Zhao Liu, Hui Chen, Yue Sun and Xiangyu Ning
Agriculture 2024, 14(6), 794; https://doi.org/10.3390/agriculture14060794 - 22 May 2024
Cited by 29 | Viewed by 6470
Abstract
The accurate prediction of crop yields is crucial for enhancing agricultural efficiency and ensuring food security. This study assesses the performance of the CNN-LSTM-Attention model in predicting the yields of maize, rice, and soybeans in Northeast China and compares its effectiveness with traditional [...] Read more.
The accurate prediction of crop yields is crucial for enhancing agricultural efficiency and ensuring food security. This study assesses the performance of the CNN-LSTM-Attention model in predicting the yields of maize, rice, and soybeans in Northeast China and compares its effectiveness with traditional models such as RF, XGBoost, and CNN. Utilizing multi-source data from 2014 to 2020, which include vegetation indices, environmental variables, and photosynthetically active parameters, our research examines the model’s capacity to capture essential spatial and temporal variations. The CNN-LSTM-Attention model integrates Convolutional Neural Networks, Long Short-Term Memory, and an attention mechanism to effectively process complex datasets and manage non-linear relationships within agricultural data. Notably, the study explores the potential of using kNDVI for predicting yields of multiple crops, highlighting its effectiveness. Our findings demonstrate that advanced deep-learning models significantly enhance yield prediction accuracy over traditional methods. We advocate for the incorporation of sophisticated deep-learning technologies in agricultural practices, which can substantially improve yield prediction accuracy and food production strategies. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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19 pages, 1439 KB  
Article
The Impact of Agricultural Socialized Service on Grain Production: Evidence from Rural China
by Ruisheng Li, Jiaoyan Chen and Dingde Xu
Agriculture 2024, 14(5), 785; https://doi.org/10.3390/agriculture14050785 - 20 May 2024
Cited by 17 | Viewed by 2863
Abstract
Although China’s grain production has reached nineteen consecutive harvests, the uncertainty of the current domestic and international environment has put more pressure on further increasing grain production in the future. For the past few years, agricultural socialization services have been crucial in boosting [...] Read more.
Although China’s grain production has reached nineteen consecutive harvests, the uncertainty of the current domestic and international environment has put more pressure on further increasing grain production in the future. For the past few years, agricultural socialization services have been crucial in boosting grain production and farmers’ revenue by addressing the issue of land cultivation and farming methods. In this regard, the question of whether and how agricultural socialized services may resolve the present grain production conundrum is extremely practical. Therefore, the study employs the China Rural Revitalization Survey data of 3709 households. Based on the 2SLS model, stepwise regression method, and moderated effects model, it creatively takes into account a variety of agricultural production segments, investigates the mechanism of services on grain production from the standpoint of improved production efficiency and plot concentration, and further examines the effects of aging populations and regional variations in grain production areas. The study found the following: (1) The average proportion of grain production area of farmers in the sample is 49%, and 42% of farmers have purchased agricultural socialization services. (2) Agricultural socialization services can significantly promote farmers’ grain cultivation behavior by facilitating connected transfers in and inhibiting connected transfers out to take advantage of plot concentration, and boosting the use of agricultural machines to enhance output efficiency. (3) The aging of the agricultural population will, to a certain extent, strengthen the promoting effect of agricultural socialization services on grain cultivation. Agricultural socialization services affect grain cultivation more in main grain-producing areas. Therefore, emphasizing the role of agricultural socialization services in accelerating the shift to moderate-scale operations, decreasing the non-grain component of the planting structure, and promoting the implementation of policies tailored to actual production needs are important steps to safeguard the production capacity of grain in different regions. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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21 pages, 6555 KB  
Article
Worldwide Examination of Magnetic Responses to Heavy Metal Pollution in Agricultural Soils
by Xuanxuan Zhao, Jiaxing Zhang, Ruijun Ma, Hui Luo, Tao Wan, Dongyang Yu and Yuanqian Hong
Agriculture 2024, 14(5), 702; https://doi.org/10.3390/agriculture14050702 - 29 Apr 2024
Cited by 10 | Viewed by 3049
Abstract
Over the last decade, a large number of studies have been conducted on heavy metals and magnetic susceptibility (χlf) measurement in soils. Yet, a global understanding of soil contamination and magnetic responses remains elusive due to the limited scope [...] Read more.
Over the last decade, a large number of studies have been conducted on heavy metals and magnetic susceptibility (χlf) measurement in soils. Yet, a global understanding of soil contamination and magnetic responses remains elusive due to the limited scope or sampling sites of these studies. Hence, we attempted to explore a pollution proxy on a global scale. Through a meta-analysis of data from 102 published studies, our research aimed to provide a worldwide overview of heavy metal pollution and magnetic responses in agriculture soils. We mapped the geographic distribution of nine heavy metals (Cr, Cu, Zn, Pb, Ni, As, Cd, Mn, and Fe) in agricultural soils and explored their pollution sources and contributions. Since 2011, The accumulation of heavy metals has escalated, with industrial activities (31.5%) being the largest contributor, followed by agricultural inputs (27.1%), atmospheric deposition (22.66%), and natural sources (18.74%). The study reports χlf ranging from 6.45 × 10−8 m3/kg to 319.23 × 10−8 m3/kg and χfd from 0.59% and 12.85%, with the majority of the samples being below 6%, indicating heavy metal influence mainly from human activities. Pearson’s correlation and redundancy analysis show significant positive correlations of Pb, Zn, and Cu with χlf (r = 0.51–0.53) and Mn and Fe with χfd (r = 0.50–0.53), while Pb, Zn, Cu, and As metals were shown to be key factors of variation in magnetic response. The average heavy metal pollution load index of 2.03 suggests moderate global agricultural soil pollution, with higher heavy metal contamination in areas of high χlf. Regression analysis confirms soil is considered to be non-polluted below χlf of 26×108 m3/kg and polluted above this threshold, with all contamination factors of metals showing a linear correlation with χlf (R = 0.72), indicating that a significant relationship between χlf and the geochemical properties of soils continues to exist on a global scale. This study provides new insights for large-scale agricultural soil quality assessment and magnetic response. Full article
(This article belongs to the Section Agricultural Soils)
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