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Digital Innovations in Agriculture—Series II

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A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 16792

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Prof. Dr. Gniewko Niedbała

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Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; forecasting; crop production
Special Issues, Collections and Topics in MDPI journals

Dr. Sebastian Kujawa

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Dr. Magdalena Piekutowska

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Department of Geoecology and Geoinformation, Institute of Biology and Earth Sciences, Pomeranian University in Słupsk, 27 Partyzantów St., 76-200 Słupsk, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; potato production; plant breeding; soil science; plant growth analysis
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Dr. Tomasz Wojciechowski

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Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: agricutural engineering; soil tillage; precison agriculture; soil monitoring; proximal sensing; spectroscopy; digital farming; smart farming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world population is increasing significantly and is expected to reach almost 10 billion in the year 2050. At the same time, observed climate change is accelerating and affecting agricultural production strongly. These aspects, as well as the latest socio-economic limitations caused by the standoff following the COVID-19 pandemic, are the ongoing economic crisis bringing new challenges to modern agriculture and the need to have high production efficiency combined with a high quality of obtained products in accordance with the principles of sustainable production. These requirements are linked to all branches of agriculture: crop production, livestock production and other links supporting the production of healthy food.

To meet these challenges, advanced digital innovation techniques are more and more frequently being used, including those based on machine learning, artificial neural networks, Internet of Things (IoT), Big Data and Digital Twins. They are widely applied in solving various optimization tasks in the agri-food production processes in the context of the increasing use of precision and digital farming technologies on the path from Agriculture 3.0 to 5.0.

The featured SI is a continuation of the very substantive and popular SI of the Agriculture journal. The current issue focuses even more on the contribution of modern technology to the development and ongoing support of sustainable, regenerative and balanced agriculture. We invite authors to submit all types of manuscripts, including original research, research concepts, communications and reviews related to digital innovation, widely defined, in the agri-food sector.

Prof. Dr. Gniewko Niedbała
Dr. Sebastian Kujawa
Dr. Magdalena Piekutowska
Dr. Tomasz Wojciechowski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

digital farming
precision agriculture
machine learning
artificial neural networks
Internet of Things (IoT)
Big Data
Digital Twins, image processing and analysis
proximal and remote sensing
data analysis and decision support
agricultural information systems (FMIS, ERP)
traceability
other digital innovations in agriculture

Published Papers (12 papers)

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Research

21 pages, 16074 KiB

Open AccessArticle

Wheat Teacher: A One-Stage Anchor-Based Semi-Supervised Wheat Head Detector Utilizing Pseudo-Labeling and Consistency Regularization Methods

by Rui Zhang, Mingwei Yao, Zijie Qiu, Lizhuo Zhang, Wei Li and Yue Shen

Agriculture 2024, 14(2), 327; https://doi.org/10.3390/agriculture14020327 - 19 Feb 2024

Viewed by 866

Abstract

Wheat breeding heavily relies on the observation of various traits during the wheat growth process. Among all traits, wheat head density stands out as a particularly crucial characteristic. Despite the realization of high-throughput phenotypic data collection for wheat, the development of efficient and robust models for extracting traits from raw data remains a significant challenge. Numerous fully supervised target detection algorithms have been employed to address the wheat head detection problem. However, constrained by the exorbitant cost of dataset creation, especially the manual annotation cost, fully supervised target detection algorithms struggle to unleash their full potential. Semi-supervised training methods can leverage unlabeled data to enhance model performance, addressing the issue of insufficient labeled data. This paper introduces a one-stage anchor-based semi-supervised wheat head detector, named “Wheat Teacher”, which combines two semi-supervised methods, pseudo-labeling, and consistency regularization. Furthermore, two novel dynamic threshold components, Pseudo-label Dynamic Allocator and Loss Dynamic Threshold, are designed specifically for wheat head detection scenarios to allocate pseudo-labels and filter losses. We conducted detailed experiments on the largest wheat head public dataset, GWHD2021. Compared with various types of detectors, Wheat Teacher achieved a mAP0.5 of 92.8% with only 20% labeled data. This result surpassed the test outcomes of two fully supervised object detection models trained with 100% labeled data, and the difference with the other two fully supervised models trained with 100% labeled data was within 1%. Moreover, Wheat Teacher exhibits improvements of 2.1%, 3.6%, 5.1%, 37.7%, and 25.8% in mAP0.5 under different labeled data usage ratios of 20%, 10%, 5%, 2%, and 1%, respectively, validating the effectiveness of our semi-supervised approach. These experiments demonstrate the significant potential of Wheat Teacher in wheat head detection. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture—Series II)

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17 pages, 9026 KiB

Open AccessArticle

Unmanned Aerial Vehicle-Based Techniques for Monitoring and Prevention of Invasive Apple Snails (Pomacea canaliculata) in Rice Paddy Fields

by Senlin Guan, Kimiyasu Takahashi, Shunichiro Watanabe and Katsunori Tanaka

Agriculture 2024, 14(2), 299; https://doi.org/10.3390/agriculture14020299 - 13 Feb 2024

Viewed by 937

Abstract

The destructive impact of invasive apple snail (Pomacea canaliculata) on young rice seedlings has garnered global attention, particularly in warm regions where rice production occurs. The preventative application of insecticide, particularly in areas with young rice seedlings and water depths exceeding 4 cm, has proven effective in mitigating this damage. In line with this recommendation, our study investigates the efficacy of site-specific drone-based insecticide applications to mitigate snail damage in rice paddies. These site-specific drone applications were strategically executed as directed by a highly accurate prescription map indicating the required insecticide quantity at specific locations. The prescription map was automatically generated through an advanced data processing program that used the aerial images acquired by a Real-Time Kinematic (RTK)-Unmanned Aerial Vehicle (UAV) as the input. Criteria were established to select the treatment locations; a value of below 4 cm from the top 95% percentile in the histogram of ground elevation data was used as a threshold to identify areas with a high-density of snail damage. The results demonstrated reductions in both the rates of rice damage and chemical usage following site-specific drone applications compared with the control fields. The findings in this study contribute to the advancement of effective site-specific pest control in precision agriculture. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture—Series II)

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14 pages, 3366 KiB

Open AccessArticle

Apple Varieties Classification Using Deep Features and Machine Learning

by Alper Taner, Mahtem Teweldemedhin Mengstu, Kemal Çağatay Selvi, Hüseyin Duran, İbrahim Gür and Nicoleta Ungureanu

Agriculture 2024, 14(2), 252; https://doi.org/10.3390/agriculture14020252 - 03 Feb 2024

Viewed by 1066

Abstract

Having the advantages of speed, suitability and high accuracy, computer vision has been effectively utilized as a non-destructive approach to automatically recognize and classify fruits and vegetables, to meet the increased demand for food quality-sensing devices. Primarily, this study focused on classifying apple varieties using machine learning techniques. Firstly, to discern how different convolutional neural network (CNN) architectures handle different apple varieties, transfer learning approaches, using popular seven CNN architectures (VGG16, VGG19, InceptionV3, MobileNet, Xception, ResNet150V2 and DenseNet201), were adopted, taking advantage of the pre-trained models, and it was found that DenseNet201 had the highest (97.48%) classification accuracy. Secondly, using the DenseNet201, deep features were extracted and traditional Machine Learning (ML) models: support vector machine (SVM), multi-layer perceptron (MLP), random forest classifier (RFC) and K-nearest neighbor (KNN) were trained. It was observed that the classification accuracies were significantly improved and the best classification performance of 98.28% was obtained using SVM algorithms. Finally, the effect of dimensionality reduction in classification performance, deep features, principal component analysis (PCA) and ML models was investigated. MLP achieved an accuracy of 99.77%, outperforming SVM (99.08%), RFC (99.54%) and KNN (91.63%). Based on the performance measurement values obtained, our study achieved success in classifying apple varieties. Further investigation is needed to broaden the scope and usability of this technique, for an increased number of varieties, by increasing the size of the training data and the number of apple varieties. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture—Series II)

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18 pages, 2961 KiB

Open AccessArticle

Insights into Drought Tolerance of Tetraploid Wheat Genotypes in the Germination Stage Using Machine Learning Algorithms

by Berk Benlioğlu, Fatih Demirel, Aras Türkoğlu, Kamil Haliloğlu, Hamdi Özaktan, Sebastian Kujawa, Magdalena Piekutowska, Tomasz Wojciechowski and Gniewko Niedbała

Agriculture 2024, 14(2), 206; https://doi.org/10.3390/agriculture14020206 - 27 Jan 2024

Viewed by 963

Abstract

Throughout germination, which represents the initial and crucial phase of the wheat life cycle, the plant is notably susceptible to the adverse effects of drought. The identification and selection of genotypes exhibiting heightened drought tolerance stand as pivotal strategies aimed at mitigating these effects. For the stated objective, this study sought to evaluate the responses of distinct wheat genotypes to diverse levels of drought stress encountered during the germination stage. The induction of drought stress was achieved using polyethylene glycol at varying concentrations, and the assessment was conducted through the application of multivariate analysis and machine learning algorithms. Statistical significance (p < 0.01) was observed in the differences among genotypes, stress levels, and their interaction. The ranking of genotypes based on tolerance indicators was evident through a principal component analysis and biplot graphs utilizing germination traits and stress tolerance indices. The drought responses of wheat genotypes were modeled using germination data. Predictions were then generated using four distinct machine learning techniques. An evaluation based on R-square, mean square error, and mean absolute deviation metrics indicated the superior performance of the elastic-net model in estimating germination speed, germination power, and water absorption capacity. Additionally, in assessing the criterion metrics, it was determined that the Gaussian processes classifier exhibited a better performance in estimating root length, while the extreme gradient boosting model demonstrated superior performance in estimating shoot length, fresh weight, and dry weight. The study’s findings underscore that drought tolerance, susceptibility levels, and parameter estimation for durum wheat and similar plants can be reliably and efficiently determined through the applied methods and analyses, offering a fast and cost-effective approach. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture—Series II)

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18 pages, 9954 KiB

Open AccessArticle

A Lightweight Detection Method for Blueberry Fruit Maturity Based on an Improved YOLOv5 Algorithm

by Feng Xiao, Haibin Wang, Yueqin Xu and Zhen Shi

Agriculture 2024, 14(1), 36; https://doi.org/10.3390/agriculture14010036 - 24 Dec 2023

Cited by 1 | Viewed by 1225

Abstract

In order to achieve accurate, fast, and robust recognition of blueberry fruit maturity stages for edge devices such as orchard inspection robots, this research proposes a lightweight detection method based on an improved YOLOv5 algorithm. In the improved YOLOv5 algorithm, the ShuffleNet module is used to achieve lightweight deep-convolutional neural networks. The Convolutional Block Attention Module (CBAM) is also used to enhance the feature fusion capability of lightweight deep-convolutional neural networks. The effectiveness of this method is evaluated using the blueberry fruit dataset. The experimental results demonstrate that this method can effectively detect blueberry fruits and recognize their maturity stages in orchard environments. The average recall (R) of the detection is 92.0%. The mean average precision (mAP) of the detection at a threshold of 0.5 is 91.5%. The average speed of the detection is 67.1 frames per second (fps). Compared to other detection algorithms, such as YOLOv5, SSD, and Faster R-CNN, this method has a smaller model size, smaller network parameters, lower memory usage, lower computation usage, and faster detection speed while maintaining high detection performance. It is more suitable for migration and deployment on edge devices. This research can serve as a reference for the development of fruit detection systems for intelligent orchard devices. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture—Series II)

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25 pages, 12299 KiB

Open AccessArticle

Prediction of Potato (Solanum tuberosum L.) Yield Based on Machine Learning Methods

by Jarosław Kurek, Gniewko Niedbała, Tomasz Wojciechowski, Bartosz Świderski, Izabella Antoniuk, Magdalena Piekutowska, Michał Kruk and Krzysztof Bobran

Agriculture 2023, 13(12), 2259; https://doi.org/10.3390/agriculture13122259 - 11 Dec 2023

Viewed by 1227

Abstract

This research delves into the application of machine learning methods for predicting the yield of potato varieties used for French fries in Poland. By integrating a comprehensive dataset comprising agronomical, climatic, soil, and satellite-based vegetation data from 36 commercial potato fields over five growing seasons (2018–2022), we developed three distinct models: non-satellite, satellite, and hybrid. The non-satellite model, relying on 85 features, excludes vegetation indices, whereas the satellite model includes these indices within its 128 features. The hybrid model, combining all available features, encompasses a total of 165 features, presenting the most-comprehensive approach. Our findings revealed that the hybrid model, particularly when enhanced with SVM outlier detection, exhibited superior performance with the lowest Mean Absolute Percentage Error (MAPE) of 5.85%, underscoring the effectiveness of integrating diverse data sources into agricultural yield prediction. In contrast, the non-satellite and satellite models displayed higher MAPE values, indicating less accuracy compared to the hybrid model. Advanced data-processing techniques such as PCA and outlier detection methods (LOF and One-Class SVM) played a pivotal role in model performance, optimising feature selection and dataset refinement. The study concluded that machine learning methods, particularly when leveraging a multifaceted approach involving a wide array of data sources and advanced processing techniques, can significantly enhance the accuracy of agricultural yield predictions. These insights pave the way for more-efficient and -informed agricultural practices, emphasising the potential of machine learning in revolutionising yield prediction and crop management. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture—Series II)

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25 pages, 7971 KiB

Open AccessArticle

SpikoPoniC: A Low-Cost Spiking Neuromorphic Computer for Smart Aquaponics

by Ali Siddique, Jingqi Sun, Kung Jui Hou, Mang I. Vai, Sio Hang Pun and Muhammad Azhar Iqbal

Agriculture 2023, 13(11), 2057; https://doi.org/10.3390/agriculture13112057 - 27 Oct 2023

Cited by 1 | Viewed by 1328

Abstract

Aquaponics is an emerging area of agricultural sciences that combines aquaculture and hydroponics in a symbiotic way to enhance crop production. A stable smart aquaponic system requires estimating the fish size in real time. Though deep learning has shown promise in the context of smart aquaponics, most smart systems are extremely slow and costly and cannot be deployed on a large scale. Therefore, we design and present a novel neuromorphic computer that uses spiking neural networks (SNNs) for estimating not only the length but also the weight of the fish. To train the SNN, we present a novel hybrid scheme in which some of the neural layers are trained using direct SNN backpropagation, while others are trained using standard backpropagation. By doing this, a blend of high hardware efficiency and accuracy can be achieved. The proposed computer SpikoPoniC can classify more than 84 million fish samples in a second, achieving a speedup of at least 3369× over traditional general-purpose computers. The SpikoPoniC consumes less than 1100 slice registers on Virtex 6 and is much cheaper than most SNN-based hardware systems. To the best of our knowledge, this is the first SNN-based neuromorphic system that performs smart real-time aquaponic monitoring. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture—Series II)

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15 pages, 3481 KiB

Open AccessArticle

Modeling of Path Loss for Radio Wave Propagation in Wireless Sensor Networks in Cassava Crops Using Machine Learning

by Alexis Barrios-Ulloa, Alejandro Cama-Pinto, Emiro De-la-Hoz-Franco, Raúl Ramírez-Velarde and Dora Cama-Pinto

Agriculture 2023, 13(11), 2046; https://doi.org/10.3390/agriculture13112046 - 25 Oct 2023

Viewed by 1253

Abstract

Modeling radio signal propagation remains one of the most critical tasks in the planning of wireless communication systems, including wireless sensor networks (WSN). Despite the existence of a considerable number of propagation models, the studies aimed at characterizing the attenuation in the wireless channel are still numerous and relevant. These studies are used in the design and planning of wireless networks deployed in various environments, including those with abundant vegetation. This paper analyzes the performance of three vegetation propagation models, ITU-R, FITU-R, and COST-235, and compares them with path loss measurements conducted in a cassava field in Sincelejo, Colombia. Additionally, we applied four machine learning techniques: linear regression (LR), k-nearest neighbors (K-NN), support vector machine (SVM), and random forest (RF), aiming to enhance prediction accuracy levels. The results show that vegetation models based on traditional approaches are not able to adequately characterize attenuation, while models obtained by machine learning using RF, K-NN, and SVM can predict path loss in cassava with RMSE and MAE values below 5 dB. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture—Series II)

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12 pages, 4040 KiB

Open AccessArticle

The Development of a Weight Prediction System for Pigs Using Raspberry Pi

by Myung Hwan Na, Wan Hyun Cho, Sang Kyoon Kim and In Seop Na

Agriculture 2023, 13(10), 2027; https://doi.org/10.3390/agriculture13102027 - 19 Oct 2023

Cited by 1 | Viewed by 1274

Abstract

Generally, measuring the weight of livestock is difficult; it is time consuming, inconvenient, and stressful for both livestock farms and livestock to be measured. Therefore, these problems must be resolved to boost convenience and reduce economic costs. In this study, we develop a portable prediction system that can automatically predict the weights of pigs, which are commonly used for consumption among livestock, using Raspberry Pi. The proposed system consists of three parts: pig image data capture, pig weight prediction, and the visualization of the predicted results. First, the pig image data are captured using a three-dimensional depth camera. Second, the pig weight is predicted by segmenting the livestock from the input image using the Raspberry Pi module and extracting features from the segmented image. Third, a 10.1-inch monitor is used to visually show the predicted results. To evaluate the performance of the constructed prediction device, the device is learned using the 3D sensor dataset collected from specific breeding farms, and the efficiency of the system is evaluated using separate verification data. The evaluation results show that the proposed device achieves approximately 10.702 for RMSE, 8.348 for MAPE, and 0.146 for MASE predictive power. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture—Series II)

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21 pages, 3467 KiB

Open AccessArticle

Detection of Bagworm Infestation Area in Oil Palm Plantation Based on UAV Remote Sensing Using Machine Learning Approach

by Siti Nurul Afiah Mohd Johari, Siti Khairunniza-Bejo, Abdul Rashid Mohamed Shariff, Nur Azuan Husin, Mohamed Mazmira Mohd Masri and Noorhazwani Kamarudin

Agriculture 2023, 13(10), 1886; https://doi.org/10.3390/agriculture13101886 - 27 Sep 2023

Cited by 2 | Viewed by 1727

Abstract

Due to its rapid reproduction rate and brief life cycle, the most well-known oil palm pest, Metisa plana (Lepidoptera: Psychidae), also known as the bagworm, can spread to epidemic proportions. The outbreak can significantly reduce oil palm yield by resulting in 40% crop losses and 10% to 13% leaf defoliation. A manual census was conducted to count the number of pests and determine the category of infestation; however, when covering a large area, it typically takes more time and labour. Therefore, this study used unmanned aerial vehicles (UAVs) as a quick way to detect the severity levels of infestation in oil palm plantations, including healthy (zero), low, mild, and severe infestation using DJI Inspire 2 with Micasense Altum-PT multispectral camera at an altitude of 70 m above ground. Three combinations were created from the most significant vegetation indices: NDVI and NDRE, NDVI and GNDVI, and NDRE and GNDVI. According to the results, the best combination in classifying healthy and low levels was found to be NDVI and GNDVI, with 100% F1 score. In addition, the combination of NDVI and NDRE was found to be the best combination in classifying mild and severe level. The most important vegetation index that could detect every level of infestation was NDVI. Furthermore, Weighted KNN become the best model that constantly gave the best performance in classifying all the infestation levels (F1 score > 99.70%) in all combinations. The suggested technique is crucial for the early phase of severity-level detection and saves time on the preparation and operation of the control measure. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture—Series II)

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16 pages, 39127 KiB

Open AccessArticle

MushR: A Smart, Automated, and Scalable Indoor Harvesting System for Gourmet Mushrooms

by Anant Sujatanagarjuna, Shohreh Kia, Dominique Fabio Briechle and Benjamin Leiding

Agriculture 2023, 13(8), 1533; https://doi.org/10.3390/agriculture13081533 - 01 Aug 2023

Cited by 1 | Viewed by 1799

Abstract

Gourmet mushrooms are foraged from the wild or grown indoors in controlled environments. Indoor mushroom farms with controlled growth environments allow for all-year-round growing. However, it remains a labor-intensive process. We propose MushR as a modular and scalable gourmet mushroom growing and harvesting system that goes beyond the state of the art, which merely monitors and controls the growing environment, by introducing an image recognition system that determines when and which mushrooms are ready to be harvested in conjunction with a proof of concept of an automated mushroom harvesting mechanism for harvesting the mushrooms without human interaction. The image recognition setup monitors the growing status of the mushrooms and guides the harvesting process. We present a Mask R-CNN model for the detection of oyster mushroom maturity with a 91.7% training accuracy and a semiautomated harvesting system, integrating a Raspberry Pi for control, an electrical switch, an air compressor, and a pneumatic cylinder with a cutting knife to facilitate timely mushroom harvesting. The modularity and scalability of the system allow for industry-level usage and can be scaled according to the required mushroom-growing systems within the facility. The AI model, its underlying dataset, a digital twin for mushroom production, the setup of our growth and control chambers, and additional information are all made available under an open-source license. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture—Series II)

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21 pages, 3531 KiB

Open AccessArticle

An Adaptive Nutcracker Optimization Approach for Distribution of Fresh Agricultural Products with Dynamic Demands

by Daqing Wu, Rong Yan, Hongtao Jin and Fengmao Cai

Agriculture 2023, 13(7), 1430; https://doi.org/10.3390/agriculture13071430 - 19 Jul 2023

Cited by 1 | Viewed by 1326

Abstract

In the operational, strategic and tactical decision-making problems of the agri-food supply chain, the perishable nature of the commodities can represent a particular complexity problem. It is, therefore, appropriate to consider decision support tools that take into account the characteristics of the products, the needs and the requirements of producers, sellers and consumers. This paper presents a green vehicle routing model for fresh agricultural product distribution and designs an adaptive hybrid nutcracker optimization algorithm (AH-NOA) based on k-means clustering to solve the problem. In the process, the AH-NOA uses the CW algorithm to increase population diversity and adds genetic operators and local search operators to enhance the global search ability for nutcracker optimization. Finally, the experimental data show that the proposed approaches effectively avoid local optima, promote population diversity and reduce total costs and carbon emission costs. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture—Series II)

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