Journal Description
AgriEngineering
AgriEngineering
is an international, peer-reviewed, open access journal on the engineering science of agricultural and horticultural production, published quarterly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), PubAg, FSTA, AGRIS, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Agricultural Engineering) / CiteScore - Q1 (Horticulture)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 25.8 days after submission; acceptance to publication is undertaken in 5.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.0 (2023);
5-Year Impact Factor:
3.1 (2023)
Latest Articles
Dynamic Cutting Properties of Miscanthus (giganteus) Stems Using an Impact Tester
AgriEngineering 2024, 6(3), 1987-2000; https://doi.org/10.3390/agriengineering6030116 (registering DOI) - 27 Jun 2024
Abstract
Miscanthus (giganteus) is a relatively new energy crop. Its mechanical properties are important for the design and modification of harvesting and processing machines or equipment. The cutting strength is critical to improve the field performance of mowing and other cutting mechanisms. The effects
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Miscanthus (giganteus) is a relatively new energy crop. Its mechanical properties are important for the design and modification of harvesting and processing machines or equipment. The cutting strength is critical to improve the field performance of mowing and other cutting mechanisms. The effects of the cutting blade type, sample supporting method, and sample locations (upper or lower) where it was taken from a single plant stem on the cutting force and energy were studied. A serrated cutting blade and a flat blade were selected to cut Miscanthus samples at the node and internode using a high-speed impact tester. The cutting force, sample diameter, and cutting speed were recorded. The specific cutting force and energy were then calculated based on the cross-sectional area of the stem sample. The average diameter of the Miscanthus samples used for this study was 9.5 mm. The blade cutting speeds for all treatments were ranged from 8.2 ms−1 to 11.3 ms−1. Overall, the maximum specific cutting force and energy of the flat blade were found at the upper portion of a plant stem, which was 441 N cm−2 and 8.3 J cm−2 at the node when one side of the stem sample was fixed, and 469 N cm−2 and 12.1 J cm−2 at the internode if both sides of the sample were fixed. The cutting strengths at the node and internode were significantly different no matter at the upper portion or lower portion of the plant. When using the serrated blade, the maximum specific cutting force was also found at the upper node section with a value of 511 N cm−2 and 437 N cm−2 for one-side fixed and two-side fixed, respectively. Meanwhile, the corresponding maximum specific cutting energies for these two cases were 10.5 J cm−2 and 10.3 J cm−2, respectively. Statistical analysis showed that the blade types and stem sample locations significantly affected the specific cutting force and energy with a 95% confidence level. The sample support methods did not make significant differences when comparing the specific cutting force and energy; but the actual cutting force and energy were significantly different.
Full article
(This article belongs to the Section Agricultural Mechanization and Machinery)
Open AccessArticle
High-Resolution Yield Mapping for Eucalyptus grandis—A Case Study
by
Rafael Donizetti Dias, José Paulo Molin, Marcelo Chan Fu Wei and Clayton Alcarde Alvares
AgriEngineering 2024, 6(3), 1972-1986; https://doi.org/10.3390/agriengineering6030115 (registering DOI) - 26 Jun 2024
Abstract
Yield data represent a valuable layer for supporting decision-making as they reflect crop management results. Forestry decision-makers often rely on coarse spatial resolution data (e.g., forest inventory plots) despite the availability of modern harvesters that can provide high-resolution forestry yield data. The objectives
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Yield data represent a valuable layer for supporting decision-making as they reflect crop management results. Forestry decision-makers often rely on coarse spatial resolution data (e.g., forest inventory plots) despite the availability of modern harvesters that can provide high-resolution forestry yield data. The objectives of this study were to present a method for generating high-resolution Eucalyptus grandis yield data (individual tree-level) and explore their applications, such as correlation analysis with soil attributes to aid nutrient recommendations. Two evaluations were conducted at two sites in Brazil: (a) assessing the positioning accuracy of the global navigation satellite system (GNSS) receiver positioning, and (b) analyzing the yield data and their correlation with the soil attributes. The results indicated that positioning the GNSS receiver at the harvesting head provided higher accuracy than placement at the top of the harvester cabin for individual tree-level data. Reliable yield data were generated despite the GNSS receiver’s increased susceptibility to damage when mounted on a harvest head. The linear correlation analysis between the Eucalyptus grandis yield data and soil attributes showed both negative (Clay, B, S, coarse sand, and potential acidity − H + Al) and positive correlations (K, Mg, pH-SMP, Ca, sum of bases, pH, base saturation, fine sand, total sand, and silt content). This study demonstrates the feasibility of obtaining high-resolution yield data at the individual tree-level and their correlation with soil attributes, providing valuable insights for improving forestry decision-making.
Full article
(This article belongs to the Special Issue Application of Geographic Information System and Remote Sensing Technology in Agricultural and Forestry Research)
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Open AccessArticle
Adaptation of Conventional Wheat Flour Mill to Refine Sorghum, Corn, and Cowpea
by
Michael Joseph, Sajid Alavi, Akinbode A. Adedeji, Lijia Zhu, Jeff Gwirtz and Shawn Thiele
AgriEngineering 2024, 6(3), 1959-1971; https://doi.org/10.3390/agriengineering6030114 - 24 Jun 2024
Abstract
This study evaluated the refinement of sorghum, corn, and cowpea grains using the processing steps and equipment originally designed for wheat milling that consists of a conventional gradual reduction system. The need to mill these grains resulted from a desire to produce alternative
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This study evaluated the refinement of sorghum, corn, and cowpea grains using the processing steps and equipment originally designed for wheat milling that consists of a conventional gradual reduction system. The need to mill these grains resulted from a desire to produce alternative ingredients for developing new fortified blended extruded foods used for food aid programming. Milling of white sorghum grain resulted in a crude protein content of 7.4% (wb) for both whole and coarse-milled flour. The crude protein content in whole fine-milled sorghum was 6.8% (wb), which was significantly lower than that of whole coarse flour at 9.3% (wb). A decrease in the ash content of sorghum flour correlates with the decortication process. However, degermed corn, fine and coarse, had significantly different crude protein content of 6.0 ± 0.2% (wb) and 7.7 ± 0.06% (wb), respectively. Degerming of corn improved the quality of corn flour (fine and coarse) by reducing the crude fat content from 3.3 ± 0.18% (wb) to 1.2 ± 0.02% (wb) and 0.6 ± 0.13% (wb), respectively. This helped increase the starch content from 60.1 ± 0.28% (wb) in raw corn to 74.7 ± 0.93% (wb) and 71.8 ± 0.00% (wb) in degermed fine and coarse corn flour, respectively. Cowpea milling did not produce differences in the milling stream outputs when the crude fat and crude protein were compared. Whole flour from the grains had higher milling yields than decorticated flour. This study demonstrated that a mill dedicated to wheat size reduction can be adapted to refine other grains to high quality.
Full article
(This article belongs to the Section Pre and Post-Harvest Engineering in Agriculture)
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Open AccessArticle
Autonomous Driving Strategy for a Specialized Four-Wheel Differential-Drive Agricultural Rover
by
Salvatore Martelli, Francesco Mocera and Aurelio Soma’
AgriEngineering 2024, 6(3), 1937-1958; https://doi.org/10.3390/agriengineering6030113 - 21 Jun 2024
Abstract
Recently, the agriconstruction machinery sector has been involved in a great technological revolution. The reasons that may explain this are strictly connected to the mitigation of climate change. At the same time, there is a necessity to ensure an adequate production level in
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Recently, the agriconstruction machinery sector has been involved in a great technological revolution. The reasons that may explain this are strictly connected to the mitigation of climate change. At the same time, there is a necessity to ensure an adequate production level in order to meet the increasing food demand due to the current population growth trend. In this context, the development of autonomously driven agricultural vehicles is one of the areas on which tractor manufacturers and academics are focusing. The fundamental prerequisite for an autonomous driving vehicle is the development of an appropriate motion strategy. Hence, the vehicle will be able to follow predetermined routes, accomplishing its missions. The aim of this study was the development of path-planning and path-following algorithms for an agricultural four- whee differential-drive vehicle operating in vineyard/orchard environments. The algorithms were completely developed within the MATLAB software environment. After a brief description of the geometrical characteristics of the vehicle, a parametric process to build a virtual orchard environment is proposed. Then, the functional principles of the autonomous driving algorithms are shown. Finally, the algorithms are tested, varying their main tuning parameters, and an indicator to quantify the algorithms' efficiency, named relative accuracy, is defined. The results obtained show the strong dependence between the relative accuracy and lookahead distance value assigned to the rover. Furthermore, an analysis of rover positioning errors was performed. The results in this case show a lower influence of the location error when the accuracy of the positioning device is within 2 cm.
Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
Open AccessArticle
Model Development for Identifying Aromatic Herbs Using Object Detection Algorithm
by
Samira Nascimento Antunes, Marcelo Tsuguio Okano, Irenilza de Alencar Nääs, William Aparecido Celestino Lopes, Fernanda Pereira Leite Aguiar, Oduvaldo Vendrametto, João Carlos Lopes Fernandes and Marcelo Eloy Fernandes
AgriEngineering 2024, 6(3), 1924-1936; https://doi.org/10.3390/agriengineering6030112 - 21 Jun 2024
Abstract
The rapid evolution of digital technology and the increasing integration of artificial intelligence in agriculture have paved the way for groundbreaking solutions in plant identification. This research pioneers the development and training of a deep learning model to identify three aromatic plants—rosemary, mint,
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The rapid evolution of digital technology and the increasing integration of artificial intelligence in agriculture have paved the way for groundbreaking solutions in plant identification. This research pioneers the development and training of a deep learning model to identify three aromatic plants—rosemary, mint, and bay leaf—using advanced computer-aided detection within the You Only Look Once (YOLO) framework. Employing the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, the study meticulously covers data understanding, preparation, modeling, evaluation, and deployment phases. The dataset, consisting of images from diverse devices and annotated with bounding boxes, was instrumental in the training process. The model’s performance was evaluated using the mean average precision at a 50% intersection over union (mAP50), a metric that combines precision and recall. The results demonstrated that the model achieved a precision of 0.7 or higher for each herb, though recall values indicated potential over-detection, suggesting the need for database expansion and methodological enhancements. This research underscores the innovative potential of deep learning in aromatic plant identification and addresses both the challenges and advantages of this technique. The findings significantly advance the integration of artificial intelligence in agriculture, promoting greater efficiency and accuracy in plant identification.
Full article
(This article belongs to the Special Issue Exploring the Application of Artificial Intelligence and Image Processing in Agriculture)
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Open AccessArticle
Development of a Greenhouse Wastewater Stream Utilization System for On-Site Microalgae-Based Biostimulant Production
by
Sofia Faliagka, Georgios Kountrias, Eleni Dimitriou, Maria Álvarez-Gil, Mario Blanco-Vieites, Fabio Magrassi, Marta Notari, Eleftheria Maria Pechlivani and Nikolaos Katsoulas
AgriEngineering 2024, 6(3), 1898-1923; https://doi.org/10.3390/agriengineering6030111 - 21 Jun 2024
Abstract
The challenges to feed the world in 2050 are becoming more and more apparent. This calls for producing more with fewer inputs (most of them under scarcity), higher resource efficiency, minimum or zero effect on the environment, and higher sustainability. Therefore, increasing the
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The challenges to feed the world in 2050 are becoming more and more apparent. This calls for producing more with fewer inputs (most of them under scarcity), higher resource efficiency, minimum or zero effect on the environment, and higher sustainability. Therefore, increasing the circularity of production systems is highly significant for their sustainability. This study investigates the utilization of waste streams from greenhouse hydroponic drainage nutrient solutions for the cultivation of the microalgae Desmodesmus sp. The cultivation was done in an automatically controlled container-scale closed tubular Photo Bio-Reactor (PBR). The study included lab-scale open-pond system experiments and in situ container-scale experiments in the greenhouse wastewater system to assess biomass growth, optical density, nitrogen consumption, and the influence of enzymatic complexes on microalgae cell breakdown. A batch-harvesting process was followed, and the harvested microalgae biomass was pre-concentrated using FeCl3 as a flocculant that has demonstrated efficient sedimentation and biomass recovery. Following microalgae sedimentation, the produced biomass was used for biostimulant production by means of a biocatalysis process. The enzymatic complexes, “EnzProt”, “EnzCell”, and “EnzMix” were tested for cell breakdown, with “EnzMix” at a dosage of 10% showing the most promising results. The results demonstrate successful biomass production and nitrogen uptake in the lab-scale open-pond system, with promising upscaling results within container-scale cultivation. The findings contribute to a better assessment of the needs of Desmodesmus sp. culture and highlight the importance in optimizing culture conditions and enzymatic processes for the production of biostimulants.
Full article
Open AccessArticle
Performance of a UHF RFID Detection System to Assess Activity Levels and Lying Behaviour in Fattening Bulls
by
Kay Fromm, Julia Heinicke, Christian Ammon, Thomas Amon and Gundula Hoffmann
AgriEngineering 2024, 6(2), 1886-1897; https://doi.org/10.3390/agriengineering6020110 - 20 Jun 2024
Abstract
Animal welfare strongly influences the health and performance of cattle and is an important factor for consumer acceptance. One parameter for the quantification of health status is the lying duration, which can be deployed for the early detection of possible production-related illnesses. Usually,
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Animal welfare strongly influences the health and performance of cattle and is an important factor for consumer acceptance. One parameter for the quantification of health status is the lying duration, which can be deployed for the early detection of possible production-related illnesses. Usually, 3D-accelerometers are the tool to detect lying duration in cattle, but the handling of bulls sometimes has special requirements because frequent manipulation in daily farming routines is often not possible. An ultrahigh-frequency (UHF) radio-frequency identification (RFID) system was installed in a beef cattle barn in Germany to measure the activity and lying time of bulls. Such UHF RFID systems are typically used for estrus detection in dairy cows via activity level, but can also be considered, for instance, as an early detection for lameness or other diseases. The aim of the study was to determine whether the estimations of activity level and lying duration can also be traced in husbandry systems for fattening bulls. Two groups of bulls (Uckermärker cattle, n = 10 and n = 13) of the same age were equipped with passive UHF RFID ear transponders. Three cameras were installed to proof the system and to observe the behaviour of the animals (standing, lying, and moving). Furthermore, accelerometers were attached to the hind legs of the bulls to validate their activity and lying durations measured by the RFID system in the recorded area. Over a period of 20 days, position (UHF RFID) and accelerometer data were recorded. Videos were recorded over a period of five days. The UHF RFID system showed an overall specificity of 95.9%, a sensitivity of 97.05%, and an accuracy of 98.45%. However, the comparison of the RFID and accelerometer data revealed residuals (ԑ) of median lying time (in minutes per day) for each group of ԑGroup1 = 51.78 min/d (p < 0.001), ԑGroup2 = −120.63 min/d (p < 0.001), and ԑGroup1+2 = −34.43 min/d (p < 0.001). In conclusion, UHF RFID systems can provide reliable activity and lying durations in 60 min intervals, but accelerometer data are more accurate.
Full article
(This article belongs to the Section Livestock Farming Technology)
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Open AccessReview
Variable Depth Tillage: Importance, Applicability, and Impact—An Overview
by
Egidijus Šarauskis, Simas Sokas and Julija Rukaitė
AgriEngineering 2024, 6(2), 1870-1885; https://doi.org/10.3390/agriengineering6020109 - 20 Jun 2024
Abstract
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Tillage, as a key agricultural operation, has an important influence on soil properties and crop productivity. However, tillage at the same depth is not always the best choice as differences in soil texture, compacted topsoil, or plow pan at different depths, crop rotation,
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Tillage, as a key agricultural operation, has an important influence on soil properties and crop productivity. However, tillage at the same depth is not always the best choice as differences in soil texture, compacted topsoil, or plow pan at different depths, crop rotation, and root penetration potential signal that the depth of tillage should take greater account of the factors involved. Variable depth tillage (VDT) is an important precision farming operation, linking soil, plants, tillage machinery, smart sensors, measuring devices, computer programs, algorithms, and variability maps. This topic is important from an agronomic, energy, and environmental perspective. However, the application of VDTs in practice is currently still very limited. The aim of this study was to carry out a detailed review of scientific work on variable depth tillage, highlighting the importance of soil compaction and VDT; the measurement methods and equipment used; and the impact on soil, crops, the environment, and the economy. Based on the reviewed studies, there is a lack of studies that use fully automated depth control of tillage systems based on input data obtained with on-the-go (also known as online) proximal soil sensing. In precision agriculture, rapidly developing Internet of Things technologies allow the adaptation of various farming operations—including tillage depth—to site-specific and temporal conditions. In this context, the use of proximal soil sensing technologies coupled with electromagnetic induction, gamma rays, and multi-sensor data fusion to provide input for recommended tillage depth would be beneficial in the future. The application of VTD in specific areas is promising as it helps to reduce the negative effects of soil compaction and avoid unnecessary use of this expensive and environmentally damaging technological operation.
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Open AccessArticle
Augmented Reality Glasses Applied to Livestock Farming: Potentials and Perspectives
by
Gabriele Sara, Daniele Pinna, Giuseppe Todde and Maria Caria
AgriEngineering 2024, 6(2), 1859-1869; https://doi.org/10.3390/agriengineering6020108 - 20 Jun 2024
Abstract
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In the last decade, Smart Glasses (SG) and augmented reality (AR) technology have gained considerable interest in all production sectors. In the agricultural field, an SG can be considered a valuable device to support farmers and agricultural operators. SGs can be distinguished by
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In the last decade, Smart Glasses (SG) and augmented reality (AR) technology have gained considerable interest in all production sectors. In the agricultural field, an SG can be considered a valuable device to support farmers and agricultural operators. SGs can be distinguished by technical specification, type of display, interaction system, and specific features. These aspects can affect their integration into farms, influencing users’ experience and the consequent level of performance. The aim of the study was to compare four SGs for AR with different technical characteristics to evaluate their potential integration in agricultural systems. This study analyzed the capability of QR code reading in terms of distance and time of visualization, the audio–video quality of image streaming during conference calls and, finally, the battery life. The results showed different levels of performance in QR code reading for the selected devices, while the audio–video quality in conference calls demonstrated similar results for all the devices. Moreover, the battery life of the SGs ranged from 2 to 7 h per charge cycle, and it was influenced by the type of usage. The findings also underlined the potential use and integration of SGs to support operators during farm management. Specifically, SGs might enable farmers to obtain fast and precise augmented information using markers placed at different points on the farm. In conclusion, the study highlights how the different technical characteristics of SG represent an important factor in the selection of the most appropriate device for a farm.
Full article
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Open AccessArticle
Effect of Defoliation on Growth, Yield and Forage Quality in Maize, as a Simulation of the Impact of Fall Armyworm (Spodoptera frugiperda)
by
Kouki Tashiro, Midori Ishitani, Saaya Murai, Mitsuhiro Niimi, Manabu Tobisa, Sachiko Idota, Tetsuya Adachi-Hagimori and Yasuyuki Ishii
AgriEngineering 2024, 6(2), 1847-1858; https://doi.org/10.3390/agriengineering6020107 - 19 Jun 2024
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This study assesses the impact of defoliation applied to three developmental stages across three cropping seasons from 2021 to 2023 on growth, yield and forage quality in maize. The experimental design included three treatments: defoliation of three expanded leaves at the 3rd–4th leaf
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This study assesses the impact of defoliation applied to three developmental stages across three cropping seasons from 2021 to 2023 on growth, yield and forage quality in maize. The experimental design included three treatments: defoliation of three expanded leaves at the 3rd–4th leaf stage (DF1), the 5th–6th expanded leaves by leaf punch (DF2) and expanding leaves with the DF2 treatment (DF3) at the 6th–7th leaf stages, compared with no defoliation (control). Over three years, the most significant decrease in dry matter (DM) yield occurred in DF1 during spring sowing, while in summer sowing, the largest reduction was in DF3, both of which were correlated with changes in the number of grains per ear. The DM yields at harvest were positively correlated with plant leaf areas at the silking stage. The digestibility of forage in in vitro DM decreased concomitantly with an increase in acid detergent fiber content, indicating a decrease in forage quality. Given the frequent severe damage observed in summer sown maize and the detrimental effects of early growth stage leaf feeding on quality and quantity of spring sown maize, the application of registered insecticides is advised to reduce pest damage to maize crops.
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Open AccessArticle
Interoperability Analysis of Tomato Fruit Detection Models for Images Taken at Different Facilities, Cultivation Methods, and Times of the Day
by
Hiroki Naito, Kota Shimomoto, Tokihiro Fukatsu, Fumiki Hosoi and Tomohiko Ota
AgriEngineering 2024, 6(2), 1827-1846; https://doi.org/10.3390/agriengineering6020106 - 19 Jun 2024
Abstract
This study investigated the interoperability of a tomato fruit detection model trained using nighttime images from two greenhouses. The goal was to evaluate the performance of the models in different environmets, including different facilities, cultivation methods, and imaging times. An innovative imaging approach
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This study investigated the interoperability of a tomato fruit detection model trained using nighttime images from two greenhouses. The goal was to evaluate the performance of the models in different environmets, including different facilities, cultivation methods, and imaging times. An innovative imaging approach is introduced to eliminate the background, highlight the target plants, and test the adaptability of the model under diverse conditions. The results demonstrate that the tomato fruit detection accuracy improves when the domain of the training dataset contains the test environment. The quantitative results showed high interoperability, achieving an average accuracy (AP50) of 0.973 in the same greenhouse and a stable performance of 0.962 in another greenhouse. The imaging approach controlled the lighting conditions, effectively eliminating the domain-shift problem. However, training on a dataset with low diversity or inferring plant appearance images but not on the training dataset decreased the average accuracy to approximately 0.80, revealing the need for new approaches to overcome fruit occlusion. Importantly, these findings have practical implications for the application of automated tomato fruit set monitoring systems in greenhouses to enhance agricultural efficiency and productivity.
Full article
(This article belongs to the Section Computer Applications and Artificial Intelligence in Agriculture)
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Open AccessArticle
Development of a Cross-Platform Mobile Application for Fruit Yield Estimation
by
Brandon Duncan, Duke M. Bulanon, Joseph Ichiro Bulanon and Josh Nelson
AgriEngineering 2024, 6(2), 1807-1826; https://doi.org/10.3390/agriengineering6020105 - 19 Jun 2024
Abstract
The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. The
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The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. The Fruit Harvest Helper seeks to simplify their process by detecting apples on images of apple trees. Once the number of apples is detected, a correlation can then be applied to this value to obtain a usable yield estimate for an apple tree. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. Borrowing ideas from the former iOS app, the new application was designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app uses a color ratio-based image-segmentation algorithm written in C++ to detect apples. This algorithm detects apples within apple tree images that farmers select for processing by using OpenCV functions and C++ code. The results of testing the algorithm on a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. The algorithm’s processing time was tested for Android and iOS, yielding an average performance of 1.16 s on Android and 0.14 s on iOS. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture. The app is nearing readiness for farmers to use for the purpose of yield monitoring and farm management within Pink Lady apple orchards.
Full article
(This article belongs to the Section Computer Applications and Artificial Intelligence in Agriculture)
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Open AccessArticle
Controlled Traffic Farm: Fuel Demand and Carbon Emissions in Soybean Sowing
by
Murilo Battistuzzi Martins, Aldir Carpes Marques Filho, Cássio de Castro Seron, Wellingthon da Silva Guimarães Júnnyor, Eduardo Pradi Vendruscolo, Fernanda Pacheco de Almeida Prado Bortolheiro, Diego Miguel Blanco Bertolo, Arthur Gabriel Caldas Lopes and Lucas Santos Santana
AgriEngineering 2024, 6(2), 1794-1806; https://doi.org/10.3390/agriengineering6020104 (registering DOI) - 18 Jun 2024
Abstract
Soil compaction between crop rows can increase a machine’s performance by reducing rolling resistance and fuel demand. Controlled Traffic Farm (CTF) stands out among modern techniques for increasing agricultural sustainability because the machines continuously travel along the same path in the field, reducing
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Soil compaction between crop rows can increase a machine’s performance by reducing rolling resistance and fuel demand. Controlled Traffic Farm (CTF) stands out among modern techniques for increasing agricultural sustainability because the machines continuously travel along the same path in the field, reducing plant crush and compacting the soil in the traffic line. This study evaluated fuel consumption and CO2 emissions at different CTF intensities in different soil management strategies for soybean crop. The experimental design involved randomized blocks in a split-plot scheme with four replications. The plots constituted the three types of soil management: conventional tillage, no-tillage with straw millet cover, and no-tillage with brachiária straw cover. The subplots constituted for agricultural tractors were passed over in traffic lines (2, 4, and 8 times). We evaluated agricultural tractor fuel consumption, CO2 emissions, and soybean productivity. The straw cover and tractor-pass significantly affected the fuel consumption and greenhouse gas emissions of the soybean cultivation. Fuel consumption and CO2 emissions were reduced due to the machine-pass increase, regardless of soil management. Thus, a CTF reduces rolling resistance and increases crop environmental efficiency. Bare-soil areas increased by 20.8% and 27.9% with respect to fuel consumption, compared to straw-cover systems. Brachiária straw and millet reduce CO2 emissions per hectare by 20% and 28% compared to bare soil. Lower traffic intensities (two passes) showed (13.72%) higher soybean yields (of 4.04 Mg ha−1). Investigating these effects in other types of soil and mechanized operations then becomes essential.
Full article
(This article belongs to the Special Issue Research Progress of Agricultural Machinery Testing)
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Open AccessArticle
A Data-Driven Method for Water Quality Analysis and Prediction for Localized Irrigation
by
Roberto Fray da Silva, Marcos Roberto Benso, Fernando Elias Corrêa, Tamara Guindo Messias, Fernando Campos Mendonça, Patrícia Angelica Alves Marques, Sergio Nascimento Duarte, Eduardo Mario Mendiondo, Alexandre Cláudio Botazzo Delbem and Antonio Mauro Saraiva
AgriEngineering 2024, 6(2), 1771-1793; https://doi.org/10.3390/agriengineering6020103 - 18 Jun 2024
Abstract
Several factors contribute to the increase in irrigation demand: population growth, demand for higher value-added products, and the impacts of climate change, among others. High-quality water is essential for irrigation, so knowledge of water quality is critical. Additionally, water use in agriculture has
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Several factors contribute to the increase in irrigation demand: population growth, demand for higher value-added products, and the impacts of climate change, among others. High-quality water is essential for irrigation, so knowledge of water quality is critical. Additionally, water use in agriculture has been increasing in the last decades. Lack of water quality can cause drip clog, a lack of application uniformity, cross-contamination, and direct and indirect impacts on plants and soil. Currently, there is a need for more automated methods for evaluating and monitoring water quality for irrigation purposes, considering different aspects, from impacts on soil to impacts on irrigation systems. This work proposes a data-driven method to address this gap and implemented it in a case study in the PCJ river basin in Brazil. The methodology contains nine components and considers the main steps of the data lifecycle and the traditional machine learning workflow, allowing for automated knowledge extraction and providing important information for improving decision making. The case study illustrates the use of the methodology, highlighting its main advantages and challenges. Clustering different scenarios in three hydrological years (high, average, and lower streamflows) and considering different inputs (soil-related metrics, irrigation system-related metrics, and all metrics) helped generate new insights into the area that would not be easily obtained using traditional methods.
Full article
(This article belongs to the Section Agricultural Irrigation Systems)
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Open AccessArticle
Performance of Neural Networks in the Prediction of Nitrogen Nutrition in Strawberry Plants
by
Jamile Raquel Regazzo, Thiago Lima da Silva, Marcos Silva Tavares, Edson José de Souza Sardinha, Caroline Goulart Figueiredo, Júlia Luna Couto, Tamara Maria Gomes, Adriano Rogério Bruno Tech and Murilo Mesquita Baesso
AgriEngineering 2024, 6(2), 1760-1770; https://doi.org/10.3390/agriengineering6020102 - 18 Jun 2024
Abstract
Among the technological tools used in precision agriculture, the convolutional neural network (CNN) has shown promise in determining the nutritional status of plants, reducing the time required to obtain results and optimizing the variable application rates of fertilizers. Not knowing the appropriate amount
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Among the technological tools used in precision agriculture, the convolutional neural network (CNN) has shown promise in determining the nutritional status of plants, reducing the time required to obtain results and optimizing the variable application rates of fertilizers. Not knowing the appropriate amount of nitrogen to apply can cause environmental damage and increase production costs; thus, technological tools are required that identify the plant’s real nutritional demands, and that are subject to evaluation and improvement, considering the variability of agricultural environments. The objective of this study was to evaluate and compare the performance of two convolutional neural networks in classifying leaf nitrogen in strawberry plants by using RGB images. The experiment was carried out in randomized blocks with three treatments (T1: 50%, T2: 100%, and T3: 150% of recommended nitrogen fertilization), two plots and five replications. The leaves were collected in the phenological phase of floral induction and digitized on a flatbed scanner; this was followed by processing and analysis of the models. ResNet-50 proved to be superior compared to the personalized CNN, achieving accuracy rates of 78% and 48% and AUC of 76%, respectively, increasing classification accuracy by 38.5%. The importance of this technique in different cultures and environments is highlighted to consolidate this approach.
Full article
(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)
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Open AccessArticle
Enhancing Dust Control for Cage-Free Hens with Electrostatic Particle Charging Systems at Varying Installation Heights and Operation Durations
by
Ramesh Bahadur Bist, Xiao Yang, Sachin Subedi, Bidur Paneru and Lilong Chai
AgriEngineering 2024, 6(2), 1747-1759; https://doi.org/10.3390/agriengineering6020101 - 17 Jun 2024
Abstract
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The poultry industry is shifting towards more sustainable and ethical practices, including adopting cage-free (CF) housing to enhance hen behavior and welfare. However, ensuring optimal indoor air quality, particularly concerning particulate matter (PM), remains challenging in CF environments. This study explores the effectiveness
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The poultry industry is shifting towards more sustainable and ethical practices, including adopting cage-free (CF) housing to enhance hen behavior and welfare. However, ensuring optimal indoor air quality, particularly concerning particulate matter (PM), remains challenging in CF environments. This study explores the effectiveness of electrostatic particle ionization (EPI) technology in mitigating PM in CF hen houses while considering the height at which the technology is placed and the duration of the electric supply. The primary objectives are to analyze the impact of EPI in reducing PM and investigate its power consumption correlation with electric supply duration. The study was conducted in a laying hen facility with four identical rooms housing 720 laying hens. The study utilized a Latin Square Design method in two experiments to assess the impact of EPI height and electric supply durations on PM levels and electricity consumption. Experiment 1 tested four EPI heights: H1 (1.5 m or 5 ft), H2 (1.8 m or 6 ft), H3 (2.1 m or 7 ft), and H4 (2.4 m or 8 ft). Experiment 2 examined four electric supply durations: D1 (control), D2 (8 h), D3 (16 h), and D4 (24 h), through 32 feet corona pipes. Particulate matter levels were measured at three different locations within the rooms for a month, and statistical analysis was conducted using ANOVA with a significance level of ≤0.05. The study found no significant differences in PM concentrations among different EPI heights (p > 0.05). However, the duration of EPI system operation had significant effects on PM1, PM2.5, and PM4 concentrations (p < 0.05). Longer EPI durations resulted in more substantial reductions: D2—17.8% for PM1, 11.0% for PM2.5, 23.1% for PM4, 23.7% for PM10, and 22.7% for TSP; D3—37.6% for PM1, 30.4% for PM2.5, 39.7% for PM4, 40.2% for PM10, and 41.1% for TSP; D4—36.6% for PM1, 24.9% for PM2.5, 38.6% for PM4, 36.3% for PM10, and 37.9% for TSP compared to the D1. These findings highlight the importance of prolonged EPI system operation for enhancing PM reduction in CF hen houses. However, utilizing 16 h EPI systems during daylight may offer a more energy-efficient approach while maintaining effective PM reduction. Further research is needed to optimize PM reduction strategies, considering factors like animal activities, to improve air quality and environmental protection in CF hen houses.
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Open AccessArticle
Tractor Three-Point Hitch Control for an Independent Lower Arms System
by
Yogesh M. Chukewad, Sidakdeep Chadha, Karan S. Jagdale, Nishant Elkunchwar, Uriel A. Rosa and Zachary Omohundro
AgriEngineering 2024, 6(2), 1725-1746; https://doi.org/10.3390/agriengineering6020100 - 14 Jun 2024
Abstract
The three-point hitch, found on agricultural tractors, facilitates the raising and lowering of an attached implement. Some tractors include a rock shaft that comprises a physical shaft that interconnects and facilitates the raising and lowering of the lower arms of the three-point hitch
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The three-point hitch, found on agricultural tractors, facilitates the raising and lowering of an attached implement. Some tractors include a rock shaft that comprises a physical shaft that interconnects and facilitates the raising and lowering of the lower arms of the three-point hitch in a synchronized manner. In this study, we deal with a hitch system with the lower arms actuated by two independent hydraulic cylinders. This innovative tractor hitch system design allows the implement to follow the terrain, instead of the tractor, about the fore–aft (roll) axis of the tractor. However, since the two lower arms are independent, a specialized controller is needed to move these arms in unison. First, we present a position controller for individual arms and a roll controller to move these arms together. Second, we present a unique algorithm to emulate a physical rock shaft while the implement is operating in float mode. The algorithm ensures that the implement does not roll around the fore–aft axis while making sure it moves up and down vertically to follow the terrain. We present experimental results from the step response of the hitch system’s height while tracking a velocity reference. With the roll of the implement defined as the difference between the left arm’s position in percentage and that of the right arm in percentage, we observe that the largest mean roll was 0.23% with a flail mower attached and 0.26% without any implement. We then present results from the implement’s position in the float mode when the software rock shaft was activated and compare them with the case without the software rock shaft. The experiments showed that, when the software rock shaft was turned on, the mean roll reduced from 4.64% to 0.58% with a seed drill implement and from −3.99% to −0.59% with a flail mower implement. The standard deviations in these two implement cases improved from 16.77% to 2.79% and 6.45% to 3.53%, respectively, proving the effectiveness of the software rock shaft and its potential to replace the physical rock shaft found on the traditional tractors.
Full article
(This article belongs to the Section Agricultural Mechanization and Machinery)
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Open AccessArticle
Evaluation of the Use of Vacuum-Dehydrated Minced Meat in Beef Patty Production
by
Mehmet Başlar, Barış Yalınkılıç, Kübra Feyza Erol and Mustafa Ü. İrkilmez
AgriEngineering 2024, 6(2), 1712-1724; https://doi.org/10.3390/agriengineering6020099 - 14 Jun 2024
Abstract
This study aimed to determine the usage potential of vacuum-dehydrated ground beef in beef patty production. First, the fresh ground beef was dehydrated in vacuum dryers at 25, 35, and 45 °C for dehydration kinetics and color change. Then, the vacuum-dehydrated ground beef
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This study aimed to determine the usage potential of vacuum-dehydrated ground beef in beef patty production. First, the fresh ground beef was dehydrated in vacuum dryers at 25, 35, and 45 °C for dehydration kinetics and color change. Then, the vacuum-dehydrated ground beef was rehydrated, and three different beef patties were separately produced using fresh ground beef, the rehydrated ground beef, and a mixture of the two (1:1). According to the results, the dehydration significantly decreased the L*, a*, and b* values of ground beef; however, after rehydration, the L* and b* values were not significantly different from the control values. The cooking loss for beef patties produced with rehydrated ground beef was higher than the control. However, there was no significant difference in the sensory of the beef patties among the treatments. In conclusion, there is potential for using vacuum-dehydrated ground beef in beef patty production.
Full article
(This article belongs to the Special Issue Novel Methods for Food Product Preservation)
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Open AccessArticle
Remote Monitoring of Coffee Leaf Miner Infestation Using Machine Learning
by
Emerson Ferreira Vilela, Gabriel Dumbá Monteiro de Castro, Diego Bedin Marin, Charles Cardoso Santana, Daniel Henrique Leite, Christiano de Sousa Machado Matos, Cileimar Aparecida da Silva, Iza Paula de Carvalho Lopes, Daniel Marçal de Queiroz, Rogério Antonio Silva, Giuseppe Rossi, Gianluca Bambi, Leonardo Conti and Madelaine Venzon
AgriEngineering 2024, 6(2), 1697-1711; https://doi.org/10.3390/agriengineering6020098 - 13 Jun 2024
Abstract
The coffee leaf miner (Leucoptera coffeella) is a key pest in coffee-producing regions in Brazil. The objective of this work was to evaluate the potential of machine learning algorithms to identify coffee leaf miner infestation by considering the assessment period and
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The coffee leaf miner (Leucoptera coffeella) is a key pest in coffee-producing regions in Brazil. The objective of this work was to evaluate the potential of machine learning algorithms to identify coffee leaf miner infestation by considering the assessment period and Sentinel-2 satellite images generated on the Google Earth Engine platform. Coffee leaf miner infestation in the field was measured monthly from 2019 to 2023. Images were selected from the Sentinel-2 satellite to determine 13 vegetative indices. The selection of images and calculations of the vegetation indices were carried out using the Google Earth Engine platform. A database was generated with information on coffee leaf miner infestation, vegetation indices, and assessment times. The database was separated into training data and testing data. Nine machine learning algorithms were used, including Linear Discriminant Analysis, Random Forest, Support Vector Machine, k-nearest neighbors, and Logistic Regression, and a principal component analysis was conducted for each algorithm. After optimizing the hyperparameters, the testing data were used to validate the model. The best model to estimate miner infestation was RF, which had an accuracy of 0.86, a kappa index of 0.64, and a precision of 0.87. The developed models were capable of monitoring coffee leaf miner infestation.
Full article
(This article belongs to the Special Issue Application of Geographic Information System and Remote Sensing Technology in Agricultural and Forestry Research)
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Open AccessArticle
The Effect of Vortex Generators on Spray Deposition and Drift from an Agricultural Aircraft
by
Daniel E. Martin and Mohamed A. Latheef
AgriEngineering 2024, 6(2), 1683-1696; https://doi.org/10.3390/agriengineering6020097 - 12 Jun 2024
Abstract
Vortex generators (VGs) attached to the leading edge of an agricultural aircraft are purported to control airflow over the upper surface of the wing by creating small vortices that delay boundary layer separation, thereby improving the performance of the aircraft. These devices are
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Vortex generators (VGs) attached to the leading edge of an agricultural aircraft are purported to control airflow over the upper surface of the wing by creating small vortices that delay boundary layer separation, thereby improving the performance of the aircraft. These devices are commercially available for use in the aviation industry, primarily to increase pilot control of the aircraft. The benefits attributed to VGs remain largely descriptive and anecdotal in nature without rigorous empirical assessment in the field. The intent of this study was to evaluate whether this aerodynamic device could improve deposition or reduce drift when mounted on an agricultural aircraft. Airborne drift and ground deposition were measured with monofilament lines and Mylar cards, respectively. Deposits were expressed as percent of fluorometric response using a spectrofluorophotometer. There were 46% fewer downwind drift deposits on monofilament lines when VGs were installed than when VGs were not installed. Whether or not VGs were installed on the aircraft was the predominant factor which influenced deposition on monofilament lines. Spray deposits on Mylar cards placed at ground level downwind of the applications at three different locations (5, 10, and 20 m) varied significantly (p < 0.0001) between treatments, with corresponding 31, 54, and 61% reductions in downwind deposits when VGs were installed. While these findings overall are positive, this is the first known study of its type, and more research is warranted to better understand the role of vortex generators in the reduction in drift relative to aerially applied sprays.
Full article
(This article belongs to the Section Agricultural Mechanization and Machinery)
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