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 5.5 days (median values for papers published in this journal in the first half of 2024).
- 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
Phenological Monitoring of Irrigated Sugarcane Using Google Earth Engine, Time Series, and TIMESAT in the Brazilian Semi-arid
AgriEngineering 2024, 6(4), 3799-3822; https://doi.org/10.3390/agriengineering6040217 - 18 Oct 2024
Abstract
Monitoring sugarcane phenology is essential since the globalized market requires reliable information on the quantity of raw materials for the industrial production of sugar and alcohol. In this context, the general objective of this study was to evaluate the phenological seasonality of the
[...] Read more.
Monitoring sugarcane phenology is essential since the globalized market requires reliable information on the quantity of raw materials for the industrial production of sugar and alcohol. In this context, the general objective of this study was to evaluate the phenological seasonality of the sugarcane varieties SP 79-1011 and VAP 90-212 observed from the NDVI time series over 19 years (2001–2020) from global databases. In addition, this research had the following specific objectives: (i) to estimate phenological parameters (Start of Season (SOS), End of Season (EOS), Length of Season (LOS), and Peak of Season (POS)) using TIMESAT software in version 3.3 applied to the NDVI time series over 19 years; (ii) to characterize the land use and land cover obtained from the MapBiomas project; (iii) to analyze rainfall variability; and (iv) to validate the sugarcane harvest date (SP 79-1011). This study was carried out in sugarcane growing areas in Juazeiro, Bahia, Brazil. The results showed that the NDVI time series did not follow the rainfall in the region. The sugarcane areas advanced over the savanna formation (Caatinga), reducing them to remnants along the irrigation channels. The comparison of the observed harvest dates of the SP 79-1011 variety to the values estimated with the TIMESAT software showed an excellent fit of 0.99. The mean absolute error in estimating the sugarcane harvest date was approximately ten days, with a performance index of 0.99 and a correlation coefficient of 0.99, significant at a 5% confidence level. The TIMESAT software was able to estimate the phenological parameters of sugarcane using MODIS sensor images processed on the Google Earth Engine platform during the evaluated period (2001 to 2020).
Full article
(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)
►
Show Figures
Open AccessArticle
Detection and Early Warning of Duponchelia fovealis Zeller (Lepidoptera: Crambidae) Using an Automatic Monitoring System
by
Edgar Rodríguez-Vázquez, Agustín Hernández-Juárez, Audberto Reyes-Rosas, Carlos Patricio Illescas-Riquelme and Francisco Marcelo Lara-Viveros
AgriEngineering 2024, 6(4), 3785-3798; https://doi.org/10.3390/agriengineering6040216 - 18 Oct 2024
Abstract
In traditional pest monitoring, specimens are manually inspected, identified, and counted. These techniques can lead to poor data quality and hinder effective pest management decisions due to operational and economic limitations. This study aimed to develop an automatic detection and early warning system
[...] Read more.
In traditional pest monitoring, specimens are manually inspected, identified, and counted. These techniques can lead to poor data quality and hinder effective pest management decisions due to operational and economic limitations. This study aimed to develop an automatic detection and early warning system using the European Pepper Moth, Duponchelia fovealis (Lepidoptera: Crambidae), as a study model. A prototype water trap equipped with an infrared digital camera controlled using a microprocessor served as the attraction and capture device. Images captured by the system in the laboratory were processed to detect objects. Subsequently, these objects were labeled, and size and shape features were extracted. A machine learning model was then trained to identify the number of insects present in the trap. The model achieved 99% accuracy in identifying target insects during validation with 30% of the data. Finally, the prototype with the trained model was deployed in the field for result confirmation.
Full article
(This article belongs to the Section Computer Applications and Artificial Intelligence in Agriculture)
►▼
Show Figures
Figure 1
Open AccessArticle
Cashew Clones Water Productivity and Production Responses to Different Biochar Levels
by
Rubens Sonsol Gondim, Carlos Alberto Kenji Taniguchi, Luiz Augusto Lopes Serrano and Carlos Farley Herbster Moura
AgriEngineering 2024, 6(4), 3768-3784; https://doi.org/10.3390/agriengineering6040215 - 17 Oct 2024
Abstract
The cashew peduncle, the so-called cashew apple, is frequently considered as waste generated by the cashew nut industries. It needs production quality improvements to achieve a more noble use. The objective of this research was to evaluate the application of biochar over irrigation
[...] Read more.
The cashew peduncle, the so-called cashew apple, is frequently considered as waste generated by the cashew nut industries. It needs production quality improvements to achieve a more noble use. The objective of this research was to evaluate the application of biochar over irrigation water productivity, yield, and cashew apple quality of two clones (‘BRS 226’ and ‘CCP 76’) of an irrigated cashew orchard. This field experiment tested four treatments of biochar from tree pruning mixed hardwood as source material, corresponding to 0, 10, 20, and 40 g per kg of soil, equivalent to the amounts of 0.0, 1.0, 2.0, and 4.0 kg per plant, respectively. The evaluated production variables were irrigation water productivity in terms of cashew nuts and peduncles per cubic meter of irrigation water applied, cashew nuts, and apples’ individual mean weight and yield. Cashew apple quality was also evaluated by soluble solids (SS), titratable acidity (TA), soluble solids/titratable acidity ratio (SS/TA), and firmness. The use of biochar had positive effects on the nut and cashew apple irrigation water productivity, on mean individual cashew apple weight only for ‘BRS 226′ Clone and soluble solids for both clones (‘BRS 226’ and ‘CCP 76’). The soluble solids/titratable acidity ratio also improved only for the BRS 226 cashew clone. There was no statistically significant positive effect of applied biochar in cashew nut and cashew apple yield and firmness. The optimal doses were 1.70 kg, 1.90 kg, 4.00 kg, 2.10 kg, and 2.25 kg per plant of biochar, respectively.
Full article
(This article belongs to the Section Agricultural Irrigation Systems)
►▼
Show Figures
Figure 1
Open AccessArticle
Automated Detection of Young Eucalyptus Plants for Optimized Irrigation Management in Forest Plantations
by
Jhonata S. Santana, Domingos S. M. Valente, Daniel M. Queiroz, Andre L. F. Coelho, Igor A. Barbosa and Abdul Momin
AgriEngineering 2024, 6(4), 3752-3767; https://doi.org/10.3390/agriengineering6040214 - 16 Oct 2024
Abstract
Forest plantations, particularly those cultivating eucalyptus, are crucial for the wood and paper industries. However, growers often encounter challenges, such as high plant mortality, after transplantation, primarily due to water deficits. While semi-mechanized systems combining machinery and manual labor are commonly used, they
[...] Read more.
Forest plantations, particularly those cultivating eucalyptus, are crucial for the wood and paper industries. However, growers often encounter challenges, such as high plant mortality, after transplantation, primarily due to water deficits. While semi-mechanized systems combining machinery and manual labor are commonly used, they incur substantial operational costs. Fully mechanized automatic irrigation systems offer a cost-effective alternative that is gaining traction in adoption. This project aimed to develop an automatic system for eucalyptus plant detection to facilitate effective irrigation management. Two real-time eucalyptus plant detection models were built and trained using acquired field images and YOLOv8 and YOLOv5 neural networks. Evaluation metrics, such as precision, recall, mAP-50, and mAP50-95, were used to compare model performance and select the best option for localized irrigation automation. The YOLOv8 model had a mean detection precision of 0.958 and a mean recall of 0.935, with an mAP-50 of 0.974 and an mAP50-95 of 0.836. Conversely, the YOLOv5 model had a mean detection precision of 0.951 and a mean recall of 0.944, with an mAP-50 of 0.972 and an mAP50-95 of 0.791. Both models could serve as support tools for the real-time automation of localized irrigation for young eucalyptus plants, contributing to the optimization of irrigation processes in forest plantations.
Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
Open AccessArticle
Can Different Cultivars of Panicum maximum Be Identified Using a VIS/NIR Sensor and Machine Learning?
by
Gelson dos Santos Difante, Gabriela Oliveira de Aquino Monteiro, Juliana Caroline Santos Santana, Néstor Eduardo Villamizar Frontado, Jéssica Gomes Rodrigues, Aryadne Rhoana Dias Chaves, Dthenifer Cordeiro Santana, Izabela Cristina de Oliveira, Luis Carlos Vinhas Ítavo, Fabio Henrique Rojo Baio, Gabriela Souza Oliveira, Carlos Antonio da Silva Junior, Vanessa Zirondi Longhini, Alexandre Menezes Dias, Paulo Eduardo Teodoro and Larissa Pereira Ribeiro Teodoro
AgriEngineering 2024, 6(4), 3739-3751; https://doi.org/10.3390/agriengineering6040213 - 16 Oct 2024
Abstract
►▼
Show Figures
Panicum maximum cultivars have distinct characteristics, especially morphological ones related to the leaf structure and coloration, and there may be differences in the spectral behavior captured by sensors. These differences can be used in classification using machine learning (ML) algorithms to differentiate biodiversity
[...] Read more.
Panicum maximum cultivars have distinct characteristics, especially morphological ones related to the leaf structure and coloration, and there may be differences in the spectral behavior captured by sensors. These differences can be used in classification using machine learning (ML) algorithms to differentiate biodiversity within the same species. The objectives of this study were to identify ML models able to differentiate P. maximum cultivars and determine which is the best spectral input for these algorithms and whether reducing the sample size improves the response of the algorithms. The experiment was carried out at the experimental area of the Forage Sector of the School Farm belonging to the Federal University of Mato Grosso do Sul (UFMS). The leaf samples of the cultivars Massai, Mombaça, Tamani, Quênia, and Zuri were collected from experimental plots in the field. Analysis was carried out on 120 leaf samples from the P. maximum cultivars using a VIS/NIR hyperspectral sensor. After obtaining the spectral data and separating them into bands, the data were submitted for ML analysis to classify the cultivars based on the spectral variables. The algorithms tested were artificial neural networks (ANNs), REPTree and J48 decision trees, random forest (RF), and support vector machine (SVM). A logistic regression (LR) was used as a traditional classification method. Two input models were evaluated in the algorithms: the entire spectrum band provided by the sensor (ALL) and another input configuration using the calculated bands. The reflectances from the P. maximum cultivars showed different behavior, especially in the green and NIR regions. RL and ANN algorithms using all information in the spectrum are able to accurately classify the cultivars, reaching accuracies above 70 for CC and above 0.6 for kappa and F-score. VIS/NIR leaf reflectance can be a powerful tool for low-cost, non-destructive, and high-performance analysis to distinguish P. maximum cultivars. Here, we achieved better model accuracy using only 40 leaf samples. In the present study, the J48 decision tree model proved to have good classification performance regardless of the sample size used, which makes it a strategic model for forage cultivar classification studies in smaller or larger datasets.
Full article
Figure 1
Open AccessArticle
Evaluating the Effect of Pulse Width Modulation-Controlled Spray Duty Cycles on Cotton Fiber Quality Using Principal Component Analysis
by
Joe Mari Maja, Jyoti Neupane, Van Patiluna, Gilbert Miller, Aashish Karki, Michael W. Marshall, Matthew Cutulle, Jun Luo and Edward Barnes
AgriEngineering 2024, 6(4), 3719-3738; https://doi.org/10.3390/agriengineering6040212 - 14 Oct 2024
Abstract
►▼
Show Figures
The optimization of cotton defoliant application is critical for enhancing fiber quality and yield. This study aims to assess the impact of different defoliant duty cycles on cotton fiber quality by applying Principal Component Analysis (PCA) to High-Volume Instrument (HVI) data from two
[...] Read more.
The optimization of cotton defoliant application is critical for enhancing fiber quality and yield. This study aims to assess the impact of different defoliant duty cycles on cotton fiber quality by applying Principal Component Analysis (PCA) to High-Volume Instrument (HVI) data from two fields. Three duty cycles—20%, 40%, and 60%—along with a control treatment were evaluated. PCA was used to identify the key factors influencing cotton quality, with a focus on parameters such as fiber length, strength, and uniformity. The results revealed that the 40% duty cycle consistently produced the most stable and uniform cotton fiber quality across both fields, minimizing variability in critical parameters. In contrast, the 20% and 60% duty cycles, as well as the control, introduced greater variability, with the control treatment showing the most significant outliers. These findings suggest that a 40% duty cycle is optimal for balancing effective defoliation with high-quality cotton production. Future research should explore the robustness of the 40% duty cycle across different environmental conditions and investigate the integration of advanced technologies to further optimize defoliant applications. This study provides valuable insights for improving cotton production practices and ensuring consistent fiber quality.
Full article
Figure 1
Open AccessArticle
A Machine Vision System for Monitoring Wild Birds on Poultry Farms to Prevent Avian Influenza
by
Xiao Yang, Ramesh Bahadur Bist, Sachin Subedi, Zihao Wu, Tianming Liu, Bidur Paneru and Lilong Chai
AgriEngineering 2024, 6(4), 3704-3718; https://doi.org/10.3390/agriengineering6040211 - 9 Oct 2024
Abstract
The epidemic of avian influenza outbreaks, especially high-pathogenicity avian influenza (HPAI), which causes respiratory disease and death, is a disaster in poultry. The outbreak of HPAI in 2014–2015 caused the loss of 60 million chickens and turkeys. The most recent HPAI outbreak, ongoing
[...] Read more.
The epidemic of avian influenza outbreaks, especially high-pathogenicity avian influenza (HPAI), which causes respiratory disease and death, is a disaster in poultry. The outbreak of HPAI in 2014–2015 caused the loss of 60 million chickens and turkeys. The most recent HPAI outbreak, ongoing since 2021, has led to the loss of over 50 million chickens so far in the US and Canada. Farm biosecurity management practices have been used to prevent the spread of the virus. However, existing practices related to controlling the transmission of the virus through wild birds, especially waterfowl, are limited. For instance, ducks were considered hosts of avian influenza viruses in many past outbreaks. The objectives of this study were to develop a machine vision framework for tracking wild birds and test the performance of deep learning models in the detection of wild birds on poultry farms. A deep learning framework based on computer vision was designed and applied to the monitoring of wild birds. A night vision camera was used to collect data on wild bird near poultry farms. In the data, there were two main wild birds: the gadwall and brown thrasher. More than 6000 pictures were extracted through random video selection and applied in the training and testing processes. An overall precision of 0.95 ([email protected]) was reached by the model. The model is capable of automatic and real-time detection of wild birds. Missed detection mainly came from occlusion because the wild birds tended to hide in grass. Future research could be focused on applying the model to alert to the risk of wild birds and combining it with unmanned aerial vehicles to drive out detected wild birds.
Full article
(This article belongs to the Special Issue Precision Farming Technologies for Monitoring Livestock and Poultry)
►▼
Show Figures
Figure 1
Open AccessArticle
Proof-of-Concept Recirculating Air Cleaner Evaluation in a Pig Nursery
by
Jackson O. Evans, MacKenzie L. Ingle, Junyu Pan, Himanth R. Mandapati, Praveen Kolar, Lingjuan Wang-Li and Sanjay B. Shah
AgriEngineering 2024, 6(4), 3686-3703; https://doi.org/10.3390/agriengineering6040210 - 9 Oct 2024
Abstract
►▼
Show Figures
Low ventilation rates used to conserve energy in pig nurseries in winter can worsen air quality, harming piglet health. A recirculating air cleaner consisting of a dust filter and ultraviolet C (UVC) lamps was evaluated in a pig nursery. It had a recirculation
[...] Read more.
Low ventilation rates used to conserve energy in pig nurseries in winter can worsen air quality, harming piglet health. A recirculating air cleaner consisting of a dust filter and ultraviolet C (UVC) lamps was evaluated in a pig nursery. It had a recirculation rate of 6.4 air changes per hour, residence time of 0.43 s, and UVC volumetric dose of 150 J·m−3. Reduced ventilation led to high particulate matter (PM) concentrations in the nursery. During the first 9 d, the air cleaner increased floor temperature in its vicinity by 1.9 °C vs. a more distant location. The air cleaner had average removal efficiencies of 29 and 27% for PM2.5 (PM with aerodynamic equivalent diameter or AED < 2.5 µm) and PM10 (PM with AED < 10 µm), respectively. It reduced PM2.5 and PM10 concentrations by 38 and 39%, respectively, in its vicinity vs. a more distant location. The air cleaner was mostly inconsistent in inactivating heterotrophic bacteria, but it eliminated fungi. It trapped 56% of the ammonia but did not trap nitrous oxide, methane, or carbon dioxide. The air cleaner demonstrated the potential for reducing butanoic, propanoic, and pentanoic acids. Design improvements using modeling and further testing are required.
Full article
Figure 1
Open AccessArticle
Quantifying Nesting Behavior Metrics of Broiler Breeder Hens with Computationally Efficient Image Processing Algorithms and Big Data Analytics
by
Aravind Mandiga, Guoming Li, Jeanna L. Wilson, Tianming Liu, Venkat Umesh Chandra Bodempudi and Jacob Hunter Mason
AgriEngineering 2024, 6(4), 3672-3685; https://doi.org/10.3390/agriengineering6040209 - 8 Oct 2024
Abstract
Nesting behaviors are important to understand facility design, resource allowance, animal welfare, and the health of broiler breeder hens. How to automatically extract informative nesting behavior metrics of broiler breeder hens remains a question. The objective of this work was to quantify the
[...] Read more.
Nesting behaviors are important to understand facility design, resource allowance, animal welfare, and the health of broiler breeder hens. How to automatically extract informative nesting behavior metrics of broiler breeder hens remains a question. The objective of this work was to quantify the nesting behavior metrics of broiler breeder hens using computationally efficient image algorithms and big data analytics. Here, 20 broiler breeder hens and 1–2 roosters were raised in an experimental pen, and four pens equipped with six-nest-slot nest boxes were used for analyzing the nesting behaviors of broiler hens over the experimental period. Cameras were installed on the top of the nest boxes to monitor the hens’ behaviors, such as the time spent in the nest slot, frequency of visits to the nest slot, simultaneous nesting pattern, hourly time spent by the hens in each nest slot, and time spent before and after feed withdrawal, and videos were continuously recorded for nine days for nine hours a day when the hens were 56 weeks of age. Image processing algorithms, including template matching, thresholding, and contour detection, were developed and applied to quantify the hen nesting behavior metrics frame by frame. The results showed that the hens spent significantly different amounts of time and frequencies in different nest slots (p < 0.001). A decrease in the time spent in all nest slots from 1 pm to 9 pm was observed. The nest slots were not used 60.1% of the time. Overall, the proposed method is a helpful tool to quantify the nesting behavior metrics of broiler breeder hens and support precision broiler breeder management.
Full article
(This article belongs to the Special Issue The Future of Artificial Intelligence in Agriculture)
►▼
Show Figures
Figure 1
Open AccessArticle
Differentiating Growth Patterns in Winter Wheat Cultivars via Unmanned Aerial Vehicle Imaging
by
Asparuh I. Atanasov, Hristo P. Stoyanov and Atanas Z. Atanasov
AgriEngineering 2024, 6(4), 3652-3671; https://doi.org/10.3390/agriengineering6040208 - 7 Oct 2024
Abstract
Wheat is one of the most widely grown cereal crops, serving as a key factor in sustaining the nutritional and food balance in numerous countries. The use of non-contact methods for wheat monitoring allows for the rapid diagnosis of vegetation density, crop growth,
[...] Read more.
Wheat is one of the most widely grown cereal crops, serving as a key factor in sustaining the nutritional and food balance in numerous countries. The use of non-contact methods for wheat monitoring allows for the rapid diagnosis of vegetation density, crop growth, and the presence of weeds and diseases in the investigated fields. This study aims to assess the potential for differentiating growth patterns in winter wheat cultivars by examining them with two unmanned aerial vehicles (UAVs), the Mavic 2 Pro and Phantom 4 Pro, equipped with a multispectral camera from the MAPIR™ brand. Based on an experimental study conducted in the Southern Dobruja region (Bulgaria), vegetation reflectance indices, such as the Normalized-Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index 2 (EVI2), were generated, and a database was created to track their changing trends. The obtained results showed that the values of the NDVI, EVI2, and SAVI can be used to predict the productive potential of wheat, but only after accounting for the meteorological conditions of the respective growing season. The proposed methodology provides accurate results in small areas, with a resolution of 0.40 cm/pixel when flying at an altitude of 12 m and 2.3 cm/pixel when flying at an altitude of 100 m. The achieved precision in small and ultra-small agricultural areas, at a width of 1.2 m, will help wheat breeders conduct precise diagnostics of individual wheat varieties.
Full article
(This article belongs to the Special Issue Computer Vision for Agriculture and Smart Farming)
►▼
Show Figures
Figure 1
Open AccessArticle
Vegetables Treated before Drying with Natural Antioxidants plus UV-C Improve Colour and Bioactive Compounds
by
Antonio-Fer Ramírez-Fajardo, Cristina Martín-Vizcaíno, Ignacio Rodríguez-García and José Luis Guil-Guerrero
AgriEngineering 2024, 6(4), 3635-3651; https://doi.org/10.3390/agriengineering6040207 - 4 Oct 2024
Abstract
The quality of minimally processed fruits and vegetables is essential for consumers’ health and effective marketing. This study investigates the effects of UV-C irradiation, applied alone and combined with various natural antioxidants, on the preservation of bioactive compounds and the colour of dehydrated
[...] Read more.
The quality of minimally processed fruits and vegetables is essential for consumers’ health and effective marketing. This study investigates the effects of UV-C irradiation, applied alone and combined with various natural antioxidants, on the preservation of bioactive compounds and the colour of dehydrated fruits and vegetables. To achieve this, red peppers, yellow peppers, tomatoes, zucchini, eggplants, and melons were subjected to pre-treatments with natural antioxidants and UV-C before processing using low-temperature airflow (50 °C). The drying kinetics showed typical curves of hygroscopic materials, while the drying time was high due to the low temperature applied. The higher drying rate was found for eggplant, due to its porosity, thus allowing a faster moisture removal. The application of antioxidants and UV-C treatments effectively preserved the colour parameters L*, a*, and b*, while in the case of untreated dried vegetables, a significant worsening of colour parameters was noted. However, most applied pre-treatments had positive effects on bioactive compound losses. The best results were obtained using a combination of UV-C with one antioxidant mix that was composed of vanillin, rosemary, and citrus extracts, and combined with a mixture of olive, onion, garlic, and citric acid extracts, which was highly effective in preserving the colour and bioactive compounds of most dried vegetables.
Full article
(This article belongs to the Section Pre and Post-Harvest Engineering in Agriculture)
►▼
Show Figures
Figure 1
Open AccessArticle
Control Based on Nonlinear Estimators of Parametric Uncertainties Applied to an Agricultural Tractor Equipped with a Towed Implement System
by
Cuauhtémoc Acosta Lúa, Claudia Verónica Vera Vaca, Joel Hinojosa-Dávalos and Claudia Carolina Vaca García
AgriEngineering 2024, 6(4), 3618-3634; https://doi.org/10.3390/agriengineering6040206 - 1 Oct 2024
Abstract
This article presents a nonlinear control strategy designed to address parametric uncertainties in an agricultural tractor system coupled to a towed implement. The controller ensures accurate tracking of lateral and yaw velocities relative to desired reference trajectories, even under the presence of parametric
[...] Read more.
This article presents a nonlinear control strategy designed to address parametric uncertainties in an agricultural tractor system coupled to a towed implement. The controller ensures accurate tracking of lateral and yaw velocities relative to desired reference trajectories, even under the presence of parametric variations and external disturbances. The reference trajectories are derived from an “ideal” tractor model, excluding the effects of the towed implement. A High-Order Sliding Mode (HOSM) estimator is employed to provide an estimation of disturbances, which are subsequently mitigated by the controller to maintain system stability and precision. The effectiveness of the proposed control strategy is validated through Matlab-Simulink simulations, which include a double-step steer maneuver. This maneuver tests the system’s ability to handle abrupt steering changes, providing insight into the controller’s robustness and its capacity to ensure accurate trajectory tracking in demanding conditions.
Full article
(This article belongs to the Section Agricultural Mechanization and Machinery)
►▼
Show Figures
Figure 1
Open AccessArticle
Novel Specifications Regarding Biogas Production from Agriengineering Activities in Romania
by
Ioana-Ancuta Halmaciu, Ioana Ionel, Maria-Cristina Miutescu and Eugenia Grecu
AgriEngineering 2024, 6(4), 3602-3617; https://doi.org/10.3390/agriengineering6040205 - 30 Sep 2024
Abstract
This study centers on examining the carbon/nitrogen (C/N) ratio and metal levels in various batches of manure and their potential impact on biogas production through anaerobic fermentation. A novel aspect of this research involves the utilization of nine distinct batches sourced exclusively from
[...] Read more.
This study centers on examining the carbon/nitrogen (C/N) ratio and metal levels in various batches of manure and their potential impact on biogas production through anaerobic fermentation. A novel aspect of this research involves the utilization of nine distinct batches sourced exclusively from livestock manure found in Romanian farms, without mixing with other potential substrates. At present, the farms are not harvesting manure for energy, but they are keen to invest in biogas production in the future as a necessary step towards renewable energy in a circular economy and a bio-waste management model. As a general conclusion that is resulting, it is shown that both the C/N ratio and the content of heavy metals in animal manure must be known when dealing with the animal manure fermentation process, especially when aiming for biogas production. The C/N ratio in the analyzed samples ranges from 6.7 to 30.2. While the ideal ratio is often considered 20–30, good methane production can occur outside this range, as seen in Sample B (small pig farm), with a C/N ratio of 13.8, proving the highest methane output. This shows that the C/N ratio is important but not the only factor influencing biogas generation. The metal content in the manure samples is similar to other studies, with potassium (K) ranging from 1.64% to 8.96%. Calcium (Ca) and K are the main metals found, posing little concern. The variation in values is linked to feed recipes. Monitoring heavy metals is crucial not only for biogas production but also for the safe use of animal manure as fertilizer, as soil contamination limits must be continuously supervised. The results are also valuable for the management of waste used as fertilizer in agricultural fields in accordance with EU law.
Full article
(This article belongs to the Special Issue Sustainable Development of Agroecosystems: Advances in Agricultural Engineering)
►▼
Show Figures
Graphical abstract
Open AccessArticle
Intelligent Classifier for Identifying and Managing Sheep and Goat Faces Using Deep Learning
by
Chandra Shekhar Yadav, Antonio Augusto Teixeira Peixoto, Luis Alberto Linhares Rufino, Aedo Braga Silveira and Auzuir Ripardo de Alexandria
AgriEngineering 2024, 6(4), 3586-3601; https://doi.org/10.3390/agriengineering6040204 - 30 Sep 2024
Abstract
►▼
Show Figures
Computer vision, particularly in artificial intelligence (AI), is increasingly being applied in various industries, including livestock farming. Identifying and managing livestock through machine learning is essential to improve efficiency and animal welfare. The aim of this work is to automatically identify individual sheep
[...] Read more.
Computer vision, particularly in artificial intelligence (AI), is increasingly being applied in various industries, including livestock farming. Identifying and managing livestock through machine learning is essential to improve efficiency and animal welfare. The aim of this work is to automatically identify individual sheep or goats based on their physical characteristics including muzzle pattern, coat pattern, or ear pattern. The proposed intelligent classifier was built on the Roboflow platform using the YOLOv8 model, trained with 35,204 images. Initially, a Convolutional Neural Network (CNN) model was developed, but its performance was not optimal. The pre-trained VGG16 model was then adapted, and additional fine-tuning was performed using data augmentation techniques. The dataset was split into training (88%), validation (8%), and test (4%) sets. The performance of the classifier was evaluated using precision, recall, and F1-Score metrics, with comparisons against other pre-trained models such as EfficientNet. The YOLOv8 classifier achieved 95.8% accuracy in distinguishing between goat and sheep images. Compared to the CNN and VGG16 models, the YOLOv8-based classifier showed superior performance in terms of both accuracy and computational efficiency. The results confirm that deep learning models, particularly YOLOv8, significantly enhance the accuracy and efficiency of livestock identification and management. Future research could extend this technology to other livestock species and explore real-time monitoring through IoT integration.
Full article
Figure 1
Open AccessArticle
Enabling Intelligence on the Edge: Leveraging Edge Impulse to Deploy Multiple Deep Learning Models on Edge Devices for Tomato Leaf Disease Detection
by
Dennis Agyemanh Nana Gookyi, Fortunatus Aabangbio Wulnye, Michael Wilson, Paul Danquah, Samuel Akwasi Danso and Awudu Amadu Gariba
AgriEngineering 2024, 6(4), 3563-3585; https://doi.org/10.3390/agriengineering6040203 - 29 Sep 2024
Abstract
Tomato diseases, including Leaf blight, Leaf curl, Septoria leaf spot, and Verticillium wilt, are responsible for up to 50% of annual yield loss, significantly impacting global tomato production, valued at approximately USD 87 billion. In Ghana, there is a yield gap of about
[...] Read more.
Tomato diseases, including Leaf blight, Leaf curl, Septoria leaf spot, and Verticillium wilt, are responsible for up to 50% of annual yield loss, significantly impacting global tomato production, valued at approximately USD 87 billion. In Ghana, there is a yield gap of about 50% in tomato production, which requires drastic measures to increase the yield of tomatoes. Conventional diagnostic methods are labor-intensive and impractical for real-time application, highlighting the need for innovative solutions. This study addresses these issues in Ghana by utilizing Edge Impulse to deploy multiple deep-learning models on a single mobile device, facilitating the rapid and precise detection of tomato leaf diseases in the field. This work compiled and rigorously prepared a comprehensive Ghanaian dataset of tomato leaf images, applying advanced preprocessing and augmentation techniques to enhance robustness. Using TensorFlow, we designed and optimized efficient convolutional neural network (CNN) architectures, including MobileNet, Inception, ShuffleNet, Squeezenet, EfficientNet, and a custom Deep Neural Network (DNN). The models were converted to TensorFlow Lite format and quantized to int8, substantially reducing the model size and improving inference speed. Deployment files were generated, and the Edge Impulse platform was configured to enable multiple model deployments on a mobile device. Performance evaluations across edge hardware provided metrics such as inference speed, accuracy, and resource utilization, demonstrating reliable real-time detection. EfficientNet achieved a high training accuracy of 97.12% with a compact 4.60 MB model size, proving its efficacy for mobile device deployment. In contrast, the custom DNN model is optimized for microcontroller unit (MCU) deployment. This edge artificial intelligence (AI) technology integration into agricultural practices offers scalable, cost-effective, and accessible solutions for disease classification, enhancing crop management, and supporting sustainable farming practices.
Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
►▼
Show Figures
Figure 1
Open AccessArticle
Balancing Efficiency and Quality: Effects of Gradual Temperature Increase on Extra Virgin Olive Oil Extraction
by
Giulia Angeloni, Agnese Spadi, Ferdinando Corti, Luca Calamai, Piernicola Masella and Alessandro Parenti
AgriEngineering 2024, 6(4), 3553-3562; https://doi.org/10.3390/agriengineering6040202 - 26 Sep 2024
Abstract
This study examined the influence of malaxation temperatures on the extraction of extra virgin olive oil (EVOO) and its phenolic compound content, aiming to balance energy efficiency with final product quality. Extraction was tested at three temperatures of malaxation, 21 °C, 27 °C,
[...] Read more.
This study examined the influence of malaxation temperatures on the extraction of extra virgin olive oil (EVOO) and its phenolic compound content, aiming to balance energy efficiency with final product quality. Extraction was tested at three temperatures of malaxation, 21 °C, 27 °C, and a gradual increase from 21 °C to 27 °C. Higher malaxation temperatures improved extraction yields and phenolic compounds. However, a gradual temperature increase produced promising results. The research found that yields like those obtained at 27 °C could be achieved using a lowered temperature of up to 6 °C for 15 min. The gradual temperature increase resulted in a 15% increase in phenolic compounds in comparison to low temperature extracted samples. The presence of beneficial aromatic compounds, such as (E)-2-hexenal, increased with higher temperatures, enhancing the fresh and fruity sensory notes of the oil. However, compounds linked to sensory defects, such as (E)-2-heptenal, increased at higher temperatures, indicating a need for careful modulation of extraction temperatures. In conclusion, adopting a gradually increasing temperature profile during malaxation represents an advantageous strategy for optimizing EVOO extraction, improving both the quality of the final product and operational efficiency, thus contributing to more sustainable and economical production.
Full article
(This article belongs to the Special Issue Advance Technology in Olive Oil Production)
►▼
Show Figures
Figure 1
Open AccessArticle
Silicon Treatment on Sorghum Plants Prior to Glyphosate Spraying: Effects on Growth, Nutrition, and Metabolism
by
Lesly Analay Yanes Simón, Dilier Olivera Viciedo, Caio Antonio Carbonari, Stephen Oscar Duke and Leonardo Bianco de Carvalho
AgriEngineering 2024, 6(4), 3538-3552; https://doi.org/10.3390/agriengineering6040201 - 26 Sep 2024
Abstract
►▼
Show Figures
Low doses of glyphosate from application drift can be phytotoxic or stimulate growth of glyphosate-susceptible crops. The application of Si can prevent herbicide-caused plant stress. The effects of Si application (3 mM Si) on low doses (0, 36, 72, and 180 g a.e.
[...] Read more.
Low doses of glyphosate from application drift can be phytotoxic or stimulate growth of glyphosate-susceptible crops. The application of Si can prevent herbicide-caused plant stress. The effects of Si application (3 mM Si) on low doses (0, 36, 72, and 180 g a.e. ha−1) of glyphosate were determined on Sorghum bicolor in a greenhouse study. Growth parameters, mineral content, metabolite content, and glyphosate and aminomethylphosphonic acid (AMPA) content were measured. Increasing glyphosate content, but no AMPA, was found with increasing glyphosate application rates. Shoot dry weight was increased by 72 g ha−1 of glyphosate when pretreated with Si, and plant height increased in Si-treated plants treated with 72 g ha−1 of glyphosate. Si alone had no effects on growth. Shikimate content was increased by the highest glyphosate rate. Phenylalanine content was generally increased by all glyphosate treatments with or without Si, except for 72 g ha−1 glyphosate without Si. Tyrosine content was increased by 36 and 180 g ha−1 glyphosate without Si. Caffeate content was decreased by Si in the control, and ferulate content was increased with 180 g ha−1 glyphosate in Si-treated plants. Ca levels were reduced by Si at 180 g ha−1 glyphosate. Mn levels were lower than those of the control without Si for all other treatments with Si. The increases in shikimate with the highest glyphosate dose indicated that the herbicide reached its herbicide target and should be causing stress, but the only growth effect was the stimulation of some growth parameters at 72 g ha−1 of glyphosate with Si pretreatment. Similarly, there were increases in some metabolites at some glyphosate concentrations with or without Si. Our results indicate that the rates that we used cause little stress and that Si pretreatment could potentiate glyphosate hormesis for some parameters.
Full article
Figure 1
Open AccessArticle
Automated Windrow Profiling System in Mechanized Peanut Harvesting
by
Alexandre Padilha Senni, Mario Luiz Tronco, Emerson Carlos Pedrino and Rouverson Pereira da Silva
AgriEngineering 2024, 6(4), 3511-3537; https://doi.org/10.3390/agriengineering6040200 - 25 Sep 2024
Abstract
In peanut cultivation, the fact that the fruits develop underground presents significant challenges for mechanized harvesting, leading to high loss rates, with values that can exceed 30% of the total production. Since the harvest is conducted indirectly in two stages, losses are higher
[...] Read more.
In peanut cultivation, the fact that the fruits develop underground presents significant challenges for mechanized harvesting, leading to high loss rates, with values that can exceed 30% of the total production. Since the harvest is conducted indirectly in two stages, losses are higher during the digging/inverter stage than the collection stage. During the digging process, losses account for about 60% to 70% of total losses, and this operation directly influences the losses during the collection stage. Experimental studies in production fields indicate a strong correlation between losses and the height of the windrow formed after the digging/inversion process, with a positive correlation coefficient of 98.4%. In response to this high correlation, this article presents a system for estimating the windrow profile during mechanized peanut harvesting, allowing for the measurement of crucial characteristics such as the height, width and shape of the windrow, among others. The device uses an infrared laser beam projected onto the ground. The laser projection is captured by a camera strategically positioned above the analyzed area, and through advanced image processing techniques using triangulation, it is possible to measure the windrow profile at sampled points during a real experiment under direct sunlight. The technical literature does not mention any system with these specific characteristics utilizing the techniques described in this article. A comparison between the results obtained with the proposed system and those obtained with a manual profilometer showed a root mean square error of only 28 mm. The proposed system demonstrates significantly greater precision and operates without direct contact with the soil, making it suitable for dynamic implementation in a control mesh for a digging/inversion device in mechanized peanut harvesting and, with minimal adaptations, in other crops, such as beans and potatoes.
Full article
(This article belongs to the Special Issue Advancing Smart Farming through Agricultural Robots and Automation Technologies)
►▼
Show Figures
Figure 1
Open AccessArticle
Image-Based Phenotyping Framework for Blackleg Disease in Canola: Progressing towards High-Throughput Analyses via Individual Plant Extraction
by
Saba Rabab, Luke Barrett, Wendelin Schnippenkoetter, Rebecca Maher and Susan Sprague
AgriEngineering 2024, 6(4), 3494-3510; https://doi.org/10.3390/agriengineering6040199 - 24 Sep 2024
Abstract
►▼
Show Figures
Crop diseases are a significant constraint to agricultural production globally. Plant disease phenotyping is crucial for the identification, development, and deployment of effective breeding strategies, but phenotyping methodologies have not kept pace with the rapid progress in the genetic and genomic characterization of
[...] Read more.
Crop diseases are a significant constraint to agricultural production globally. Plant disease phenotyping is crucial for the identification, development, and deployment of effective breeding strategies, but phenotyping methodologies have not kept pace with the rapid progress in the genetic and genomic characterization of hosts and pathogens, still largely relying on visual assessment by trained experts. Remote sensing technologies were used to develop an automatic framework for extracting the stems of individual plants from RGB images for use in a pipeline for the automated quantification of blackleg crown canker (Leptopshaeria maculans) in mature Brassica napus plants. RGB images of the internal surfaces of stems cut transversely (cross-section) and vertically (longitudinal) were extracted from 722 and 313 images, respectively. We developed an image processing algorithm for extracting and spatially labeling up to eight individual plants within images. The method combined essential image processing techniques to achieve precise plant extraction. The approach was validated by performance metrics such as true and false positive rates and receiver operating curves. The framework was 98% and 86% accurate for cross-section and longitudinal sections, respectively. This algorithm is fundamental for the development of an accurate and precise quantification of disease in individual plants, with wide applications to plant research, including disease resistance and physiological traits for crop improvement.
Full article
Figure 1
Open AccessArticle
Nutritional Monitoring of Rhodena Lettuce via Neural Networks and Point Cloud Analysis
by
Alfonso Ramírez-Pedraza, Sebastián Salazar-Colores, Juan Terven, Julio-Alejandro Romero-González, José-Joel González-Barbosa and Diana-Margarita Córdova-Esparza
AgriEngineering 2024, 6(3), 3474-3493; https://doi.org/10.3390/agriengineering6030198 - 23 Sep 2024
Abstract
In traditional farming, fertilizers are often used without precision, resulting in unnecessary expenses and potential damage to the environment. This study introduces a new method for accurately identifying macronutrient deficiencies in Rhodena lettuce crops. We have developed a four-stage process. First, we gathered
[...] Read more.
In traditional farming, fertilizers are often used without precision, resulting in unnecessary expenses and potential damage to the environment. This study introduces a new method for accurately identifying macronutrient deficiencies in Rhodena lettuce crops. We have developed a four-stage process. First, we gathered two sets of data for lettuce seedlings: one is composed of color images and the other of point clouds. In the second stage, we employed the interactive closest point (ICP) method to align the point clouds and extract 3D morphology features for detecting nitrogen deficiencies using machine learning techniques. Next, we trained and compared multiple detection models to identify potassium deficiencies. Finally, we compared the outcomes with traditional lab tests and expert analysis. Our results show that the decision tree classifier achieved 90.87% accuracy in detecting nitrogen deficiencies, while YOLOv9c attained an mAP of 0.79 for identifying potassium deficiencies. This innovative approach has the potential to transform how we monitor and manage crop nutrition in agriculture.
Full article
(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)
►▼
Show Figures
Figure 1
Highly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Agriculture, AgriEngineering, Sustainability, Agronomy, Crops
Emerging Agricultural Engineering Sciences, Technologies, and Applications—2nd Edition
Topic Editors: Muhammad Sultan, Yuguang Zhou, Redmond R. Shamshiri, Muhammad ImranDeadline: 20 March 2025
Conferences
Special Issues
Special Issue in
AgriEngineering
Computer Vision for Agriculture and Smart Farming
Guest Editor: Mariano CrimaldiDeadline: 31 October 2024
Special Issue in
AgriEngineering
Implementation of Artificial Intelligence in Agriculture
Guest Editors: Muhammad Jehanzeb Masud Cheema, Muhammad Aqib, Ahmed Elbeltagi, Shoaib Rashid Saleem, Saddam HussainDeadline: 31 October 2024
Special Issue in
AgriEngineering
Application of Remote Sensing and GIS in Agricultural Engineering
Guest Editors: Jiang Chen, Lorena Nunes Lacerda, Lirong XiangDeadline: 15 November 2024
Special Issue in
AgriEngineering
Sustainable Development of Agroecosystems: Advances in Agricultural Engineering
Guest Editors: Taras Hutsol, Savelіі KukharetsDeadline: 30 November 2024