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AgriEngineering
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Implementation of Artificial Intelligence in Agriculture
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Implementation of Artificial Intelligence in Agriculture

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Computer Applications and Artificial Intelligence in Agriculture".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 30086

Special Issue Editors

Prof. Dr. Muhammad Jehanzeb Masud Cheema

E-Mail Website
Guest Editor
Department of Land and Water Conservation Engineering, Faculty of Agricultural Engineering and Technology, PMAS-Arid Agriculture University Rawalpindi, Rawalpindi 46300, Pakistan
Interests: transboundary river basin management; hydrological modelling; remote sensing; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Aqib

E-Mail Website
Guest Editor
University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpinid, Pakistan
Interests: artificial intelligence; deep learning; object detection; object classification; digital agriculture; smart farming; big data
Dr. Ahmed Elbeltagi

E-Mail Website
Guest Editor
Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
Interests: agricultural engineering; water resources management; irrigation science; water footprint; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Shoaib Rashid Saleem

E-Mail Website
Guest Editor
Department of Farm Machinery and Precision Engineering, PMAS-Arid Agriculture University, Rawalpindi 46000, Pakistan
Interests: precision agriculture; digital agriculture; variable rate spraying; sensing; environmental engineering; UAV; spot specific sprayer; variable rate fertilizer
Dr. Saddam Hussain

E-Mail Website
Guest Editor
1. National Center of Industrial Biotechnology (NCIB), PMAS-Arid Agriculture University, Rawalpindi 46000, Pakistan
2. Department of Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA
Interests: e-agriculture; smart farming; decision support system; remote sensing; intelligent irrigation system; AI in agriculture; variable rate spraying; variable rate fertilizer; HEIS; software solutions; spatial and temporal variability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

World population is increasing day by day and expected to reach 10 billion by 2050. The water scarcity, food security and climate change are the hot topics for sustainable growth of the agricultural products. The world is shifting from conventional agricultural practices to the modern/advanced farming techniques (i.e., precision agriculture, digital agriculture, e-agriculture or smart farming). Artificial Intelligence (AI) has major contributions in latest smart farming technologies and applications. Now a days, all kind of crop management practices including intelligent irrigation systems, soil mapping, insect/pest management, weeds management, yield estimation and prediction etc., are heavily relying on AI (including machine and deep learning) based techniques and technologies. Moreover, advanced crop harvesting technologies, fruit picking robots and drones are also getting popularity in the precision agriculture. However, 4R strategy (right place, right time, right rate and right product) can help to enhance the crop production to ensure the food security globally.

In this Special Issue, we invite authors to publish their research on a wide range of Artificial Intelligence (AI) applications for smart farming research (experimental–laboratory, pilot, or actual-scale) and analysis methods.

Potential topics include, but are not limited to:

  • Role of machine learning in Agriculture
  • Software solutions for smart farming
  • Object detection in crops management
  • AI based decision support system for agriculture
  • Development of variable rate fertilizer technologies
  • Deep learning for yield prediction in smart farming
  • Unmanned aerial vehicles for smart spraying and monitoring
  • Development and evaluation of variable rate spraying technologies
  • Robotic systems for reducing the farming inputs and environmental impact
  • Precision agriculture, digital agriculture, e-agriculture and smart farming for food security
  • Crop water productivity estimation and modeling using hybrid algorithms
  • Soil water retention, conservation, mapping and management

Prof. Dr. Muhammad Jehanzeb Masud Cheema
Dr. Muhammad Aqib
Dr. Ahmed Elbeltagi
Dr. Shoaib Rashid Saleem
Dr. Saddam Hussain
Guest Editors

Manuscript Submission Information

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

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI based agriculture
  • software solutions
  • UAVs and robotics
  • variable rate technology
  • e-agriculture
  • yield estimation
  • water footprint
  • water use efficiency
  • sustainability

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Published Papers (15 papers)

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16 pages, 5704 KiB  
Article
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
Viewed by 478
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)
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23 pages, 10727 KiB  
Article
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
Viewed by 878
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)
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16 pages, 977 KiB  
Article
Influence of Preprocessing Methods of Automated Milking Systems Data on Prediction of Mastitis with Machine Learning Models
by Olivier Kashongwe, Tina Kabelitz, Christian Ammon, Lukas Minogue, Markus Doherr, Pablo Silva Boloña, Thomas Amon and Barbara Amon
AgriEngineering 2024, 6(3), 3427-3442; https://doi.org/10.3390/agriengineering6030195 - 18 Sep 2024
Viewed by 714
Abstract
Missing data and class imbalance hinder the accurate prediction of rare events such as dairy mastitis. Resampling and imputation are employed to handle these problems. These methods are often used arbitrarily, despite their profound impact on prediction due to changes caused to the [...] Read more.
Missing data and class imbalance hinder the accurate prediction of rare events such as dairy mastitis. Resampling and imputation are employed to handle these problems. These methods are often used arbitrarily, despite their profound impact on prediction due to changes caused to the data structure. We hypothesize that their use affects the performance of ML models fitted to automated milking systems (AMSs) data for mastitis prediction. We compare three imputations—simple imputer (SI), multiple imputer (MICE) and linear interpolation (LI)—and three resampling techniques: Synthetic Minority Oversampling Technique (SMOTE), Support Vector Machine SMOTE (SVMSMOTE) and SMOTE with Edited Nearest Neighbors (SMOTEEN). The classifiers were logistic regression (LR), multilayer perceptron (MLP), decision tree (DT) and random forest (RF). We evaluated them with various metrics and compared models with the kappa score. A complete case analysis fitted the RF (0.78) better than other models, for which SI performed best. The DT, RF, and MLP performed better with SVMSMOTE. The RF, DT and MLP had the overall best performance, contributed by imputation or resampling (SMOTE and SVMSMOTE). We recommend carefully selecting resampling and imputation techniques and comparing them with complete cases before deciding on the preprocessing approach used to test AMS data with ML models. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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20 pages, 4268 KiB  
Article
Prediction of Soil Field Capacity and Permanent Wilting Point Using Accessible Parameters by Machine Learning
by Liwei Liu and Xingmao Ma
AgriEngineering 2024, 6(3), 2592-2611; https://doi.org/10.3390/agriengineering6030151 - 2 Aug 2024
Viewed by 1593
Abstract
The field capacity (FC) and permanent wilting point (PWP) are fundamental hydrological properties critical for assessing water availability within soils, rather than direct measures of soil health. Due to the challenges associated with their field measurement, alternative assessment methods are necessary. In this [...] Read more.
The field capacity (FC) and permanent wilting point (PWP) are fundamental hydrological properties critical for assessing water availability within soils, rather than direct measures of soil health. Due to the challenges associated with their field measurement, alternative assessment methods are necessary. In this study, global-scale accessible soil data were retrieved from the world soil database called the World Soil Information Service (WoSIS), and artificial neural network (ANN) and gene-expression programming (GEP) algorithms were used to predict soil FC and PWP based on easily obtainable parameters from the database. The best-fit variable combination for FC (longitude, latitude, altitude, sand content, silt content, clay content, and electrical conductivity) and PWP (best-fit FC combination plus pH) modeling was determined. Both ANN and GEP showed greater accuracy than linear-based models in simulating the FC and PWP from the best-fit variables. The mean absolute error (MAE) was reduced by 51.54% for the FC and 56.38% for the PWP by the ANN model, compared with the linear model used in the previous literature. The normalized root mean square error (NRMSE) evaluation indicated that the ANN model performed best for PWP prediction (NRMSE of 19.9%), while the GEP model was superior for FC prediction (NRMSE of 29.9%). Between the ANN and GEP models, the ANN model showed a slightly higher model of interpretability; however, the GEP model exhibited a similar or better ability to avoid large error, based on the error distribution. Overall, our results demonstrated that machine learning is effective in predicting the FC and PWP from easily accessible data from WoSIS, and the GEP model is more preferable for FC and PWP modeling. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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22 pages, 7390 KiB  
Article
Autonomous Driving Strategy for a Specialized Four-Wheel Differential-Drive Agricultural Rover
by Salvatore Martelli, Francesco Mocera and Aurelio Somà
AgriEngineering 2024, 6(3), 1937-1958; https://doi.org/10.3390/agriengineering6030113 - 21 Jun 2024
Viewed by 1381
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 [...] Read more.
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)
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14 pages, 7854 KiB  
Article
Combining Image Classification and Unmanned Aerial Vehicles to Estimate the State of Explorer Roses
by David Herrera, Pedro Escudero-Villa, Eduardo Cárdenas, Marcelo Ortiz and José Varela-Aldás
AgriEngineering 2024, 6(2), 1008-1021; https://doi.org/10.3390/agriengineering6020058 - 16 Apr 2024
Cited by 2 | Viewed by 1670
Abstract
The production of Explorer roses has historically been attractive due to the acceptance of the product around the world. This species of roses presents high sensitivity to physical contact and manipulation, creating a challenge to keep the final product quality after cultivation. In [...] Read more.
The production of Explorer roses has historically been attractive due to the acceptance of the product around the world. This species of roses presents high sensitivity to physical contact and manipulation, creating a challenge to keep the final product quality after cultivation. In this work, we present a system that combines the capabilities of intelligent computer vision and unmanned aerial vehicles (UAVs) to identify the state of roses ready for cultivation. The system uses a deep learning-based approach to estimate Explorer rose crop yields by identifying open and closed rosebuds in the field using videos captured by UAVs. The methodology employs YOLO version 5, along with DeepSORT algorithms and a Kalman filter, to enhance counting precision. The evaluation of the system gave a mean average precision (mAP) of 94.1% on the test dataset, and the rosebud counting results obtained through this technique exhibited a strong correlation (R2 = 0.998) with manual counting. This high accuracy allows one to minimize the manipulation and times used for the tracking and cultivation process. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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14 pages, 8105 KiB  
Article
Prediction of Noise Levels According to Some Exploitation Parameters of an Agricultural Tractor: A Machine Learning Approach
by Željko Barač, Dorijan Radočaj, Ivan Plaščak, Mladen Jurišić and Monika Marković
AgriEngineering 2024, 6(2), 995-1007; https://doi.org/10.3390/agriengineering6020057 - 15 Apr 2024
Viewed by 1043
Abstract
The paper presents research on measuring and the possibility of prediction of noise levels on the left and right sides of the operator within the cabin of an agricultural tractor when moving across various agrotechnical surfaces, considering movement velocity and tire pressures while [...] Read more.
The paper presents research on measuring and the possibility of prediction of noise levels on the left and right sides of the operator within the cabin of an agricultural tractor when moving across various agrotechnical surfaces, considering movement velocity and tire pressures while employing machine learning techniques. Noise level measurements were conducted on a LANDINI POWERFARM 100 type tractor, and aligned with standards (HRN ISO 5008, HRN ISO 6396 and HRN ISO 5131). The obtained noise values were divided into two data sets (left and right set) and processed using multiple linear regression (mlr) and three machine learning methods (gradient boosting machine (gbm); support vector machine using radial basis function kernel (svmRadial); monotone multi-layer perceptron neural network (monmlp)). The most accurate method, considering surfaces, from the left side data set—(R2 0.515–0.955); (RMSE 0.302–0.704); (MAE 0.225–0.488)—and the right side—(R2 0.555–0.955); (RMSE 0.180–0.969); (MAE 0.139–0.644)—was monmlp predominantly, and to a lesser extent svmRadial. On analyzing the total data sets from the left and right sides regarding surfaces, gbm emerged as the most accurate method. The application of machine learning methods demonstrated data accuracy, yet in future research, measurements on certain surfaces may need to be repeated multiple times potentially to improve accuracy further. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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12 pages, 3825 KiB  
Article
Sweet Pepper Leaf Area Estimation Using Semantic 3D Point Clouds Based on Semantic Segmentation Neural Network
by Truong Thi Huong Giang and Young-Jae Ryoo
AgriEngineering 2024, 6(1), 645-656; https://doi.org/10.3390/agriengineering6010038 - 4 Mar 2024
Viewed by 1103
Abstract
In the field of agriculture, measuring the leaf area is crucial for the management of crops. Various techniques exist for this measurement, ranging from direct to indirect approaches and destructive to non-destructive techniques. The non-destructive approach is favored because it preserves the plant’s [...] Read more.
In the field of agriculture, measuring the leaf area is crucial for the management of crops. Various techniques exist for this measurement, ranging from direct to indirect approaches and destructive to non-destructive techniques. The non-destructive approach is favored because it preserves the plant’s integrity. Among these, several methods utilize leaf dimensions, such as width and length, to estimate leaf areas based on specific models that consider the unique shapes of leaves. Although this approach does not damage plants, it is labor-intensive, requiring manual measurements of leaf dimensions. In contrast, some indirect non-destructive techniques leveraging convolutional neural networks can predict leaf areas more swiftly and autonomously. In this paper, we propose a new direct method using 3D point clouds constructed by semantic RGB-D (Red Green Blue and Depth) images generated by a semantic segmentation neural network and RGB-D images. The key idea is that the leaf area is quantified by the count of points depicting the leaves. This method demonstrates high accuracy, with an R2 value of 0.98 and a RMSE (Root Mean Square Error) value of 3.05 cm2. Here, the neural network’s role is to segregate leaves from other plant parts to accurately measure the leaf area represented by the point clouds, rather than predicting the total leaf area of the plant. This method is direct, precise, and non-invasive to sweet pepper plants, offering easy leaf area calculation. It can be implemented on laptops for manual use or integrated into robots for automated periodic leaf area assessments. This innovative method holds promise for advancing our understanding of plant responses to environmental changes. We verified the method’s reliability and superior performance through experiments on individual leaves and whole plants. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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14 pages, 2843 KiB  
Article
AI-Based Prediction of Carrot Yield and Quality on Tropical Agriculture
by Yara Karine de Lima Silva, Carlos Eduardo Angeli Furlani and Tatiana Fernanda Canata
AgriEngineering 2024, 6(1), 361-374; https://doi.org/10.3390/agriengineering6010022 - 9 Feb 2024
Cited by 2 | Viewed by 1899
Abstract
The adoption of artificial intelligence tools can improve production efficiency in the agroindustry. Our objective was to perform the predictive modeling of carrot yield and quality. The crop was grown in two commercial areas during the summer season in Brazil. The root samples [...] Read more.
The adoption of artificial intelligence tools can improve production efficiency in the agroindustry. Our objective was to perform the predictive modeling of carrot yield and quality. The crop was grown in two commercial areas during the summer season in Brazil. The root samples were taken at 200 points with a 30 × 30 m sampling grid at 82 and 116 days after sowing in both areas. The total fresh biomass, aerial part, and root biometry were quantified for previous crop harvesting to measure yield. The quality of the roots was assessed by sub-sampling three carrots by the concentration of total soluble solids (°Brix) and firmness in the laboratory. Vegetation indices were extracted from satellite imagery. The most important variables for the predictive models were selected by principal component analysis and submitted to the Artificial Neural Network (ANN), Random Forest (RF), and Multiple Linear Regression (MLR) algorithms. SAVI and NDVI indices stood out as predictors of crop yield, and the results from the ANN (R2 = 0.68) were superior to the RF (R2 = 0.67) and MLR (R2 = 0.61) models. Carrot quality cannot be modeled by the predictive models in this study; however, it should be explored in future research, including other crop variables. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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18 pages, 6666 KiB  
Article
TinyML Olive Fruit Variety Classification by Means of Convolutional Neural Networks on IoT Edge Devices
by Ali M. Hayajneh, Sahel Batayneh, Eyad Alzoubi and Motasem Alwedyan
AgriEngineering 2023, 5(4), 2266-2283; https://doi.org/10.3390/agriengineering5040139 - 1 Dec 2023
Cited by 5 | Viewed by 2219
Abstract
Machine learning (ML) within the edge internet of things (IoT) is instrumental in making significant shifts in various industrial domains, including smart farming. To increase the efficiency of farming operations and ensure ML accessibility for both small and large-scale farming, the need for [...] Read more.
Machine learning (ML) within the edge internet of things (IoT) is instrumental in making significant shifts in various industrial domains, including smart farming. To increase the efficiency of farming operations and ensure ML accessibility for both small and large-scale farming, the need for a low-cost ML-enabled framework is more pressing. In this paper, we present an end-to-end solution that utilizes tiny ML (TinyML) for the low-cost adoption of ML in classification tasks with a focus on the post-harvest process of olive fruits. We performed dataset collection to build a dataset that consists of several varieties of olive fruits, with the aim of automating the classification and sorting of these fruits. We employed simple image segmentation techniques by means of morphological segmentation to create a dataset that consists of more than 16,500 individually labeled fruits. Then, a convolutional neural network (CNN) was trained on this dataset to classify the quality and category of the fruits, thereby enhancing the efficiency of the olive post-harvesting process. The goal of this study is to show the feasibility of compressing ML models into low-cost edge devices with computationally constrained settings for tasks like olive fruit classification. The trained CNN was efficiently compressed to fit into a low-cost edge controller, maintaining a small model size suitable for edge computing. The performance of this CNN model on the edge device, focusing on metrics like inference time and memory requirements, demonstrated its feasibility with an accuracy of classification of more than 97.0% and minimal edge inference delays ranging from 6 to 55 inferences per second. In summary, the results of this study present a framework that is feasible and efficient for compressing CNN models on edge devices, which can be utilized and expanded in many agricultural applications and also show the practical insights for implementing the used CNN architectures into edge IoT devices and show the trade-offs for employing them using TinyML. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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12 pages, 1991 KiB  
Article
A Machine Learning Approach for the Estimation of Alfalfa Hay Crop Yield in Northern Nevada
by Diego Quintero, Manuel A. Andrade, Uriel Cholula and Juan K. Q. Solomon
AgriEngineering 2023, 5(4), 1943-1954; https://doi.org/10.3390/agriengineering5040119 - 23 Oct 2023
Cited by 2 | Viewed by 1490
Abstract
Increasing pressure over water resources in the western U.S. is currently forcing alfalfa (Medicago sativa L.) producers to adopt water-saving irrigation techniques. Crop yield forecasting tools can be used to develop smart irrigation scheduling methods that can be used to estimate the [...] Read more.
Increasing pressure over water resources in the western U.S. is currently forcing alfalfa (Medicago sativa L.) producers to adopt water-saving irrigation techniques. Crop yield forecasting tools can be used to develop smart irrigation scheduling methods that can be used to estimate the future effects of a given irrigation amount applied during a current irrigation event on yield. In this work, a linear model and a random forest model were used to estimate the yield of irrigated alfalfa crops in northern Nevada. It was found that water (rain + irrigation), the occurrence of extreme temperatures, and wind have a greater effect on crop yield. Other variables that accounted for the photoperiod and the dormant period were also included in the model and are also important. The linear model had the best performance with an R2 of 0.854. On the other hand, the R2 value for the random forest was 0.793. The linear model showed a good response to water variability; therefore, it is a good model to consider for use as an irrigation decision support system. However, unlike the linear model, the random forest model can capture non-linear relationships occurring between the crop, water, and the atmosphere, and its results may be enhanced by including more data for its training. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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22 pages, 5205 KiB  
Article
Integration of an Innovative Atmospheric Forecasting Simulator and Remote Sensing Data into a Geographical Information System in the Frame of Agriculture 4.0 Concept
by Giuliana Bilotta, Emanuela Genovese, Rocco Citroni, Francesco Cotroneo, Giuseppe Maria Meduri and Vincenzo Barrile
AgriEngineering 2023, 5(3), 1280-1301; https://doi.org/10.3390/agriengineering5030081 - 17 Jul 2023
Cited by 7 | Viewed by 2532
Abstract
In a world in continuous evolution and in which human needs grow exponentially according to the increasing world population, the advent of new technologies plays a fundamental role in all fields of industry, especially in agriculture. Optimizing times, automating machines, and guaranteeing product [...] Read more.
In a world in continuous evolution and in which human needs grow exponentially according to the increasing world population, the advent of new technologies plays a fundamental role in all fields of industry, especially in agriculture. Optimizing times, automating machines, and guaranteeing product quality are key objectives in the field of Agriculture 4.0, which integrates various innovative technologies to meet the needs of producers and consumers while guaranteeing respect for the environment and the planet’s resources. In this context, our research aims to propose an integrated system using data coming from an innovative experimental atmospheric and forecasting simulator (capable of predicting some characteristic climate variables subsequently validated with local sensors), combined with indices deriving from Remote Sensing and UAV images (treated with the data fusion method), that can give fundamental information related to Agriculture 4.0 with particular reference to the subsequent phases of system automation. These data, in fact, can be collected in an open-source GIS capable of displaying areas that need irrigation and fertilization and, moreover, establishing the path of an automated drone for the monitoring of the crops and the route of a self-driving tractor for the irrigation of the areas of interest. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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19 pages, 4599 KiB  
Article
A Novel YOLOv6 Object Detector for Monitoring Piling Behavior of Cage-Free Laying Hens
by Ramesh Bahadur Bist, Sachin Subedi, Xiao Yang and Lilong Chai
AgriEngineering 2023, 5(2), 905-923; https://doi.org/10.3390/agriengineering5020056 - 12 May 2023
Cited by 24 | Viewed by 3370
Abstract
Piling behavior (PB) is a common issue that causes negative impacts on the health, welfare, and productivity of the flock in poultry houses (e.g., cage-free layer, breeder, and broiler). Birds pile on top of each other, and the weight of the birds can [...] Read more.
Piling behavior (PB) is a common issue that causes negative impacts on the health, welfare, and productivity of the flock in poultry houses (e.g., cage-free layer, breeder, and broiler). Birds pile on top of each other, and the weight of the birds can cause physical injuries, such as bruising or suffocation, and may even result in death. In addition, PB can cause stress and anxiety in the birds, leading to reduced immune function and increased susceptibility to disease. Therefore, piling has been reported as one of the most concerning production issues in cage-free layer houses. Several strategies (e.g., adequate space, environmental enrichments, and genetic selection) have been proposed to prevent or mitigate PB in laying hens, but less scientific information is available to control it so far. The current study aimed to develop and test the performance of a novel deep-learning model for detecting PB and evaluate its effectiveness in four CF laying hen facilities. To achieve this goal, the study utilized different versions of the YOLOv6 models (e.g., YOLOv6t, YOLOv6n, YOLOv6s, YOLOv6m, YOLOv6l, and YOLOv6l relu). The objectives of this study were to develop a reliable and efficient tool for detecting PB in commercial egg-laying facilities based on deep learning and test the performance of new models in research cage-free facilities. The study used a dataset comprising 9000 images (e.g., 6300 for training, 1800 for validation, and 900 for testing). The results show that the YOLOv6l relu-PB models perform exceptionally well with high average recall (70.6%), [email protected] (98.9%), and [email protected]:0.95 (63.7%) compared to other models. In addition, detection performance increases when the camera is placed close to the PB areas. Thus, the newly developed YOLOv6l relu-PB model demonstrated superior performance in detecting PB in the given dataset compared to other tested models. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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19 pages, 4654 KiB  
Article
Development and Assessment of a Field-Programmable Gate Array (FPGA)-Based Image Processing (FIP) System for Agricultural Field Monitoring Applications
by Sabiha Shahid Antora, Young K. Chang, Tri Nguyen-Quang and Brandon Heung
AgriEngineering 2023, 5(2), 886-904; https://doi.org/10.3390/agriengineering5020055 - 11 May 2023
Cited by 5 | Viewed by 2431
Abstract
Field imagery is an effective way to capture the state of the entire field; yet, current field inspection approaches, when accounting for image resolution and processing speed, using existent imaging systems, do not always enable real-time field inspection. This project involves the innovation [...] Read more.
Field imagery is an effective way to capture the state of the entire field; yet, current field inspection approaches, when accounting for image resolution and processing speed, using existent imaging systems, do not always enable real-time field inspection. This project involves the innovation of novel technologies by using an FPGA-based image processing (FIP) device that eliminates the technical limitations of the current agricultural imaging services available in the market and will lead to the development of a market-ready service solution. The FIP prototype developed in this study was tested in both a laboratory and outdoor environment by using a digital single-lens reflex (DSLR) camera and web camera, respectively, as the reference system. The FIP system had a high accuracy with a Lin’s concordance correlation coefficient of 0.99 and 0.91 for the DLSR and web camera reference system, respectively. The proposed technology has the potential to provide on-the-spot decisions, which in turn, will improve the compatibility and sustainability of different land-based systems. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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Review

Jump to: Research

22 pages, 2519 KiB  
Review
Development Challenges of Fruit-Harvesting Robotic Arms: A Critical Review
by Abdul Kaleem, Saddam Hussain, Muhammad Aqib, Muhammad Jehanzeb Masud Cheema, Shoaib Rashid Saleem and Umar Farooq
AgriEngineering 2023, 5(4), 2216-2237; https://doi.org/10.3390/agriengineering5040136 - 17 Nov 2023
Cited by 4 | Viewed by 4973
Abstract
Promotion of research and development in advanced technology must be implemented in agriculture to increase production in the current challenging environment where the demand for manual farming is decreasing due to the unavailability of skilled labor, high cost, and shortage of labor. In [...] Read more.
Promotion of research and development in advanced technology must be implemented in agriculture to increase production in the current challenging environment where the demand for manual farming is decreasing due to the unavailability of skilled labor, high cost, and shortage of labor. In the last two decades, the demand for fruit harvester technologies, i.e., mechanized harvesting, manned and unmanned aerial systems, and robotics, has increased. However, several industries are working on the development of industrial-scale production of advanced harvesting technologies at low cost, but to date, no commercial robotic arm has been developed for selective harvesting of valuable fruits and vegetables, especially within controlled strictures, i.e., greenhouse and hydroponic contexts. This research article focused on all the parameters that are responsible for the development of automated robotic arms. A broad review of the related research works from the past two decades (2000 to 2022) is discussed, including their limitations and performance. In this study, data are obtained from various sources depending on the topic and scope of the review. Some common sources of data for writing this review paper are peer-reviewed journals, book chapters, and conference proceedings from Google Scholar. The entire requirement for a fruit harvester contains a manipulator for mechanical movement, a vision system for localizing and recognizing fruit, and an end-effector for detachment purposes. Performance, in terms of harvesting time, harvesting accuracy, and detection efficiency of several developments, has been summarized in this work. It is observed that improvement in harvesting efficiency and custom design of end-effectors is the main area of interest for researchers. The harvesting efficiency of the system is increased by the implementation of optimal techniques in its vision system that can acquire low recognition error rates. Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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