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 March 2024 | Viewed by 11970

Special Issue Editors

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
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
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
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
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

Published Papers (8 papers)

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Research

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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 - 04 Mar 2024
Viewed by 333
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 - 09 Feb 2024
Viewed by 750
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 - 01 Dec 2023
Cited by 2 | Viewed by 1193
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
Viewed by 854
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 2 | Viewed by 1763
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 6 | Viewed by 2331
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%), mAP@0.50 (98.9%), and mAP@0.50:0.95 (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 3 | Viewed by 1664
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

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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 1 | Viewed by 2000
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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