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Electronics
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Applications of Artificial Intelligence(AI) in Agriculture
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Applications of Artificial Intelligence(AI) in Agriculture

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 February 2025 | Viewed by 4515

Special Issue Editors

Dr. Dong Chen

E-Mail Website
Guest Editor
Environmental Institute, University of Virginia, Charlottesville, VA 23450, USA
Interests: reinforcement learning; deep learning; computer vision; smart agriculture
Prof. Dr. Yanbo Huang

E-Mail Website
Guest Editor
Genetics and Sustainable Agriculture Research Unit, U.S. Department of Agriculture–Agricultural Research Service, Mississippi State, MS 39762, USA
Interests: process modeling; optimization and control; remote sensing; artificial intelligence; machine learning; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Lirong Xiang

E-Mail Website
Guest Editor
Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, USA
Interests: agricultural robotics; plant phenotyping; machine vision; digital twin; automation
Special Issues, Collections and Topics in MDPI journals
Dr. Nitin Rai

E-Mail Website
Guest Editor
Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
Interests: artificial intelligence; precision agriculture; remote sensing; site-specific application
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In an era in which technological advancements are reshaping agricultural applications, machine learning and artificial intelligence have emerged as pivotal catalysts for transformation. Integrating these cutting-edge technologies into agriculture can revolutionize how we approach farming, from enhancing productivity and precision to ensuring sustainability and resilience in the face of climate change. This Special Issue on "Applications of AI in Agriculture" aims to explore the transformative impact of AI on modern agricultural practices, highlighting innovative research and applications that address pressing challenges and promote sustainable farming practices.

The primary focus of this Special Issue is on the application of AI across various agricultural domains. We aim to cover a broad spectrum of topics, including but not limited to precision farming, crop management, pest and disease detection, soil health monitoring, livestock management, and smart irrigation systems. By bringing together cutting-edge research, we intend to showcase how AI-driven solutions can optimize agricultural practices, enhance productivity, and reduce environmental impact.

This Special Issue invites original research articles, reviews, and case studies that demonstrate the practical implementation of AI technologies in agriculture. We encourage submissions that cover a wide range of AI methodologies, including machine learning, deep learning, computer vision, natural language processing, and robotics. Studies that integrate AI with other emerging technologies, such as the Internet of Things (IoT), remote sensing, and big data analytics, are particularly welcome. Our goal is to provide a comprehensive overview of the current state of AI applications in agriculture and to identify future research directions and challenges.

Topics of interest include, but are not limited to, the following:

  • Precision farming techniques leveraging AI for real-time decision making;
  • AI-based crop management systems for optimizing yield and resource use;
  • Pest and disease detection using machine learning and computer vision;
  • Soil health monitoring through AI-driven data analytics;
  • Livestock management solutions utilizing AI for health and productivity;
  • Smart irrigation systems powered by AI for efficient water use;
  • Integration of AI with IoT for enhanced farm management;
  • Remote sensing applications in agriculture using AI;
  • Big data analytics for predictive modeling and decision support in farming;
  • Robotics and automation in agricultural operations;
  • AI-driven solutions for sustainable and climate-resilient agriculture.

Technical Program Committee Member:
Name: Ms. Jiajia Li
Email: [email protected]
Affiliation: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
Research Interests: smart agriculture; computer vision; deep learning; machine learning

Dr. Dong Chen
Dr. Yanbo Huang
Dr. Lirong Xiang
Dr. Nitin Rai
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • artificial intelligence
  • deep learning
  • smart agriculture
  • big data
  • machine learning
  • decision support systems
  • computer vision
  • AI and deep learning in agriculture digital agriculture solutions for soil health and water quality
  • agricultural robotics
  • edge computing and cloud solutions
  • precision agriculture and global food security
  • weather and models for precision agriculture
  • precision crop protection small holders and precision agriculture
  • precision dairy and livestock management
  • precision farming
  • digital agriculture
  • crop management
  • weed management
  • fruit detection
  • object detection
  • classification
  • image segmentation
  • large language models
  • foundation models

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Published Papers (4 papers)

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Research

24 pages, 6728 KiB  
Article
Energy-Efficient Deployment of Laser Illumination for Rotating Vertical Farms
by Tian Liu, Yunxiang Ye, Shiyi Tan, Xianglei Xue, Hang Zheng, Ning Ren, Shuai Shen and Guohong Yu
Electronics 2025, 14(3), 445; https://doi.org/10.3390/electronics14030445 - 23 Jan 2025
Viewed by 375
Abstract
As the global population grows, vertical farming offers a promising solution by using vertically stacked shelves in controlled environments to grow crops efficiently within urban areas. However, the shading effects of farm structures make artificial lighting a significant cost, accounting for approximately [...] Read more.
As the global population grows, vertical farming offers a promising solution by using vertically stacked shelves in controlled environments to grow crops efficiently within urban areas. However, the shading effects of farm structures make artificial lighting a significant cost, accounting for approximately 67% of total operational expenses. This study presents a novel approach to optimizing the deployment of laser illumination in rotating vertical farms by incorporating structural design, light modeling, and photosynthesis. By theoretically analyzing the beam pattern of laser diodes and the dynamics in the coverage area of rotating farm layers, we accurately characterize the light conditions on each vertical layer. Based on these insights, we introduce a new criterion, cumulative coverage, which accounts for both light intensity and coverage area. Then, an optimization framework is formulated, and a swarm intelligence algorithm, Differential Evolution (DE) is used to solve the optimization while considering the structural and operational constraints. It is found that tilting lights and placing them slightly off-center are more effective than traditional vertically aligned and center-aligned deployment. Our results show that the proposed strategy improves light coverage by 4% compared to the intensity-only optimization approach, and by 10% compared to empirical methods. This study establishes the first theoretical framework for designing energy-efficient artificial lighting deployment strategies, providing insights into enhancing the efficiency of vertical farming systems. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence(AI) in Agriculture)
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20 pages, 1446 KiB  
Article
Integrating Artificial Intelligence in the Sustainable Development of Agriculture: Applications and Challenges in the Resource-Based Theory Approach
by Monica Aureliana Petcu, Maria-Iulia Sobolevschi-David, Stefania Cristina Curea and Dumitru Florin Moise
Electronics 2024, 13(23), 4580; https://doi.org/10.3390/electronics13234580 - 21 Nov 2024
Viewed by 1360
Abstract
In the electronics sector, artificial intelligence (AI) has grown into a disruptive force that is changing how humans engage with technology and creating new opportunities. AI is expanding the capabilities of electronic devices, granting them higher intelligence, increased intuitiveness, and the ability to [...] Read more.
In the electronics sector, artificial intelligence (AI) has grown into a disruptive force that is changing how humans engage with technology and creating new opportunities. AI is expanding the capabilities of electronic devices, granting them higher intelligence, increased intuitiveness, and the ability to comprehend and react to human behavior. The purpose of this approach is to highlight the knowledge structure in artificial intelligence application in agriculture and its challenges within the European Union. A bibliometric analysis was conducted, distinguishing the following items as the main research themes: agriculture 4.0; advanced monitoring and controlling strategies in intelligent agriculture; the automation of agriculture by including practices such as cloud computing, Internet of Things (IoT), big data, blockchain, robotics and AI, information security; new skills, and responsible leadership. The regression analysis revealed that the employers’ assumption of responsibility, by ensuring opportunities for training and development of digital skills, determines the growth of added value (0.013) and its rate (0.0003). Enhancing labor productivity depends on Internet access for the integration of technologies based on artificial intelligence (1.343). An increasing employment rate of low-skilled people affects agricultural production (0.0127). The contributions of this two-dimensional approach consist in supporting the integration of digital technology in agriculture as a condition for achieving the goals of sustainable development. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence(AI) in Agriculture)
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24 pages, 4919 KiB  
Article
Unmanned Agricultural Machine Operation System in Farmland Based on Improved Fuzzy Adaptive Priority-Driven Control Algorithm
by Yinchao Che, Guang Zheng, Yong Li, Xianghui Hui and Yang Li
Electronics 2024, 13(20), 4141; https://doi.org/10.3390/electronics13204141 - 21 Oct 2024
Viewed by 1136
Abstract
Autonomous driving technology for agricultural machinery can maximise crop yield, reduce labour costs, and alleviate labour intensity. In response to the current low degree of automation and low tracking accuracy of driving paths in agricultural equipment, this research proposes an unmanned agricultural machinery [...] Read more.
Autonomous driving technology for agricultural machinery can maximise crop yield, reduce labour costs, and alleviate labour intensity. In response to the current low degree of automation and low tracking accuracy of driving paths in agricultural equipment, this research proposes an unmanned agricultural machinery operating system based on an improved fuzzy adaptive PD control algorithm. Firstly, mechanical kinematic models and fuzzy adaptive control algorithms are introduced to achieve autonomous driving, and parameter settings and speed adjustments are made while considering errors. Secondly, in the autonomous driving operation system, taking a certain rice machine as an example, perception information, trajectory design, dynamic control, operation supervision, and remote control design are carried out. The experimental results show that the improved fuzzy algorithm exhibits smaller deviation results in driving path tracking, with an average error between the actual path and the expected path of less than 0.001 m. In different testing scenarios, compared with the actual control results, the maximum deviation of the control system platform in straight sections is less than 2.8 m, which is more stable. More than 95% of the lateral deviation results in the road sections are within 0.11 m. And the tracking distance error of the proposed method in the straight and curved segments is relatively small, far smaller than other comparative algorithms. The unmanned agricultural machinery operation system proposed in this study can significantly improve the efficiency and accuracy of agricultural machinery work, promote the development of intelligent and modern agricultural machinery, and provide reference value and important contributions to social and economic development as well as the progress and promotion of related technologies. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence(AI) in Agriculture)
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16 pages, 3335 KiB  
Article
Lightweight and Optimized Multi-Label Fruit Image Classification: A Combined Approach of Knowledge Distillation and Image Enhancement
by Juce Zhang, Yao Lu, Yi Guo, Chengkai Wu, Hengjun Liu, Zhuoyi Yu and Jiayi Zhou
Electronics 2024, 13(16), 3267; https://doi.org/10.3390/electronics13163267 - 17 Aug 2024
Viewed by 833
Abstract
In our research, we aimed to address the shortcomings of traditional fruit image classification models, which struggle with inconsistent lighting, complex backgrounds, and high computational demands. To overcome these challenges, we developed a novel multi-label classification method incorporating advanced image preprocessing techniques, such [...] Read more.
In our research, we aimed to address the shortcomings of traditional fruit image classification models, which struggle with inconsistent lighting, complex backgrounds, and high computational demands. To overcome these challenges, we developed a novel multi-label classification method incorporating advanced image preprocessing techniques, such as Contrast Limited Adaptive Histogram Equalization and the Gray World algorithm, which enhance image quality and color balance. Utilizing lightweight encoder–decoder architectures, specifically MobileNet, DenseNet, and EfficientNet, optimized with an Asymmetric Binary Cross-Entropy Loss function, we improved model performance in handling diverse sample difficulties. Furthermore, Multi-Label Knowledge Distillation (MLKD) was implemented to transfer knowledge from large, complex teacher models to smaller, efficient student models, thereby reducing computational complexity without compromising accuracy. Experimental results on the DeepFruit dataset, which includes 21,122 images of 20 fruit categories, demonstrated that our method achieved a peak mean Average Precision (mAP) of 90.2% using EfficientNet-B3, with a computational cost of 7.9 GFLOPs. Ablation studies confirmed that the integration of image preprocessing, optimized loss functions, and knowledge distillation significantly enhances performance compared to the baseline models. This innovative method offers a practical solution for real-time fruit classification on resource-constrained devices, thereby supporting advancements in smart agriculture and the food industry. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence(AI) in Agriculture)
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