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Information Technologies and Artificial Intelligence in Smart Agriculture
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Information Technologies and Artificial Intelligence in Smart Agriculture

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 April 2026) | Viewed by 3038

Editors

Dr. Zeng Hu

E-Mail Website
Guest Editor
College of Artificial Intelligence, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Interests: agriculture information technology; machine learning; MIMO; index modulation and signal processing for data transmission
Dr. Mingyue Cheng

E-Mail Website
Guest Editor
College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Interests: agriculture information technology; artificial intelligence; underwater acoustic communication; networking
Dr. Xuan Chen

E-Mail Website
Guest Editor
School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
Interests: wireless and molecular communications; machine learning; index modulation
Dr. Yu Huang

E-Mail Website
Guest Editor
Research Center of Intelligent Communication Engineering, School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
Interests: molecular communications; wireless communications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Miaowen Wen

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China
Interests: index modulation; OTFS; OFDM; non-orthogonal multiple access; mobile edge computing; physical-layer security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture is a highly uncertain and complex field due to the changes in environmental factors during the farming processes. With the rapid development of information technologies and artificial intelligence (AI), agriculture is undergoing a significant revolution from traditional agriculture to smart agriculture. In recent years, AI, Internet of Things (IoT), wireless communication, cloud computing, machine learning, big data and other innovative information technologies are accelerating the development of smart agriculture, which greatly reduce the cost of various kinds of data acquisition in agricultural scenarios and accelerate the popularization and development of smart agriculture, also promoting the use of robotics in agriculture.

AI is making a huge impact on all industries through its perception, reasoning and learning capabilities. With the aid of IoT and sensors, many long-lasting challenges in agriculture that might be better resolved by the integration of AI, such as animal/crop growth monitoring, pest and disease identification and control, farming management, quality evaluation, etc. In the future, for plant production or poultry farming, AI and robotics can be deeply integrated into processes to develop intelligent agricultural systems and unmanned farms.

This Special Issue aims to explore the frontiers of the groundbreaking advances, real-world applications, and critical challenges. Applications of information technologies and AI in smart agriculture. By encouraging interdisciplinary research and collaboration, research areas may include (but are not limited to) the following:

  • AI-driven smart farming
  • Internet of Things (IoT)
  • Machine learning in intelligent decision
  • Sensors networks in agriculture
  • Wireless data transmission
  • Big data and data mining
  • Data security in transmission
  • Data-driven decision making in agriculture
  • Application analysis

Dr. Zeng Hu
Dr. Mingyue Cheng
Dr. Xuan Chen
Dr. Yu Huang
Prof. Dr. Miaowen Wen
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 (AI)
  • Internet of Things (IoT)
  • machine learning
  • smart agriculture
  • sensors networks
  • wireless communication

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Published Papers (3 papers)

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Research

27 pages, 9745 KB  
Article
A Novel Water-Flow Live-Insect Monitoring Device for Measuring the Light-Trap Attraction Rate of Insects
by Jiarui Fang, Lei Shu, Ru Han, Kailiang Li and Wei Lin
Electronics 2026, 15(3), 714; https://doi.org/10.3390/electronics15030714 - 6 Feb 2026
Viewed by 812
Abstract
The light-trap attraction rate (LTARI) is an important metric for characterizing diel activity patterns and supports studies in insect behavioral ecology and pest management. However, conventional automatic light-trap devices often rely on lethal methods (e.g., high-voltage grids or infrared heating), causing high mortality [...] Read more.
The light-trap attraction rate (LTARI) is an important metric for characterizing diel activity patterns and supports studies in insect behavioral ecology and pest management. However, conventional automatic light-trap devices often rely on lethal methods (e.g., high-voltage grids or infrared heating), causing high mortality of non-target insects and severe image obstruction due to stacking of insect bodies. These issues disturb natural populations and bias attempts to quantify LTARI. Our primary objective is to develop and evaluate a non-lethal monitoring system as a methodological basis for future LTARI research, rather than to provide head-to-head quantitative comparisons with conventional traps. To address the above limitations, we propose a live-insect monitoring instrument that integrates a wind-suction trap with a Water-Flow Dispersion and Transport Structure (WF-DTS). The non-destructive trapping–dispersion–release process limits body stacking, allows captured insects to be released, and yields a community-level post-capture survival rate of 94% under the conditions tested. Experimental results show that the prototype maintains image integrity with clearly isolated single insects and achieves a detection performance of 95.6% (mAP@0.5) using the YOLOv8s model. At the inference stage, only the standard resizing and normalization operations of YOLOv8s are applied, without additional denoising, background subtraction, or data augmentation. These observations suggest that the WF-DTS generates images that are easier to segment and classify than those from conventional devices. The high detection accuracy is largely attributable to the physical dispersion of specimens and the uniform white matte background provided by the hardware design. Overall, the system constitutes a non-lethal hardware–software platform that may reduce backend processing complexity and provide a methodological basis for more accurate LTARI estimation in future, dedicated field studies. Full article
(This article belongs to the Special Issue Information Technologies and Artificial Intelligence in Smart Agriculture)
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24 pages, 3808 KB  
Article
CSOOC: Communication-State Driven Online–Offline Coordination Strategy for UAV Swarm Multi-Target Tracking
by Haoran Sun, Yicheng Yan, Guojie Liu, Ying Zhan and Xianfeng Li
Electronics 2025, 14(23), 4743; https://doi.org/10.3390/electronics14234743 - 2 Dec 2025
Cited by 1 | Viewed by 880
Abstract
Unmanned aerial vehicle (UAV) swarms have shown great potential in large-scale IoT (Internet of Things) and smart agriculture applications, particularly for cooperative monitoring and multi-target tracking in field environments. However, most existing coordination strategies assume ideal communication conditions, overlooking realistic network impairments such [...] Read more.
Unmanned aerial vehicle (UAV) swarms have shown great potential in large-scale IoT (Internet of Things) and smart agriculture applications, particularly for cooperative monitoring and multi-target tracking in field environments. However, most existing coordination strategies assume ideal communication conditions, overlooking realistic network impairments such as congestion, packet loss, and latency. These impairments disrupt the timely exchange of information between UAVs and the ground base station, leading to delayed or lost control signals. As a result, coordination quality deteriorates and tracking performance is severely degraded in real-world deployments. To address this gap, we propose CSOOC (Communication-State Driven Online–Offline Coordination with Congestion Control), a hybrid control architecture that integrates centralized learning-based decision-making with decentralized rule-based policies to adapt UAV behaviors according to real-time network states. CSOOC consists of three key components: (1) an online module that enables centralized coordination under reliable communication, (2) an offline profit-driven mobility strategy based on local Gaussian maps for autonomous target tracking during communication loss, and (3) a congestion control mechanism based on STAR(Stratified Transmission and RTS/CTS), which combines temporal transmission desynchronization and RTS/CTS handshaking to enhance uplink reliability. We establish a unified co-simulation paradigm that connects network communication with swarm control and swarm coordination behavior. Experiments demonstrate that CSOOC achieves an average observation rate of 39.7%, surpassing baseline algorithms by 4.4–11.13%, while simultaneously improving network stability through significantly higher packet delivery ratios under congested conditions. These results demonstrate that CSOOC effectively bridges the gap between algorithmic performance in simulation and practical UAV swarm operations in communication-constrained environments. Full article
(This article belongs to the Special Issue Information Technologies and Artificial Intelligence in Smart Agriculture)
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27 pages, 24006 KB  
Article
RDT-YOLO: An Improved Lightweight Model for Fish Maw Authenticity Detection
by Caijian Xie, Mingguang Liu, Wanzhen Zhang, Yuting Zhang, Shahbaz Gul Hassan, Weijie Guo, Tonglai Liu, Shuangyin Liu and Xuekai Gao
Electronics 2025, 14(23), 4588; https://doi.org/10.3390/electronics14234588 - 23 Nov 2025
Viewed by 846
Abstract
With the rapid expansion of the global fish maw industry, the increasing prevalence of counterfeit products has made authenticity detection a critical challenge. Traditional detection methods rely on organoleptic assessment, chemical analysis, or molecular techniques, which limits their practical application. This paper treats [...] Read more.
With the rapid expansion of the global fish maw industry, the increasing prevalence of counterfeit products has made authenticity detection a critical challenge. Traditional detection methods rely on organoleptic assessment, chemical analysis, or molecular techniques, which limits their practical application. This paper treats fish maw authenticity detection as an object detection problem and proposes RDT-YOLO, a lightweight detection algorithm based on YOLO11n. Specifically, to address the challenges of insufficient fine texture feature extraction and computational redundancy in fish maw detection, we design hierarchical reparameterized feature extraction modules that utilize reparameterization technology to enhance texture feature extraction capability at different scales. To mitigate information loss during multi-scale feature fusion, we develop a Dynamic Adaptive Multi-Scale Pyramid Processing (DAMSPP) module that incorporates dynamic convolution mechanisms for adaptive feature aggregation. Additionally, we propose an Adaptive Task-Aligned Detection Head (ATADH) that combines task interaction and shared convolution to reduce model parameters while improving detection accuracy. Furthermore, a Wise-ShapeIoU loss function is introduced by incorporating a focusing coefficient into Shape-IoU, enhancing model detection performance through improved bounding box shape optimization. Experimental validation demonstrates that RDT-YOLO achieves 91.9% precision, 89.6% recall, and 94% mAP@0.5 while reducing parameters, model size, and computational complexity by 75.6%, 73.8%, and 63.8%, respectively, compared to YOLO11s. When evaluated against YOLOv10s and YOLOv12s, RDT-YOLO shows mAP@0.5 improvements of 0.8% and 0.5%, respectively. This work provides an automated solution for fish maw authenticity detection with potential for broader food safety applications. Full article
(This article belongs to the Special Issue Information Technologies and Artificial Intelligence in Smart Agriculture)
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