Key Technology Research and Applications of Agricultural Inspection Robots Based on Machine Vision and Artificial Intelligence

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 410

Special Issue Editors

College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Interests: microclimate analytics of poultry houses; intelligent agricultural equipment; smart farming; non-destructive detection of meat quality; agricultural robot
Special Issues, Collections and Topics in MDPI journals
1. School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
2. Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: lake-atmosphere exchange

Special Issue Information

Dear Colleagues,

In the rapidly evolving realm of modern agriculture, machine vision (MV) and artificial intelligence (AI) have emerged as pivotal technologies, revolutionizing multiple agricultural sectors. For planting, agricultural inspection robots, equipped with high-resolution cameras and sophisticated environmental sensors, leverage MV to extract plant phenotypes, automate plant and organ detection, and gather real-time environmental data. Subsequently, AI algorithms analyze these data to build models, including convolutional neural networks for identifying diseases and pests and time series models for predicting plant growth. In livestock and poultry farming, MV and AI enable the continuous monitoring of animal behaviors, health status, and growth, thus facilitating early disease detection and optimized feeding strategies. In agricultural research laboratories, MV-based visual inspection systems, coupled with AI-driven gas detection sensors, ensure safety by detecting hazards, monitoring equipment operations, and alerting researchers to abnormal gas concentrations. The integration of MV and AI in agriculture promotes intelligent automation, reduces costs, enhances productivity, and reveals significant potential for the development of smart farming.

We are delighted to announce a new Special Issue focused on the cutting-edge research and applications of MV and AI in agriculture. Our research scope covers diverse topics, such as optimizing the visual systems of agricultural inspection robots for precise plant phenotyping and early pest detection in the planting industry; utilizing robotic MV and AI for livestock behavior analysis and health management; and deploying inspection robots integrated with MV and AI gas sensors to improve safety in agricultural research laboratories. We sincerely invite original research papers and comprehensive reviews to further explore and innovate in this vibrant field.

Dr. Xiuguo Zou
Dr. Yan Qian
Dr. Yuhua Li
Dr. Yong Wang
Dr. Wentian Zhang
Guest Editors

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Keywords

  • machine vision
  • artificial intelligence
  • agricultural inspection robots
  • agricultural sensors
  • agricultural big data analytics
  • behavior recognition of livestock and poultry
  • plant phenotyping and pest detection
  • agricultural laboratory safety and gas sensing

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Published Papers (1 paper)

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Research

29 pages, 5277 KiB  
Article
DualHet-YOLO: A Dual-Backbone Heterogeneous YOLO Network for Inspection Robots to Recognize Yellow-Feathered Chicken Behavior in Floor-Raised House
by Yaobo Zhang, Linwei Chen, Hongfei Chen, Tao Liu, Jinlin Liu, Qiuhong Zhang, Mingduo Yan, Kaiyue Zhao, Shixiu Zhang and Xiuguo Zou
Agriculture 2025, 15(14), 1504; https://doi.org/10.3390/agriculture15141504 - 12 Jul 2025
Viewed by 194
Abstract
The behavior of floor-raised chickens is closely linked to their health status and environmental comfort. As a type of broiler chicken with special behaviors, understanding the daily actions of yellow-feathered chickens is crucial for accurately checking their health and improving breeding practices. Addressing [...] Read more.
The behavior of floor-raised chickens is closely linked to their health status and environmental comfort. As a type of broiler chicken with special behaviors, understanding the daily actions of yellow-feathered chickens is crucial for accurately checking their health and improving breeding practices. Addressing the challenges of high computational complexity and insufficient detection accuracy in existing floor-raised chicken behavior recognition models, a lightweight behavior recognition model was proposed for floor-raised yellow-feathered chickens, based on a Dual-Backbone Heterogeneous YOLO Network. Firstly, DualHet-YOLO enhances the feature extraction capability of floor-raised chicken images through a dual-path feature map extraction architecture and optimizes the localization and classification of multi-scale targets using a TriAxis Unified Detection Head. Secondly, a Proportional Scale IoU loss function is introduced that improves regression accuracy. Finally, a lightweight structure Eff-HetKConv was designed, significantly reducing model parameters and computational complexity. Experiments on a private floor-raised chicken behavior dataset show that, compared with the baseline YOLOv11 model, the DualHet-YOLO model increases the mAP for recognizing five behaviors—pecking, resting, walking, dead, and inactive—from 77.5% to 84.1%. Meanwhile, it reduces model parameters by 14.6% and computational complexity by 29.2%, achieving a synergistic optimization of accuracy and efficiency. This approach provides an effective solution for lightweight object detection in poultry behavior recognition. Full article
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