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Zhe Yin, Mingkang Peng, Zhaodong Guo, Yue Zhao, Yaoyu Li, Wuping Zhang, Fuzhong Li and Xiaohong Guo
With the advancement of machine vision technology, pig face recognition has garnered significant attention as a key component in the establishment of precision breeding models. In order to explore non-contact individual pig recognition, this study propos...
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Ruicheng Gao, Zhancai Dong, Yuqi Wang, Zhuowen Cui, Muyang Ye, Bowen Dong, Yuchun Lu, Xuaner Wang, Yihong Song and Shuo Yan
In this study, a deep-learning-based intelligent detection model was designed and implemented to rapidly detect cotton pests and diseases. The model integrates cutting-edge Transformer technology and knowledge graphs, effectively enhancing pest and disea...
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Zhendong He, Wenbin Yang, Yanjie Liu, Anping Zheng, Jie Liu, Taishan Lou and Jie Zhang
Ensuring the safety of transmission lines necessitates effective insulator defect detection. Traditional methods often need more efficiency and accuracy, particularly for tiny defects. This paper proposes an innovative insulator defect recognition method...
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Xiang Sun, Yisheng Miao, Xiaoyan Wu, Yuansheng Wang, Qingxue Li, Huaji Zhu and Huarui Wu
To enhance the transplantation effectiveness of vegetables and promptly formulate subsequent work strategies, it is imperative to study the recognition approach for transplanted seedlings. In the natural and complex environment, factors like background a...
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Bo Zhao, Qifan Zhang, Yangchun Liu, Yongzhi Cui and Baixue Zhou
In response to the need for precision and intelligence in the assessment of transplanting machine operation quality, this study addresses challenges such as low accuracy and efficiency associated with manual observation and random field sampling for the ...
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Hao Gu, Ming Chen and Dongmei Gan
The identification of gender in Chinese mitten crab juveniles is a critical prerequisite for the automatic classification of these crab juveniles. Aiming at the problem that crab juveniles are of different sizes and relatively small, with unclear male an...
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Rujia Li, Yadong Li, Weibo Qin, Arzlan Abbas, Shuang Li, Rongbiao Ji, Yehui Wu, Yiting He and Jianping Yang
This research tackles the intricate challenges of detecting densely distributed maize leaf diseases and the constraints inherent in YOLO-based detection algorithms. It introduces the GhostNet_Triplet_YOLOv8s algorithm, enhancing YOLO v8s by integrating t...
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Li Sun, Jingfa Yao, Hongbo Cao, Haijiang Chen and Guifa Teng
In agricultural production, rapid and accurate detection of peach blossom bloom plays a crucial role in yield prediction, and is the foundation for automatic thinning. The currently available manual operation-based detection and counting methods are extr...
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Hailiang Gong, Xi Wang and Weidong Zhuang
This study focuses on real-time detection of maize crop rows using deep learning technology to meet the needs of autonomous navigation for weed removal during the maize seedling stage. Crop row recognition is affected by natural factors such as soil expo...
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Fei Huang, Yanming Li, Zixiang Liu, Liang Gong and Chengliang Liu
The leaf area of pak choi is a critical indicator of growth rate, nutrient absorption, and photosynthetic efficiency, and it is required to be precisely measured for an optimal agricultural output. Traditional methods often fail to deliver the necessary ...
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