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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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Qing Dong, Lina Sun, Tianxin Han, Minqi Cai and Ce Gao
Timely and effective pest detection is essential for agricultural production, facing challenges such as complex backgrounds and a vast number of parameters. Seeking solutions has become a pressing matter. This paper, based on the YOLOv5 algorithm, develo...
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Wenji Yang and Xiaoying Qiu
The damage caused by pests to crops results in reduced crop yield and compromised quality. Accurate and timely pest detection plays a crucial role in helping farmers to defend against and control pests. In this paper, a novel crop pest detection model na...
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Zhichao Chen, Hongping Zhou, Haifeng Lin and Di Bai
The tea industry, as one of the most globally important agricultural products, is characterized by pests and diseases that pose a serious threat to yield and quality. These diseases and pests often present different scales and morphologies, and some pest...
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Charalampos S. Kouzinopoulos, Eleftheria Maria Pechlivani, Nikolaos Giakoumoglou, Alexios Papaioannou, Sotirios Pemas, Panagiotis Christakakis, Dimosthenis Ioannidis and Dimitrios Tzovaras
Citizen science reinforces the development of emergent tools for the surveillance, monitoring, and early detection of biological invasions, enhancing biosecurity resilience. The contribution of farmers and farm citizens is vital, as volunteers can streng...
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Ruixue Zhu, Fengqi Hao and Dexin Ma
Object detection in deep learning provides a viable solution for detecting crop-pest-infected regions. However, existing rectangle-based object detection methods are insufficient to accurately detect the shape of pest-infected regions. In addition, the m...
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Jizhong Deng, Chang Yang, Kanghua Huang, Luocheng Lei, Jiahang Ye, Wen Zeng, Jianling Zhang, Yubin Lan and Yali Zhang
The realization that mobile phones can detect rice diseases and insect pests not only solves the problems of low efficiency and poor accuracy from manually detection and reporting, but it also helps farmers detect and control them in the field in a timel...
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Tibor József, Szonja Réka Kiss, Ferenc Muzslay, Orsolya Máté, Gábor P. Stromájer and Tímea Stromájer-Rácz
Active substances detected in surface water in Hungary today include pain and anti-inflammatory agents and antiepileptics, as wastewater treatment mechanisms cannot remove these micropollutants. The aim of our research is to detect residues of four pain-...
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Zijia Yang, Hailin Feng, Yaoping Ruan and Xiang Weng
Timely and accurate identification of tea tree pests is critical for effective tea tree pest control. We collected image data sets of eight common tea tree pests to accurately represent the true appearance of various aspects of tea tree pests. The datase...
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Yuzhe Bai, Fengjun Hou, Xinyuan Fan, Weifan Lin, Jinghan Lu, Junyu Zhou, Dongchen Fan and Lin Li
With the widespread application of drone technology, the demand for pest detection and identification from low-resolution and noisy images captured with drones has been steadily increasing. In this study, a lightweight pest identification model based on ...
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