Intelligent Cotton Pest and Disease Detection: Edge Computing Solutions with Transformer Technology and Knowledge Graphs

Ruicheng Gao, Zhancai Dong, Yuqi Wang, Zhuowen Cui, Muyang Ye, Bowen Dong, Yuchun Lu, Xuaner Wang, Yihong Song and Shuo Yan ()
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Ruicheng Gao: China Agricultural University, Beijing 100083, China
Zhancai Dong: China Agricultural University, Beijing 100083, China
Yuqi Wang: China Agricultural University, Beijing 100083, China
Zhuowen Cui: China Agricultural University, Beijing 100083, China
Muyang Ye: China Agricultural University, Beijing 100083, China
Bowen Dong: China Agricultural University, Beijing 100083, China
Yuchun Lu: China Agricultural University, Beijing 100083, China
Xuaner Wang: China Agricultural University, Beijing 100083, China
Yihong Song: China Agricultural University, Beijing 100083, China
Shuo Yan: China Agricultural University, Beijing 100083, China

Agriculture, 2024, vol. 14, issue 2, 1-27

Abstract: 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 disease feature recognition precision. With the application of edge computing technology, efficient data processing and inference analysis on mobile platforms are facilitated. Experimental results indicate that the proposed method achieved an accuracy rate of 0.94, a mean average precision (mAP) of 0.95, and frames per second (FPS) of 49.7. Compared with existing advanced models such as YOLOv8 and RetinaNet, improvements in accuracy range from 3% to 13% and in mAP from 4% to 14%, and a significant increase in processing speed was noted, ensuring rapid response capability in practical applications. Future research directions are committed to expanding the diversity and scale of datasets, optimizing the efficiency of computing resource utilization and enhancing the inference speed of the model across various devices. Furthermore, integrating environmental sensor data, such as temperature and humidity, is being considered to construct a more comprehensive and precise intelligent pest and disease detection system.

Keywords: cotton pest detection; edge computing in farming; object detection; mobile application; dataset augmentation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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