Implementation and Evaluation of Attention Aggregation Technique for Pear Disease Detection

Tong Hai, Ningyi Zhang, Xiaoyi Lu, Jiping Xu, Xinliang Wang, Jiewei Hu, Mengxue Ji, Zijia Zhao, Jingshun Wang () and Min Dong ()
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Tong Hai: China Agricultural University, Beijing 100083, China
Ningyi Zhang: China Agricultural University, Beijing 100083, China
Xiaoyi Lu: China Agricultural University, Beijing 100083, China
Jiping Xu: China Agricultural University, Beijing 100083, China
Xinliang Wang: China Agricultural University, Beijing 100083, China
Jiewei Hu: China Agricultural University, Beijing 100083, China
Mengxue Ji: China Agricultural University, Beijing 100083, China
Zijia Zhao: China Agricultural University, Beijing 100083, China
Jingshun Wang: China Agricultural University, Beijing 100083, China
Min Dong: China Agricultural University, Beijing 100083, China

Agriculture, 2024, vol. 14, issue 7, 1-27

Abstract: In this study, a novel approach integrating multimodal data processing and attention aggregation techniques is proposed for pear tree disease detection. The focus of the research is to enhance the accuracy and efficiency of disease detection by fusing data from diverse sources, including images and environmental sensors. The experimental results demonstrate that the proposed method outperforms in key performance metrics such as precision, recall, accuracy, and F1-Score. Specifically, the model was tested on the Kaggle dataset and compared with existing advanced models such as RetinaNet, EfficientDet, Detection Transformer (DETR), and the You Only Look Once (YOLO) series. The experimental outcomes indicate that the proposed model achieves a precision of 0.93, a recall of 0.90, an accuracy of 0.92, and an F1-Score of 0.91, surpassing those of the comparative models. Additionally, detailed ablation experiments were conducted on the multimodal weighting module and the dynamic regression loss function to verify their specific contributions to the model performance. These experiments not only validated the effectiveness of the proposed method but also demonstrate its potential application in pear tree disease detection. Through this research, an effective technological solution is provided for the agricultural disease detection domain, offering substantial practical value and broad application prospects.

Keywords: multimodal data processing; attention aggregation techniques; dynamic regression loss; AI in agriculture; deep learning for precision agriculture (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 complete reference list from CitEc
Citations: View citations in EconPapers (1)

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