Agriculture (Jul 2024)

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,
  • Min Dong

DOI
https://doi.org/10.3390/agriculture14071146
Journal volume & issue
Vol. 14, no. 7
p. 1146

Abstract

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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.

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