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Disease Detection and Identification of Rice Leaf Based on Improved Detection Transformer

Author

Listed:
  • Hua Yang

    (School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430040, China
    These authors contributed equally to this work.)

  • Xingquan Deng

    (School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430040, China
    These authors contributed equally to this work.)

  • Hao Shen

    (Baijuncheng Technology Co., Ltd., Wuhan 434000, China)

  • Qingfeng Lei

    (School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430040, China)

  • Shuxiang Zhang

    (School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430040, China)

  • Neng Liu

    (School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430040, China)

Abstract

In recent years, the domain of diagnosing plant afflictions has predominantly relied upon the utilization of deep learning techniques for classifying images of diseased specimens; however, these classification algorithms remain insufficient for instances where a single plant exhibits multiple ailments. Consequently, we view the region afflicted by the malady of rice leaves as a minuscule issue of target detection, and then avail ourselves of a computational approach to vision to identify the affected area. In this paper, we advance a proposal for a Dense Higher-Level Composition Feature Pyramid Network (DHLC-FPN) that is integrated into the Detection Transformer (DETR) algorithm, thereby proffering a novel Dense Higher-Level Composition Detection Transformer (DHLC-DETR) methodology which can effectively detect three diseases: sheath blight, rice blast, and flax spot. Initially, the proposed DHLC-FPN is utilized to supersede the backbone network of DETR through amalgamation with Res2Net, thus forming a feature extraction network. Res2Net then extracts five feature scales, which are coalesced through the deployment of high-density rank hybrid sampling by the DHLC-FPN architecture. The fused features, in concert with the location encoding, are then fed into the transformer to produce predictions of classes and prediction boxes. Lastly, the prediction classes and the prediction boxes are subjected to binary matching through the application of the Hungarian algorithm. On the IDADP datasets, the DHLC-DETR model, through the utilization of data enhancement, elevated mean Average Precision (mAP) by 17.3% in comparison to the DETR model. Additionally, mAP for small target detection was improved by 9.5%, and the magnitude of hyperparameters was reduced by 324.9 M. The empirical outcomes demonstrate that the optimized structure for feature extraction can significantly enhance the average detection accuracy and small target detection accuracy of the model, achieving an average accuracy of 97.44% on the IDADP rice disease dataset.

Suggested Citation

  • Hua Yang & Xingquan Deng & Hao Shen & Qingfeng Lei & Shuxiang Zhang & Neng Liu, 2023. "Disease Detection and Identification of Rice Leaf Based on Improved Detection Transformer," Agriculture, MDPI, vol. 13(7), pages 1-17, July.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:7:p:1361-:d:1189205
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    References listed on IDEAS

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    1. Zhu, Qiannan & Jiang, Feng & Li, Chaoshun, 2023. "Time-varying interval prediction and decision-making for short-term wind power using convolutional gated recurrent unit and multi-objective elephant clan optimization," Energy, Elsevier, vol. 271(C).
    2. Jianwen Shi & Xiuyun Yang, 2022. "Sustainable Development Levels and Influence Factors in Rural China Based on Rural Revitalization Strategy," Sustainability, MDPI, vol. 14(14), pages 1-19, July.
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