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An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation

Author

Listed:
  • Shuo Chen

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Kefei Zhang

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
    Satellite Positioning for Atmosphere, Climate and Environment (SPACE) Research Center, RMIT University, Melbourne, VIC 3001, Australia)

  • Yindi Zhao

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Yaqin Sun

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Wei Ban

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Yu Chen

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Huifu Zhuang

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Xuewei Zhang

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Jinxiang Liu

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Tao Yang

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Rice bacterial leaf streak (BLS) is a serious disease in rice leaves and can seriously affect the quality and quantity of rice growth. Automatic estimation of disease severity is a crucial requirement in agricultural production. To address this, a new method (termed BLSNet) was proposed for rice and BLS leaf lesion recognition and segmentation based on a UNet network in semantic segmentation. An attention mechanism and multi-scale extraction integration were used in BLSNet to improve the accuracy of lesion segmentation. We compared the performance of the proposed network with that of DeepLabv3+ and UNet as benchmark models used in semantic segmentation. It was found that the proposed BLSNet model demonstrated higher segmentation and class accuracy. A preliminary investigation of BLS disease severity estimation was carried out based on our BLS segmentation results, and it was found that the proposed BLSNet method has strong potential to be a reliable automatic estimator of BLS disease severity.

Suggested Citation

  • Shuo Chen & Kefei Zhang & Yindi Zhao & Yaqin Sun & Wei Ban & Yu Chen & Huifu Zhuang & Xuewei Zhang & Jinxiang Liu & Tao Yang, 2021. "An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation," Agriculture, MDPI, vol. 11(5), pages 1-18, May.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:5:p:420-:d:549875
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    References listed on IDEAS

    as
    1. Helin Yin & Yeong Hyeon Gu & Chang-Jin Park & Jong-Han Park & Seong Joon Yoo, 2020. "Transfer Learning-Based Search Model for Hot Pepper Diseases and Pests," Agriculture, MDPI, vol. 10(10), pages 1-16, September.
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    Cited by:

    1. Rutuja Rajendra Patil & Sumit Kumar & Shwetambari Chiwhane & Ruchi Rani & Sanjeev Kumar Pippal, 2022. "An Artificial-Intelligence-Based Novel Rice Grade Model for Severity Estimation of Rice Diseases," Agriculture, MDPI, vol. 13(1), pages 1-19, December.
    2. Md. Mehedi Hasan & Touficur Rahman & A. F. M. Shahab Uddin & Syed Md. Galib & Mostafijur Rahman Akhond & Md. Jashim Uddin & Md. Alam Hossain, 2023. "Enhancing Rice Crop Management: Disease Classification Using Convolutional Neural Networks and Mobile Application Integration," Agriculture, MDPI, vol. 13(8), pages 1-17, August.
    3. Mingfeng Huang & Guoqin Xu & Junyu Li & Jianping Huang, 2021. "A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++," Agriculture, MDPI, vol. 11(12), pages 1-14, December.
    4. Changguang Feng & Minlan Jiang & Qi Huang & Lingguo Zeng & Changjiang Zhang & Yulong Fan, 2022. "A Lightweight Real-Time Rice Blast Disease Segmentation Method Based on DFFANet," Agriculture, MDPI, vol. 12(10), pages 1-12, September.

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