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

Shuo Chen, Kefei Zhang, Yindi Zhao, Yaqin Sun, Wei Ban, Yu Chen, Huifu Zhuang, Xuewei Zhang, Jinxiang Liu and Tao Yang
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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
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

Agriculture, 2021, vol. 11, issue 5, 1-18

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.

Keywords: rice bacterial leaf streak; leaf disease recognition; lesion segmentation; semantic segmentation; deep learning; convolutional neural network; disease severity estimation (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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