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

doi:10.3390/agriculture11050420

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

2021-05-07

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DOI：10.3390/agriculture11050420

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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

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

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