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.
Highlights
Rice (Oryza sativa L.) is one of the three main crops in Asia and is widely planted.Due to the complex characteristics of rice growing in relatively hot and humid conditions, it is vulnerable to diseases
The third-row sample is enhanced by a scale transformer and the detailed features of lesion segmentation from the BLSNet model were better than those produced by UNet and DeepLabV3+
This paper proposed BLSNet for rice bacterial leaf streak (BLS) lesion segmentation and disease severity estimation based on the semantic segmentation technique and attention mechanism and multi-scale methods
Summary
Rice (Oryza sativa L.) is one of the three main crops in Asia and is widely planted.Due to the complex characteristics of rice growing in relatively hot and humid conditions, it is vulnerable to diseases. Rice bacterial leaf streak (BLS) is one of the most serious diseases affecting rice production, occurring early in the growth cycle, spreading quickly and causing severe damage [1]. The timely monitoring and prediction of the occurrence and development of BLS is of great significance to maintain rice production. The leaves are usually damaged by BLS and the traditional disease severity estimation of BLS depends on the lesion area as a proportion of the total leaf area. The estimation of leaf damage is largely dependent on the level of experience of agronomists and farmers, which is labor intensive and time consuming, and the estimations are often subjective and unreliable [2]. The in each eachrow rowinin table is the highest accuracy achieved by model
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