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

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...

Full description

Saved in:
Bibliographic Details
Published in:Agriculture (Basel) 2021-05, Vol.11 (5), p.420
Main Authors: Chen, Shuo, Zhang, Kefei, Zhao, Yindi, Sun, Yaqin, Ban, Wei, Chen, Yu, Zhuang, Huifu, Zhang, Xuewei, Liu, Jinxiang, Yang, Tao
Format: Article
Language:English
Subjects:
Accuracy
Agricultural production
Classification
convolutional neural network
Crop diseases
Crops
Datasets
Deep learning
Design
leaf disease recognition
Leaf streak
Leaves
lesion segmentation
Lesions
Model accuracy
Neural networks
Plant diseases
Rice
rice bacterial leaf streak
Semantic segmentation
Semantics
Soybeans
Support vector machines
Unmanned aerial vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2077-0472
2077-0472
DOI:10.3390/agriculture11050420