2021
DOI: 10.3390/agriculture11050420
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An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation

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 seg… Show more

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Cited by 72 publications
(38 citation statements)
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“…In a recent study this approach was demonstrated for black rot, black measles, leaf blight and mites on grapevine leaves [39]. Further studies have shown promising detection of leaf localized diseases using semantic segmentation for coffee and rice [40,41] However, the results obtained with the simple binary classification setup presented here clearly show a high correlation with standard scoring methods indicating its suitability for highthroughput phenotyping of leaf discs. Furthermore, it keeps the requirements in terms of computational resources and image training sets to a minimum allowing a fast adaptation to new plant-pathogen systems.…”
Section: Further Future Improvementsmentioning
confidence: 81%
“…In a recent study this approach was demonstrated for black rot, black measles, leaf blight and mites on grapevine leaves [39]. Further studies have shown promising detection of leaf localized diseases using semantic segmentation for coffee and rice [40,41] However, the results obtained with the simple binary classification setup presented here clearly show a high correlation with standard scoring methods indicating its suitability for highthroughput phenotyping of leaf discs. Furthermore, it keeps the requirements in terms of computational resources and image training sets to a minimum allowing a fast adaptation to new plant-pathogen systems.…”
Section: Further Future Improvementsmentioning
confidence: 81%
“…In a recent study this approach was demonstrated for black rot, black measles, leaf blight and mites on grapevine leaves [39]. Further studies have shown promising detection of leaf localized diseases using semantic segmentation for coffee and rice [40,41] However, the results obtained with the simple binary classification setup presented here clearly show a high correlation with standard scoring methods indicating its suitability for high-throughput phenotyping of leaf discs. Furthermore, it keeps the requirements in terms of computational resources and image training sets to a minimum allowing a fast adaptation to new plant-pathogen systems.…”
Section: Further Improvementsmentioning
confidence: 82%
“…Konstantinos et al developed CNN models to perform plant disease detection and diagnosis using simple leaves images of healthy and diseased plants through deep learning methodologies [23]. Chen et al used the UNet-based BLSNet to automatic identify and segment the diseased region of Rice bacterial leaf streak from the camera photos [24]. The appearance of the attention mechanism also further improves the performance of the network [22,25].…”
Section: Introductionmentioning
confidence: 99%