Diagnosing the symptoms of sheath blight disease on rice stalk with an in-situ hyperspectral imaging technique

文献类型： 外文期刊

第一作者： Zhang, Jingcheng

作者： Zhang, Jingcheng;Tian, Yangyang;Yan, Lijie;Wang, Bin;Wu, Kaihua;Wang, Ling;Xu, Junfeng

作者机构：

关键词： Rice sheath blight disease; Hyperspectral imaging technique; Anomalous region detection; Disease lesion recognition

期刊名称：BIOSYSTEMS ENGINEERING （ 影响因子：4.123； 五年影响因子：4.508 ）

ISSN： 1537-5110

年卷期： 2021 年 209 卷

页码：

收录情况： SCI

摘要： As a non-destructive technology for detection, hyperspectral imaging has potential for phenotyping plant diseases and providing information for plant protection. Currently, most studies based on hyperspectral imaging technology primarily focus on leaf diseases and less on stalk diseases. In this study, a set of automatic detection and diagnosis methods, combined with spectral and image analyses, was proposed for detecting a stalk disease, rice sheath blight (Rhizoctonia solani), which is widely distributed and deleterious to yield and quality of rice. This study proposed a stepwise method for detecting rice sheath blight. The procedure starts from removal of non-plant background using the k-means clustering algorithm. Then the rice anomalous regions are identified by applying Fisher linear discrimination on sensitive bands. Finally, the scabs of rice sheath blight are detected through a newly developed approach called Hyperspectral Feature Profile Scanning-based Scab Detection (HFPSSD). The validation results showed that the proposed method can effectively recognise disease scabs. The overall accuracy reached 98.42% at pixel level and 95.92% at patch level, which outperformed the traditional support vector machines (SVM) algorithm. The proposed method can potentially serve as a tool for high throughput detection of plant stalk diseases in the field. (c) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.

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