文献类型： 外文期刊
作者： Yuan, Lin 1 ; Yan, Peng 2 ; Han, Wenyan 2 ; Huang, Yanbo 3 ; Wang, Bin 4 ; Zhang, Jingcheng 4 ; Zhang, Haibo 1 ; Bao, Zhi 1 ;
作者机构： 1.Zhejiang Univ Water Resources & Elect Power, Sch Informat Engn & Art & Design, Hangzhou 310018, Zhejiang, Peoples R China
2.Chinese Acad Agr Sci, Tea Res Inst, Hangzhou 310008, Zhejiang, Peoples R China
3.ARS, Crop Prod Syst Res Unit, USDA, POB 350, Stoneville, MS 38776 USA
4.Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Zhejiang, Peoples R China
关键词： Tea plant; Anthracnose; Hyperspectral image; ISODATA classification
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）
ISSN： 0168-1699
年卷期： 2019 年 167 卷
页码：
收录情况： SCI
摘要： Anthracnose (Gloeosporiwn theae-sinesis Miyake) is an important and common foliar disease in tea plants and is a severe threat to tea quality and production. Hyperspectral imaging technology enables non-invasive, objective detection of the damages ca by foliar disease and offers significant potential for plant disease prevention and phenotyping. This study proposes a novel method for detecting anthracnose in tea plants based on hyperspectral imaging. By analyzing the spectral sensitivity, we identified disease-sensitive bands at 542, 686, and 754 nm and used these bands to create two new disease indices: the Tea Anthracnose Ratio Index (TART) and the Tea Anthracnose Normalized Index (TANI). Based on an optimized set of spectral features, a strategy combining unsupervised classification and adaptive two-dimensional thresholding was developed to detect disease scabs. Compared with traditional pixel-based classification methods, the proposed method was not affected by leaf background differences and thereby provides an effective means for disease identification and damage analysis. The validation results gave an overall accuracy of 98% for identifying the disease at the leaf level and 94% at the pixel level. These results suggest that automated and accurate detection of anthracnose-infected tea leaves is possible by using hyperspectral imaging for practical tea-plant protection.
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