Early Detection and Identification of Rice Blast Based on Hyperspectral Image
KANG Li1, 2, YUAN Jian-qing3, GAO Rui1, KONG Qing-ming1, JIA Yin-jiang1, SU Zhong-bin1*
1. Academy of Electric and Information, Northeast Agricultural University, Harbin 150030, China
2. School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China
3. Harbin Finance University, Harbin 150030, China
Abstract：Rice blast is a worldwide destructive rice disease. It is of great significance for rice disease control and precision spraying to detect rice blast early and identify the severity of the disease. Based on field experiment and natural infection of rice blast, infected leaves and healthy leaves were collected in the early stage of leaf blast. Hyperspectral images in the spectral range of 400~1 000 nm were captured and the spectral data were extracted. Rice leaves will not immediately show lesions at the beginning of the disease, so it is impossible to identify and collect samples of infected leaves without lesions. In order to realize the early detection of infected leaves without visible lesion, this study proposed to take hyperspectral data of lesion-free areas adjacent to the lesioned areas on the infected leaves as level 1 samples. According to the area of the lesion, the samples were divided into four levels: level 0 (109 pieces) for healthy leaves, level 1 (116 pieces) for infected leaves without visible lesion, level 2 (107 pieces) for leaves with lesion area <10%, and level 3 (101 pieces) for leaves with lesion area <25%. Principal component analysis (PCA) and competitive adaptive reweighting sampling (CARS) were used to extract feature variables; PCA algorithm was used to reduce further the dimension of the bands extracted by CARS. The support vector machine (SVM), PCA4-SVM, PCA8-SVM，CARS-SVM and CARS-PCA-SVM models for early detection of rice blast were build based on the full spectral variables and extracted feature variables, respectively. In this study, all models had high detection accuracy for all levels of samples. Level 1 had good detection accuracy, similar to other levels. All models had an overall accuracy rate above 94.6%. The highest was the CARS-SVM model at 97.29%, and the CARS-PCA-SVM model at 96.61% was slightly lower, but its number of input variables was only 6, which was 71.43% less than that of 21 in the CARS-SVM model. It further reduced the complexity of CARS-SVM model and improved the operation speed. So, the comprehensive evaluation of CARS-PCA-SVM model was optimal, with the identification accuracy of 97.30%, 94.87%, 94.29% and 100.00% for each level, respectively. Therefore, it is feasible to use hyperspectral imaging technology to detect the early stage of rice blast. The results presented in this paper can provide new ideas for the detection of infected leaves without lesions at the beginning of rice blast, and provide a theoretical basis for the early control of rice blast, precision spraying of pesticide and the development of detection instruments.
Key words：Hyperspectral; Rice blast; Early detection; Principal component analysis; Competitive adaptive reweighting sampling
康 丽，袁建清，高 睿，孔庆明，贾银江，苏中滨. 高光谱成像的水稻稻瘟病早期分级检测[J]. 光谱学与光谱分析, 2021, 41(03): 898-902.
KANG Li, YUAN Jian-qing, GAO Rui, KONG Qing-ming, JIA Yin-jiang, SU Zhong-bin. Early Detection and Identification of Rice Blast Based on Hyperspectral Image. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 898-902.
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