Early Detection and Identification of Rice Sheath Blight Disease Based on Hyperspectral Image and Chlorophyll Content
ZHU Meng-yuan1,2, YANG Hong-bing1,2*, LI Zhi-wei1,2
1. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
2. Jiangsu Key Laboratory of Intelligent Agricultural Equipment, Nanjing 210031, China
Abstract：Hyperspectral imaging combined with chemometrics was successfully proposed to identify the rice sheath blight disease. First, infected rice plants with rice sheath blight in the seedling period to get the infected rice plants, then used the hyperspectral imaging system to acquire the hyperspectral imagines in the spectral range of 358~1 021 nm, finally selected 240 samples of all hyperspectral imagines to analyze, including 120 healthy samples and 120 infected samples. According to the spectral dimension of hyperspectral image, extracted the region of interest(ROI) of healthy and infected rice leaves, pretreated the spectral data of the region of interest with pretreatments including SG smoothing, SG-1D, SG-2D, SNV and MSC, then established the linear discriminant analysis (LDA) and support vector machine (SVM) classification models. The result showed that the linear discriminant analysis (LDA) model with SG-2D pretreatment achieved the better performance, with the correct recognition rate of the modeling set being 98.3% and the correct recognition rate of the prediction set being 95%. After five kinds of pretreatments, extracted the feature wavelengths with the method of x-loading weights, then established the linear discriminant analysis (LDA) and support vector machine (SVM) classification models based on feature wavelengths. The result showed that the linear discriminant analysis (LDA) model with SG-2D pretreatment achieved the better performance, with the correct recognition rate being 97.8% in the modeling set and 95% in the prediction set. Moreover, the model performance based on x-loading weights was equivalent to that of the whole band. So, it can be used to identify the rice sheath blight disease with x-loading weights. According to the image dimension of hyperspectral image, the principal component analysis, probabilistic filtering and second-order probabilistic filtering were proposed in this paper, then established the back propagation neural network (BPNN) and support vector machine (SVM) classification models. The result showed that the BPNN based on image principal component analysis achieved the better performance, with the correct recognition rate being 90.6% in the modeling set and 93.3% in the prediction set. According to the spectral and image dimension of hyperspectral image, the chlorophyll content was proposed to be another feature of disease recognition, which was combined with spectral characteristics and image features to build models to compare the performance of each model. Then established the back propagation neural network (BPNN) and linear discriminant analysis (LDA) classification models. The spectral features combining with chlorophyll content, image features combining with chlorophyll content and spectral, image features combining with chlorophyll content were proposed. The performance of spectral, image features combining with chlorophyll respectively were both better than that using the spectral and image features alone. BPNN based on spectral features combining with chlorophyll content achieved the better performance, with the correct recognition rate being 100% in the modeling set and 96.7% in the prediction set, also, this model achieved the best performance compared with all models in this paper. The overall results indicated that hyperspectral imaging technology with chlorophyll content can accurately identify the early rice sheath blight disease and provide a new method for early detection of rice disease.
朱梦远，杨红兵，李志伟. 高光谱图像和叶绿素含量的水稻纹枯病早期检测识别[J]. 光谱学与光谱分析, 2019, 39(06): 1898-1904.
ZHU Meng-yuan, YANG Hong-bing, LI Zhi-wei. Early Detection and Identification of Rice Sheath Blight Disease Based on Hyperspectral Image and Chlorophyll Content. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1898-1904.
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