Grading method of rice panicle blast severity based on hyperspectral image

Abstract: Estimation of panicle blast level plays an important role in high-quality production of rice. It helps to quantitatively assess the level of blast resistance and severity in the field to make appropriate decisions in gauging cultivar resistance in rice breeding or precisely controlling blast epidemic. However, it is difficult to evaluate the blast disease degree automatically and accurately. In this study, a novel grading method for panicle blast severity based on hyperspectral imaging technology is proposed. The method defines a bag of spectrum words (BoSW) model for hyperspectral image data representation. The BoSW model based on hyperspectral image data representation is used as the input of a Chi-square kernel support vector machine (Chi-SVM) classifier for predicting the rice panicle blast level. More precisely, dense grids are firstly extracted over the spatial X- and Y-axes across the whole spectral Z-axis. The average spectrum curve of all the pixels within a grid cube is calculated. Then, K-Means clustering would be performed on the large collection of average spectrum curves from the training samples to form the dictionary of spectrum words. Next, each spectrum curve on the grid cube is quantized into one of spectrum words. Each hyperspectral image of rice panicle is transformed into a map of spectrum words. All the spectrum words are distributed evenly on the spatial XY-axis plane. BoSW model for each hyperspectral data cube is then formed by means of histogram statistics of spectrum word occurrences. Finally, a Chi-SVM classifier is trained using the BoSW representations of rice panicle hyperspectral images for predicting panicle blast infection levels. The proposed BoSW method uses both the image and full-spectrum information by means of regular grid cube extraction, which utilizes the full potential of the imaging sensing system. Meanwhile, the representation dimension for each hyperspectral image is significantly reduced, i.e. 100 here, and thus relieving modeling difficulty. The procedure of clustering helps to find the representative spectrum curves and quantizing helps to transform all the continuous-state spectrum curves into one of representative spectrum curve. Thus the proposed BoSW method is invariant to complicated noise and robust to rice cultivars.To verify the proposed BoSW method, a total of 170 fresh rice panicles covering more than 50 cultivars are collected from an experimental field for the performance evaluation. The experimental field is located in regional testing area for evaluating rice cultivars in Guangdong province. Therefore, all the rice plants in this area are naturally inoculated as the area is a typical source of rice blast fungus. The hyperspectral images of all the rice panicles are acquired using HyperSIS-VNIR-QE imaging spectrometer and then are transformed into 100-dimension BoSW representation for the construction of Chi-SVM classifier. Four-class label of hyperspectral image sample is determined by plant protection expert according to description of blast infection levels. Two thirds of the labeled BoSW representations are randomly selected for training and the rest for testing. Experimental results show that the proposed BoSW based method achieved high classification accuracy of 94.72%. This result is much better than traditional hyperspectral image analysis methods such as Principal component analysis (PCA), sensitive bands selection and etc. Moreover, the proposed BoSW demonstrates strong robustness to rice cultivars, which is vital for the wide and practical application. This research improves the classification accuracy of rice panicle blast grading and provides a reference to evaluate other disease level grading as well.