Abstract
Rice leaf blast, which is seriously affecting the yield and quality of rice around the world, is a fungal disease that easily develops under high temperature and humidity conditions. Therefore, the use of accurate and non-destructive diagnostic methods is important for rice production management. Hyperspectral imaging technology is a type of crop disease identification method with great potential. However, a large amount of redundant information mixed in hyperspectral data makes it more difficult to establish an efficient disease classification model. At the same time, the difficulty and small scale of agricultural hyperspectral imaging data acquisition has resulted in unrepresentative features being acquired. Therefore, the focus of this study was to determine the best classification features and classification models for the five disease classes of leaf blast in order to improve the accuracy of grading the disease. First, the hyperspectral imaging data were pre-processed in order to extract rice leaf samples of five disease classes, and the number of samples was increased by data augmentation methods. Secondly, spectral feature wavelengths, vegetation indices and texture features were obtained based on the amplified sample data. Thirdly, seven one-dimensional deep convolutional neural networks (DCNN) models were constructed based on spectral feature wavelengths, vegetation indices, texture features and their fusion features. Finally, the model in this paper was compared and analyzed with the Inception V3, ZF-Net, TextCNN and bidirectional gated recurrent unit (BiGRU); support vector machine (SVM); and extreme learning machine (ELM) models in order to determine the best classification features and classification models for different disease classes of leaf blast. The results showed that the classification model constructed using fused features was significantly better than the model constructed with a single feature in terms of accuracy in grading the degree of leaf blast disease. The best performance was achieved with the combination of the successive projections algorithm (SPA) selected feature wavelengths and texture features (TFs). The modeling results also show that the DCNN model provides better classification capability for disease classification than the Inception V3, ZF-Net, TextCNN, BiGRU, SVM and ELM classification models. The SPA + TFs-DCNN achieved the best classification accuracy with an overall accuracy (OA) and Kappa of 98.58% and 98.22%, respectively. In terms of the classification of the specific different disease classes, the F1-scores for diseases of classes 0, 1 and 2 were all 100%, while the F1-scores for diseases of classes 4 and 5 were 96.48% and 96.68%, respectively. This study provides a new method for the identification and classification of rice leaf blast and a research basis for assessing the extent of the disease in the field.
Highlights
The results show that all seven deep convolutional neural networks (DCNN) models designed based on different features have high classification accuracy, with overall accuracy (OA) greater than 88% and Kappa coefficient greater than 85%
The results showed that the model constructed based on fused features was significantly better than the model constructed based on single feature variables in terms of accuracy in the classification of the degree of leaf blast disease
The best performance was achieved by combining the successive projections algorithm (SPA) screened spectral features (450, 543, 679, 693, 714, 757, 972 and 985 nm) with textural features (MEne, SDEne, mean value of entropy (MEnt), SDEnt, mean value of contrast (MCon), standard deviation of contrast (SDCon) and mean value of correlation (MCor))
Summary
Crop pests and diseases cause huge losses of agricultural production [1]. According to the Food and Agriculture Organization of the United Nations, the annual reduction in creativecommons.org/licenses/by/ 4.0/). In China, the amount of grain lost due to pest and disease outbreaks and hazards is about 30% of the total production each year, which has a huge impact on the domestic economy [3]. We still mainly rely on plant protection personnel to conduct field surveys and field sampling in order to monitor crop disease. These traditional detection methods have high accuracy and reliability, they are time-consuming, laborious and lack representativeness. There is an urgent need to improve pest and disease monitoring and control methods
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