Lu Zhihao, Huang Shihao, Zhang Xiaojun, Shi Yuxuan, Yang Wanneng, Zhu Longfu, Huang Chenglong
College of Engineering, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
Plant Methods. 2023 Jul 29;19(1):75. doi: 10.1186/s13007-023-01056-4.
Verticillium wilt is the major disease of cotton, which would cause serious yield reduction and economic losses, and the identification of cotton verticillium wilt is of great significance to cotton research. However, the traditional method is still manual, which is subjective, inefficient, and labor-intensive, and therefore, this study has proposed a novel method for cotton verticillium wilt identification based on spectral and image feature fusion. The cotton hyper-spectral images have been collected, while the regions of interest (ROI) have been extracted as samples including 499 healthy leaves and 498 diseased leaves, and the average spectral information and RGB image of each sample were obtained. In spectral feature processing, the preprocessing methods including Savitzky-Golay smoothing (SG), multiplicative scatter correction (MSC), de-trending (DT) and mean normalization (MN) algorithms have been adopted, while the feature band extraction methods have adopted principal component analysis (PCA) and successive projections algorithm (SPA). In RGB image feature processing, the EfficientNet was applied to build classification model and 16 image features have been extracted from the last convolutional layer. And then, the obtained spectral and image features were fused, while the classification model was established by support vector machine (SVM) and back propagation neural network (BPNN). Additionally, the spectral full bands and feature bands were used as comparison for SVM and BPNN classification respectively.
The results showed that the average accuracy of EfficientNet for cotton verticillium wilt identification was 93.00%. By spectral full bands, SG-MSC-BPNN model obtained the better performance with classification accuracy of 93.78%. By feature bands, SG-MN-SPA-BPNN model obtained the better performance with classification accuracy of 93.78%. By spectral and image fused features, SG-MN-SPA-FF-BPNN model obtained the best performance with classification accuracy of 98.99%.
The study demonstrated that it was feasible and effective to use fused spectral and image features based on hyper-spectral imaging to improve identification accuracy of cotton verticillium wilt. The study provided theoretical basis and methods for non-destructive and accurate identification of cotton verticillium wilt.
黄萎病是棉花的主要病害，会导致严重的产量下降和经济损失，棉花黄萎病的识别对棉花研究具有重要意义。然而，传统方法仍然是人工操作，主观、低效且劳动强度大，因此，本研究提出了一种基于光谱和图像特征融合的棉花黄萎病识别新方法。采集了棉花高光谱图像，提取了感兴趣区域（ROI）作为样本，包括499片健康叶片和498片患病叶片，并获取了每个样本的平均光谱信息和RGB图像。在光谱特征处理中，采用了包括Savitzky-Golay平滑（SG）、多元散射校正（MSC）、去趋势（DT）和均值归一化（MN）算法在内的预处理方法，同时采用主成分分析（PCA）和连续投影算法（SPA）进行特征波段提取。在RGB图像特征处理中，应用EfficientNet构建分类模型，并从最后一个卷积层提取了16个图像特征。然后，将获得的光谱和图像特征进行融合，同时通过支持向量机（SVM）和反向传播神经网络（BPNN）建立分类模型。此外，分别将光谱全波段和特征波段用于SVM和BPNN分类进行比较。
结果表明，EfficientNet对棉花黄萎病识别的平均准确率为93.00%。通过光谱全波段，SG-MSC-BPNN模型表现较好，分类准确率为93.78%。通过特征波段，SG-MN-SPA-BPNN模型表现较好，分类准确率为93.78%。通过光谱和图像融合特征，SG-MN-SPA-FF-BPNN模型表现最佳，分类准确率为98.99%。
该研究表明，基于高光谱成像的融合光谱和图像特征来提高棉花黄萎病识别准确率是可行且有效的。该研究为棉花黄萎病的无损、准确识别提供了理论依据和方法。
