背景:辣椒疫霉疫病是辣椒生长过程中的一种破坏性疾病,显著影响其产量和品质。准确,快速,辣椒疫霉的无损早期检测对辣椒生产管理具有重要意义。这项研究调查了使用多光谱成像结合机器学习检测辣椒疫病的可能性。辣椒分为两组:一组接种疫霉疫病,另一个不处理作为对照。在接种前0小时和接种后48、60、72和84小时收集多光谱图像。利用多光谱成像系统的支持软件对19个波长的光谱特征进行提取,使用灰度共生矩阵(GLCM)和局部二进制模式(LBP)提取纹理特征。主成分分析(PCA),连续投影算法(SPA),和遗传算法(GA)用于从提取的光谱和纹理特征中进行特征选择。基于有效的单光谱特征和显著的光谱纹理融合特征建立了两种分类模型:偏最小二乘判别分析(PLS_DA)和一维卷积神经网络(1D-CNN)。基于PCA从光谱数据中提取的五个主成分(PC)系数,构建了二维卷积神经网络(2D-CNN),加权,并与19通道多光谱图像相加以创建新的PC图像。
结果:结果表明,使用PCA进行特征选择的模型表现出相对稳定的分类性能。基于单光谱特征的PLS-DA和1D-CNN的准确率分别为82.6%和83.3%,分别,在48h标记。相比之下,基于光谱纹理融合的PLS-DA和1D-CNN的准确率分别达到85.9%和91.3%,分别,在相同的48h标记。基于5张PC图像的2D-CNN的准确率为82%。
结论:研究表明,接种后48小时(可见症状前36小时)可以检测到疫霉感染。本研究为辣椒疫霉疫病的早期检测提供了一种有效的方法。
BACKGROUND: Pepper Phytophthora blight is a devastating disease during the growth process of peppers, significantly affecting their yield and quality. Accurate, rapid, and non-destructive early detection of pepper Phytophthora blight is of great importance for pepper production management. This study investigated the possibility of using multispectral imaging combined with machine learning to detect Phytophthora blight in peppers. Peppers were divided into two groups: one group was inoculated with Phytophthora blight, and the other was left untreated as a control. Multispectral images were collected at 0-h samples before inoculation and at 48, 60, 72, and 84 h after inoculation. The supporting software of the multispectral imaging system was used to extract spectral features from 19 wavelengths, and textural features were extracted using a gray-level co-occurrence matrix (GLCM) and a local binary pattern (LBP). The principal component analysis (PCA), successive projection algorithm (SPA), and genetic algorithm (GA) were used for feature selection from the extracted spectral and textural features. Two classification models were established based on effective single spectral features and significant spectral textural fusion features: a partial least squares discriminant analysis (PLS_DA) and one-dimensional convolutional neural network (1D-CNN). A two-dimensional convolutional neural network (2D-CNN) was constructed based on five principal component (PC) coefficients extracted from the spectral data using PCA, weighted, and summed with 19-channel multispectral images to create new PC images.
RESULTS: The results indicated that the models using PCA for feature selection exhibit relatively stable classification performance. The accuracy of PLS-DA and 1D-CNN based on single spectral features is 82.6% and 83.3%, respectively, at the 48h mark. In contrast, the accuracy of PLS-DA and 1D-CNN based on spectral texture fusion reached 85.9% and 91.3%, respectively, at the same 48h mark. The accuracy of the 2D-CNN based on 5 PC images is 82%.
CONCLUSIONS: The research indicates that Phytophthora blight infection can be detected 48 h after inoculation (36 h before visible symptoms). This study provides an effective method for the early detection of Phytophthora blight in peppers.