马铃薯晚疫病害的高光谱图像空谱对比研究

为了快速检测马铃薯晚疫病,采用高光谱成像技术对马铃薯晚疫病的空谱信息进行对比研究以得到最佳判别手段。使用高光谱相机采集病害侵染0～6 d的高光谱图像,同时选取第6 d典型晚疫病病害的高光谱数据作为研究对象。采用二阶导数结合主成分分析和二次主成分分析分别从光谱和空间两个方面进行特征提取,之后基于特征波段反射率和主成分图像灰度值建立K最近邻分类算法、BP神经网络、决策树算法3种识别模型对不同时期病害进行识别。实验结果表明:基于二次主成分图像的灰度值结合BP神经网络建立的模型对马铃薯晚疫病的识别具有良好的成效,其识别率达96.6%。利用主成分图像灰度值建立的3种模型既减少了波段的冗余又提高了识别率,为研究和开发实时在线检测仪器提供了参考。

Comparative study on spatial and spectral of hyperspectral potato leaf late blight

In order to detect potato late blight quickly and compare the difference of spatial spectrum information, hyperspectral imaging technology was used to compare the spatial spectrum of potato late blight in order to find the best discriminant method. A hyperspectral camera was used to collect the hyperspectral images of 0−6 days of disease infection. At the same time, the hyperspectral data of typical late blight diseases on the 6th day were selected as the research object. Second-order derivative combined with principal component analysis and second-order principal component analysis were used to extract features from spectral and spatial aspects respectively. Then, K-nearest neighbor classification algorithm, BP neural network and decision tree algorithm were established based on the reflectance of characteristic band and the gray value of principal component image to identify diseases in different periods. The recognition rate of the model was 96.6% based on the gray value of the secondary principal component image and BP neural network. The experimental results showed that the model based on the gray value of the secondary principal component image and BP neural network had good effect on the identification of potato late blight. The three models based on the gray value of the principal component image reduce the redundancy of the band and improve the recognition rate, which provides a reference for researching and developing real-time on-line testing equipment and instruments..