二次主成分提取高光谱的病害薯叶特征波长

针对马铃薯晚疫病,提出了将病害叶片和健康叶片联合测试并提取有效特征波长的检测方法。对健康和病害叶片的光谱图像进行主成分分析,并从主成分图像的权重系数曲线中提取出6个健康叶片特征波长和病害叶片特征波长。基于健康叶片与病害叶片的6个特征波长做第二次主成分分析,将特征波长优化至712.19 nm、749.70 nm和841.47 nm,再基于这3个特征波长做主成分分析,选用主成分中对比度最明显的图像来识别病害区域。研究表明,采用健康叶片与病害叶片联合使用并结合二次主成分分析可实现马铃薯叶片病害的设别,且识别率可达100%。

Extraction of hyperspectral diseased potato leaf characteristic wavelength by second principal component

A method for joint detection of diseased leaves and healthy leaves and extraction of effective characteristic wavelengths for potato late blight was proposed. Principal component analysis was performed on the spectral images of healthy and late blight leaves, and the weight coefficient curves of principal component images were analyzed to extract six characteristic bands of healthy leaf and diseased leaf. Based on the six characteristic wavelengths, the second principal component analysis was performed, and the optimization was reduced to three characteristic wavelengths of 712.19, 749.70 and 841.47 nm. Based on these three characteristic wavelengths, principal component analysis was used to identify the diseased area with the most contrasting image of the main component, and the recognition rate was 100%. The characteristic wavelength of potato late blight disease could be achieved by combining healthy leaves with diseased leaves and secondary principal components. This technology provides a reference for the development of related equipment for potato leaf disease detection.