基于高光谱叶片图像的扩展协同表示模型黄瓜病害识别

摘要：在每种病害具有足够训练样本的情况下，基于协同表示 (CR) 的分类已成功应用于植物病害识别。然而，收集足 够的训练样本通常耗时费力。此外，受非理想测量环境的影响，样本可能会因不良照明和相邻叶片遮挡而引入的变量而 受到破坏。因此，本文提出了一种基于扩展协同表示 (ECR) 的分类模型。然后，将其应用于黄瓜叶片病害识别，构建一 个由每种病害的几个代表性样本组成的纯光谱库，并设计一个处理叠加在样本上的线性变量的通用变异光谱库。因此， 每个查询样本都被编码为来自这两个光谱库的原子的线性组合，并且疾病身份由最小重建残差的疾病确定。对从正常叶 片和感染黄瓜炭疽病和褐斑病的叶片的病害中提取的光谱曲线进行了实验。诊断准确率高于 94.7%，平均在线诊断时间 短，约为1~1.3 ms。基于ECR的分类模型在黄瓜叶部病害快速准确诊断中是可行的。

关键词：黄瓜病害识别；高光谱成像；扩展协同表示（ECR）；光谱库

期刊：Sensors

DOI：10.3390/s20144045

设备：GaiaField

Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model

Abstract：Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with su cient training samples of each disease. However, collecting enough training samples is usually time consuming and labor- intensive. Moreover, influenced by the non-ideal measurement environment, samples may be corrupted by variables introduced by bad illumination and occlusions of adjacent leaves. Consequently, an extended collaborative representation (ECR)-based classification model is presented in this paper. Then, it is applied to cucumber leaf disease recognition, which constructs a pure spectral library consisting of several representative samples for each disease and designs a universal variation spectral library that deals with linear variables superimposed on samples. Thus, each query sample is encoded as a linear combination of atoms from these two spectral libraries and disease identity is determined by the disease of minimal reconstruction residuals. Experiments are conducted on spectral curves extracted from normal leaves and the disease lesions of leaves infected with cucumber anthracnose and brown spot. The diagnostic accuracy is higher than 94.7% and the average online diagnosis time is short, about 1 to 1.3 ms. The results indicate that the ECR-based classification model is feasible in the fast and accurate diagnosis of cucumber leaf diseases.

Keywords：cucumber disease recognition; hyperspectral imaging; extended collaborative representation (ECR); spectral library