基于高光谱病害特征提取的温室黄瓜霜霉病早期检测

针对温室黄瓜早期霜霉病高光谱图像田间采集环境光照的影响及有效病害特征难以提取的问题，提出融合病害差异信息改进的竞争性自适应重加权算法(Competitive adaptive reweighted sampling, CARS)和连续投影算法(Successive projections algorithm, SPA)相结合的特征波段提取方法，并建立了黄瓜霜霉病早期检测模型。首先，采集黄瓜健康叶片和染病12d内每天的高光谱图像，按病程分为7类；提取感兴趣区域，并计算平均光谱作为光谱数据；采用包络线消除法确定霜霉病害差异波段，基于病害差异波段采用CARS对7个不同阶段的光谱数据分别提取特征波段，再利用SPA进行二次降维寻优；最后，将各特征波段组合，得到47个特征波段数据，据此建立最小二乘-支持向量机(Least square support vector machines, LSSVM)模型，用于病害检测。在94个叶片样本组成的测试集上进行了病害检测实验，结果表明，融合病害差异信息的Dis-CARS-SPA-LSSVM对染病2d到发病12d均能取得100%的检测识别率；对染病1d的测试集检测识别率达到95.83%，其中染病样本的召回率达到100%，相较于未融合病害差异信息的CARS-SPA特征提取方法识别率高4.16个百分点。说明所提出的Dis-CARS-SPA-LSSVM模型能够有效实现温室黄瓜霜霉病害的早期检测。

Early Detection of Cucumber Downy Mildew in Greenhouse by Hyperspectral Disease Differential Feature Extraction

For the early hyperspectral images of cucumber downy mildew in greenhouses collected in field, it is influenced by environmental illumination and difficult to extract effective features from them. To solve these problems, a novel method of extracting feature bands based on disease difference information was proposed, which improved competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA). Besides, an early detection model was built for cucumber downy mildew. Firstly, hyperspectral images were collected for leaves of healthy cucumber and leaves after infection in 12 consecutive days, which were divided into seven categories based on the degree of infection. Then, spectral data was calculated as the average spectrum of region of interest, the difference bands of downy mildew disease were determined by envelope elimination method and feature bands were extracted via CARS for seven different stages of it. SPA was used to perform secondary dimensionality reduction and optimization. Finally, all feature bands were combined to obtain 47 feature bands data. Based on this, a least square support vector machine (LSSVM) was established for disease detection. The disease detection test was performed on a test set of 94 leaf samples. The results showed that Dis-CARS-SPA-LSSVM fused disease difference information can obtain 100% detection rate after 2~12 days infection of disease. The detection rate of the test set infected with disease for 1 day reached 95.83%, the recall rate of infected samples reached 100%, and it avoided the randomness of CARS-SPA feature extraction method which did not fuse the disease difference information due to the interference bands of the non downy mildew disease feature bands, and the recognition rate was 4.16 percentage points higher than that of CARS-SPA feature extraction model. The experiment results demonstrated that the proposed Dis-CARS-SPA-LSSVM model can effectively achieve early detection of downy mildew disease in greenhouse with a higher accuracy rate.