中文摘要：本文旨在以874-1734 nm光谱范围内的高光谱图像法和化学计量学联用进行油菜花瓣真菌感染检测。本文首先采用主成分分析法进行聚类分析，然后采用负荷加载主成分分析法和随机蛙跳算法对比选择最优波段，采用最小二乘支持向量机法基于最优波段和全波段建立判别模型，最后，采用AUC评估最小二乘支持向量机模型的分类性能。研究发现，基于最优波段组合的LS-SVM效果最好，AUC值为0.929。
外文摘要：Infected petals are often regarded as the source for the spread of fungi Sclerotinia sclerotiorum in all growing process of rapeseed (Brassica napus L.) plants. This research aimed to detect fungal infection of rapeseed petals by applying hyperspectral imaging in the spectral region of 874-1734 nm coupled with chemometrics. Reflectance was extracted from regions of interest (ROIs) in the hyperspectral image of each sample. Firstly, principal component analysis (PCA) was applied to conduct a cluster analysis with the first several principal components (PCs). Then, two methods including X-loadings of PCA and random frog (RF) algorithm were used and compared for optimizing wavebands selection. Least squares-support vector machine (LS-SVM) methodology was employed to establish discriminative models based on the optimal and full wavebands. Finally, area under the receiver operating characteristics curve (AUC) was utilized to evaluate classification performance of these LS-SVM models. It was found that LS-SVM based on the combination of all optimal wavebands had the best performance with AUC of 0.929. These results were promising and demonstrated the potential of applying hyperspectral imaging in fungus infection detection on rapeseed petals.
 
外文关键词：SCLEROTINIA-SCLEROTIORUM; OILSEED RAPE; RAMAN-SPECTROSCOPY; CHLOROPHYLL DISTRIBUTION; INFRARED-SPECTROSCOPY; COMPONENT ANALYSIS; CUCUMBER LEAVES; PLANT-DISEASES; RANDOM FROG; LEAF
作者：Zhao, Yan-Ru; Yu, Ke-Qiang; Li, Xiaoli; He, Yong
作者单位：Zhejiang Univ, Coll Biosystems Engn & Food Sci, 866 Yuhangtang Rd, Hangzhou 310058, Peoples R China.
期刊名称：SCIENTIFIC REPORTS
期刊影响因子：5.228
出版年份：2016
出版刊次：12
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