高光谱成像
主成分分析
线性判别分析
模式识别(心理学)
人工智能
二次分类器
数学
VNIR公司
分类器(UML)
生物
计算机科学
作者
Chandra B. Singh,Digvir S. Jayas,Jitendra Paliwal,N. D. G. White
出处
期刊:Transactions of the ASABE
[American Society of Agricultural and Biological Engineers]
日期:2007-01-01
卷期号:50 (6): 2171-2176
被引量:82
摘要
Different species of fungi infect grain in the field and storage facilities. Contamination by fungi in grain is detected and quantified by traditional methods, such as microbial incubation and microscopic detection, which are subjective, labor intensive, and time consuming. An accurate and timely detection technique for fungal growth in grain is needed to prevent grain from spoiling and to reduce quality loss. In this study, the potential of near-infrared hyperspectral imaging to detect fungal infection in wheat was investigated. Wheat kernels infected with storage fungi, namely Penicillium spp., Aspergillus glaucus, and Aspergillus niger, were scanned using a hyperspectral imaging system, and a total of 20 image slices at evenly spaced wavelengths between 1000 to 1600 nm were acquired to form a hypercube. A multivariate image analysis (MIA) technique based on principal component analysis (PCA) was used to reduce the dimensionality of the image hypercubes. Two-class and four-class classification models were developed by applying k-means clustering and discriminant (linear, quadratic, and Mahalanobis) analyses. Two-class discriminant classification models gave maximum classification accuracy of 100%, and on average 97.8% infected kernels were correctly classified by the linear discriminant classifier. The four-class linear discriminant classifier correctly classified more than 95% of the kernels infected with Penicillium and 91.7% healthy kernels. However, the discriminant classifiers misclassified the kernels infected with A. niger and A. glaucus.
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