高光谱成像
线性判别分析
马氏距离
模式识别(心理学)
情景喜剧
人工智能
二次分类器
数学
主成分分析
标准差
统计
生物
支持向量机
计算机科学
农学
作者
Chandra B. Singh,Digvir S. Jayas,Jitendra Paliwal,N. D. G. White
标识
DOI:10.1016/j.jspr.2008.12.002
摘要
Insect damage in wheat adversely affects its quality and is considered one of the most important degrading factors in Canada. The potential of near-infrared (NIR) hyperspectral imaging for the detection of insect-damaged wheat kernels was investigated. Healthy wheat kernels and wheat kernels visibly damaged by Sitophilus oryzae, Rhyzopertha dominica, Cryptolestes ferrugineus, and Tribolium castaneum were scanned in the 1000–1600 nm wavelength range using an NIR hyperspectral imaging system. Dimensionality of the acquired hyperspectral data was reduced using multivariate image analysis. Six statistical image features (maximum, minimum, mean, median, standard deviation, and variance) and 10 histogram features were extracted from images at 1101.69 and 1305.05 nm and given as input to statistical discriminant classifiers (linear, quadratic, and Mahalanobis) for classification. Linear discriminant analysis and quadratic discriminant analysis classifiers correctly classified 85–100% healthy and insect-damaged wheat kernels.
科研通智能强力驱动
Strongly Powered by AbleSci AI