Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging

216 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Comparison of Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR) Methods for Protein and Hardness Predictions using the Near-Infrared (NIR) Hyperspectral Images of Bulk Samples of Canadian Wheat (2014, methodology): “Near-Infrared Hyperspectral Imaging System and Image Acquisition Procedures In this study, the NIR hyperspectral imaging system reported by Mahesh et al. (2008, 2011, 2014) and Singh et al. (2009) was used and is described below for enhanced readability of this manuscript.”
Use of diatomaceous earth for the management of stored-product pests (2014, background): “About 10 30% of grains produced worldwide are lost every year due to stored-grain pests (Singh et al. 2009).”
Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice (2015, methodology): “…with toxigenic fungi from uninfected samples, and NIR hyperspectral imaging (1,000–1,600 nm) was employed to classify healthy wheat kernels from damaged kernels based on linear discriminant analysis and quadratic discriminant analysis classifiers with an accuracy of 85–100 % (Singh et al. 2009).”
Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety (2014, methodology): “A NIR hyperspectral imaging system in the range of wavelengths 1,000–1,600 nm was developed for detection of insect-damaged wheat kernels [103].”