Learning techniques used in computer vision for food quality evaluation: a review

Automated evaluation of food colour by means of multivariate image analysis coupled to a wavelet-based classification algorithm

Shahin, M.A. and Symons, S.J. 2001. A machine vision system for grading lentils. Canadian Biosystems Engineering/Le génie des biosystèmes au Canada 43:7.7-7.14. Color and appearance of lentils are important grading factors. A machine vision system for color grading of lentils was developed using a flatbed scanner as the imagegathering device. Grain samples belonging to different grades of large green lentils were scanned and analyzed over a two-crop season period. Image color, color distribution, and textural features were found to be good indicators of lentil grade. Linear discriminant analysis, k-nearest neighbors, and neural network based classifiers performed equally well in predicting sample grade. An online classification system was developed with a neural classifier that achieved an overall accuracy (agreement with the grain inspectors) of more than 90%.

A machine vision system for grading lentils

AE—Automation and Emerging Technologies: Identification and Segmentation of Occluding Groups of Grain Kernels in a Grain Sample Image

Measurement of the geometrical features and surface color of rapeseeds using digital image analysis

Image Analysis In Conjunction With Statistical Pattern Recognition Was Used To Discriminate Whole Corn From Broken Com Kemels. Multivariate Discriminant Analysis Was Used To Develop Classification Rules To Aid In The Identification Of Whole Kernels And/Or Broken Kernels. Twelve Parameters Describing The Shape And Size Of Com Kernels Were Considered As Discriminators. A Stepwise Discriminant Analysis Was Performed To Identify Different Subsets Of The 12 Parameters To Consider As Discriminators. Subsets Of 7 And 4 Parameters, Along With The Full Set Of 12 Parameters, Were Evaluated For Discrimination Performance. Experiments Were Conducted With Different Optical Settings To Evaluate The Effect Of Image Resolution On The Results Of Discriminant Analysis. All Of The Broken And 98% Of The Whole Com Kemels In An Independent Set Of Unknown Kemels Were Correctly Identified When Using The Procedures Developed.

J.Steele

Papers

Discrimination of whole from broken corn kernels with image analysis