Automated recognition and classification of adulteration levels from bulk paddy grain samples

Abstract

Fraudulent labeling and adulteration are the major concerns in the global rice industry. Almost all the paddy varieties being sold in the market are prone to adulteration. It is very difficult to differentiate paddy grains of various varieties in the mixed bulk sample based on visual observation. Currently, there is no sophisticated appearance-based commercial scale technology to reliably detect and quantify adulteration in bulk paddy grain samples. The paper presents a cost-effective image processing technique for the recognition of adulteration and classification of adulteration levels (%) from the images of adulterated bulk paddy samples using state-of-the-art color and texture features. In this work, seven adulterated bulk paddy samples are considered and each of the samples is prepared by mixing a premium paddy variety with the identical looking and commercially inferior paddy variety at five different adulteration levels (weight ratios) of 10%, 15%, 20%, 25% and 30%. The study compares the performances of three different classification models, namely, Multilayer Back Propagation Neural Network (BPNN), Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN). The Principal Component Analysis (PCA) and Sequential Forward Floating Selection (SFFS) methods have been employed separately for the automatic selection of optimal feature subsets from the combined color and texture features. The maximum average adulteration level classification accuracy of 93.31% is obtained using the BPNN classification model trained with PCA-based reduced features. The proposed technique can be used as an economic, rapid, non-destructive and quantitative technique for testing adulteration, authenticity, and quality of bulk paddy grain samples.