Hybrid computational intelligence algorithms and their applications to detect food quality

Abstract

Food security is a major problem faced today. With primitive storage facilities, especially in developing countries, it often leads to extensive losses. This work aims to develop algorithms based on vision data to assess the food quality and deploy them in food storage facilities to detect early signs of spoilage. This paper presents various segmentation techniques for finding spoilt food. Novel optimization techniques have been developed and implemented to improve K-means clustering and multilevel thresholding. A hybrid of moth flame optimization (MFO) and gravitational search algorithm (GSA) has been developed. Also, in another hybrid, particle swarm optimization (PSO) was also incorporated along with MFO and GSA. Both the hybrids performed better than the individual algorithms and the MFO–GSA–PSO hybrid performed better than the MFO–GSA hybrid on the benchmark functions. Segmented images using optimized K-means were used for feature extraction using local binary patterns (LBP). Multiclass support vector machine was used for classification which gave an accuracy of 81% for features from segmented images obtained using MFO–GSA hybrid and 83.33% for that using MFO–GSA–PSO hybrid. Results of simple linear iterative clustering superpixels for segmentation have also been discussed. The segmented clusters are then used to judge the rottenness of the food. Classification using LBP and Haralick features of the segmented image obtained using graphs over superpixels gave an accuracy of 81.7% and 78% respectively.