POST-HARVEST GRADING OF CARICA PAPAYA FRUIT USING IMAGE SEGMENTATION AND SOFT COMPUTING

Abstract

In the era of the computer age, with the development of new technologies, the need to compute with accuracy is increasing. The natural approach for detection of the quality of fruits is done by the experts on the basis of the human eye. Automation of quality analysis of fruits is important in order to reduce human efforts and save time. The paper describes the recent development and application of image analysis and soft computing system in quality evaluation of products in the field of agriculture. Soft computing is a rapid, consistent and objective inspection technique, which has expanded into many diverse industries. Image processing can be used to detect the quality of fruit which includes extraction of morphological features. After feature extraction, feature vectors are formed on which K-Means clustering segmentation process is applied to form clusters. In this paper, we present the framework for Carica papaya grading using the Artificial Bee Colony algorithm (ABC) to classify the papaya fruits from digital images. Our initial experiment on the image features indicates that affected area, shape and textures could be used as the parameters for the ABC algorithm for classifying the papaya fruits into its respective grades. Finally, for papaya grading, a comparison between the performance of GUI using support vector machine (SVM), Naive bays classifier and fuzzy logic is done. In the classification process, an input papaya is classified into two categories of healthy and defected. In all grading steps, SVM classifier gives an accuracy of 93.5%, naive Bayes classifier gives 92% and fuzzy logic gives 86.04% respectively. Moreover, the accuracy of the proposed optimization algorithms including for different papaya fruits image databases is 94.04% respectively.