An Efficient Chrominance Based Detection and Segmentation of Rotten Pyrus Malus Using a Cooperative Approach of Hill Climbing Optimization and Modified FCM Clustering

In this study, a novel method for the accurate and reliable identification and segmentation of rotten regions in Pyrus Malus (apples) is presented. In the proposed method, the initial cluster centroids computed from 3D color histogram of CIELab image. These centroids are utilized and processed using Hill Climbing (HC) Optimization to get the finest promising end result. Afterwards, the improved centroids are utilized by Modified FCM (MFCM) clustering method, which effectively divides the apple's rotten regions, if any. This collaborative strategy has a number of benefits: 1. The chrominance exploration increases overall efficacy by reducing susceptibility to changes in lighting. 2. In improving arrangement of FCM, HC optimization enhances optimal cluster centers; consequently, leads to enhanced identification of the rotten area. 3. In conjunction with each other, the combined method helps to make further identification of rotting areas easy and facilitate the creation of computerized classification and quality checks. The results of the experiments give evidence that the mentioned strategy allows for obtaining better precision in identifying and classification of rotten areas in apples in comparison to the approaches based on the use of RGB. This strategy may be the best tool for sorting and grading of apples using the automated grading systems for optimizing the processing of food products in the sector.