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Particle Swarm Optimized Fuzzy Model for the Classification of Banana Ripeness

Ripeness classification is an important task in the postharvest of banana (Musa sp.) to regulate the ripening treatment. In this paper, a fuzzy model is formulated to classify the level of banana fruit into unripe, ripe, and overripe stages. Peak hue and normalized brown area are the features of the region of interest extracted from hue channel and opponent colors of CIELa*b*. In a fuzzy modeling, defining the linguistic labels, mapping it to the appropriate intervals, and constructing the rule base play vital roles. Classification and regression tree algorithm is applied to model the intervals of feature space and rule base for the fuzzy system. The parameters of fuzzy model are tuned with particle swarm optimization technique. The proposed work is evaluated on MUSA database comprising of banana samples at different ripening stages. Experimental results show that the fuzzy model achieves the average classification rate of 93.11%, which outperforms the state-of-the-art algorithms.