Multi-spectral Imaging To Detect Artificial Ripening Of Banana: A Comprehensive Empirical Study

Abstract

Naturally, ripened fruits contain essential nutrients, but with the increasing demand and consumer benefits, the artificial ripening of fruits is practiced in recent times in the market chain. Compared to natural ripening, artificial ripening significantly reduces the quality of fruits at the same time, increases the health-related risks. Especially, Calcium Carbide (CaC2), which has the carcinogenic properties are consistently being used as a ripening agent. Considering the significance of this problem, in this paper, we present the multi-spectral imaging approach to acquire the spatial and spectral eight narrow spectrum bands across VIS and NIR wavelength range to detect the artificial ripened banana. To present this study, we introduced our newly constructed multi-spectral images dataset for naturally and artificially ripened banana samples. Further, the extensive set of experimental results computed on our large scale database of 5760 banana samples observes the 94.66% average classification accuracy presenting the significance of using multi-spectral imaging to detect artificially ripened fruits.