A maturity estimation of bell pepper (capsicum annuum l.) by artifcial vision system for quality control

Abstract

Sweet bell peppers are a Solanaceous fruit belonging to the Capsicum annuumL. species whose consumption is popular in world gastronomy due to its wide variety of colors (ranging green, yellow orange, red, and purple), shapes, and sizes and the absence of spicy favor. In addition, these fruits have a characteristic favor and nutritional attributes that include ascorbic acid, polyphenols, and carotenoids. A quality criterion for the harvest of this fruit is maturity; this attribute is visually determined by the consumer when verifying the color of the fruit’s pericarp. The present work proposes an artificial vision system that automatically describes ripeness levels of the bell pepper and compares the Fuzzy logic (FL) and Neuronal Networks for the classification stage. In this investigation, maturity stages of bell peppers were referenced y measuring total soluble solids (TSS), ◦ Brix, and using refractometry. The proposed method was integrated in four stages. The first one consists in the image acquisition of five views using the Raspberry Pi 5 Megapixel camera. The second one is the segmentation of acquired image samples, where background and noise are removed from each image. The third phase is the segmentation of the regions of interest (green, yellow, orange and red) using the connect components algorithm to select areas. The last phase is the classification, which outputs the maturity stage. The classificatory was designed using Matlab’s Fuzzy Logic Toolbox and Deep Learning Toolbox. Its implementation was carried out onto Raspberry Pi platform. It tested the maturity classifier models using neural networks (RBF-ANN) and fuzzy logic models (ANFIS) with an accuracy of 100% and 88%, respectively. Finally, it was constructed with a content of ◦ Brix prediction model with small improvements regarding the state of art