Artificial vision systems (AVS) have become very important in precision agriculture applied to produce high-quality and low-cost foods with high functional characteristics generated through environmental care practices. This article reported the design and implementation of a new fuzzy classification architecture based on the RGB color model with descriptors. Three inputs were used that are associated with the average value of the color components of four views of the tomato; the number of triangular membership functions associated with the components R and B were three and four for the case of component G. The amount of tomato samples used in training were forty and twenty for testing; the training was done using the Matlab© ANFISEDIT. The tomato samples were divided into six categories according to the US Department of Agriculture (USDA). This study focused on optimizing the descriptors of the color space to achieve high precision in the prediction results of the final classification task with an error of 536,995×10-6. The Computer Vision System (CVS) is integrated by an image isolation system with lighting; the image capture system uses a Raspberry Pi 3 and Camera Module Raspberry Pi 2 at a fixed distance and a black background. In the implementation of the CVS, three different color description methods for tomato classification were analyzed and their respective diffuse systems were also designed, two of them using the descriptors described in the literature.
Optical systems are used for analysing the internal composition and the external properties in food. The measurement of the lycopene content in fruits and vegetables is important because of its benefits to human health. Lycopene prevents cardiovascular diseases, cataracts, cancer, osteoporosis, male infertility, and peritonitis. Among the optical systems focused on the estimation and identification of lycopene molecule are high-performance liquid chromatography (HPLC), the colorimeter, infrared near NIR spectroscopy, UV-VIS spectroscopy, Raman spectroscopy, and the systems of multispectral imaging (MSI) and hyperspectral imaging (HSI). The main objective of this paper is to present a review of the current state of optical systems used to measure lycopene in fruits. It also reports important factors to be considered in order to improve the design and implementation of those optical systems. Finally, it was observed that measurements with HPLC and spectrophotometry present the best results but use toxic solvents and require specialized personnel for their use. Moreover, another widely used technique is colorimetry, which correlates the lycopene content using color descriptors, typically those of CIELAB. Likewise, it was identified that spectroscopic techniques and multispectral images are gaining importance because they are fast and non-invasive.
Sweet bell peppers are a Solanaceous fruit belonging to the Capsicum annuum L. 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 flavor. In addition, these fruits have a characteristic flavor 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 by measuring total soluble solids (TSS), ° Brix, 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.
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