A Maturity Estimation of Bell Pepper (Capsicum annuum L.) by Artificial Vision System for Quality Control

Abstract

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.

Postharvest UV-B and Photoperiod with Blue + Red LEDs as Strategies to Stimulate Carotenogenesis in Bell Peppers

“…Regarding colour variations (∆E), this effect is similar to results reported by Kasim and Kasim, [31], who showed that low UV-B doses (4.46 kJ m -2 ) did not affect the colour development during 6 d at 7 • C. As observed, TSS has been related to MI and, particularly in red peppers, these two parameters have also been associated with colour development [32], which can also be contrasted with our present results. Moreover, sensory quality values can be related to the overall marketability, which has been previously reported to be increased after applying blue and red LEDs [33] or UV low doses [31,34].…”

Salt Stress Differentially Affects the Primary and Secondary Metabolism of Peppers (Capsicum annuum L.) According to the Genotype, Fruit Part, and Salinity Level

“…‘Caro F1’ and ‘Somborka’ both form elongated, cone-shaped fruits that are quite large, and ‘Novosadka’ and ‘Berenyi F1’ form round, small fruits. All four cultivars were harvested at full size, in technological mature stage, as reported by Villaseñor et al [ 15 ].…”

“…Each of the three treatments was repeated twice (two systems for one treatment) so that each contained four plants of the same cultivar. Sampling was performed when the fruits reached a sufficient size, firmness, and color (yellow) [ 15 ]. Each plant (three to four fruits from each plant) represented a separate replicate.…”

Quality Inspection of Food and Agricultural Products using Artificial Intelligence

“…In order to solve the issue of writing code for a particular program to be used in the food quality inspection, fuzzy logic saves the time and effort related to the feature specification. New technologies have been established based on fuzzy logic applications in various food quality detection including sorting of orange (Jhawar, 2016), determination of moisture content in olive oil (Ortega et al, 2016), guava grading based on ripening stages (Kanade & Shaligram, 2018), and maturity estimation of bell peppers (Villaseñor-Aguilar et al, 2020). Kanade and Shaligram (2018) integrated the electronic nose sensor with fuzzy logic to classify guava according to the maturity levels.…”

“…The findings indicated a good performance of the CNN models compared to the conventional methods such as successive projection algorithm and partial least squares. Villaseñor-Aguilar et al (2020) examined the maturity estimation of bell peppers using a fuzzy logic model. It was revealed that the fuzzy logic model obtained an accuracy of 99% in order to classify the maturity level of bell peppers.…”