A Maturity Estimation of Bell Pepper (<i>Capsicum annuum</i> L.) by Artificial Vision System for Quality Control
Sweet bell peppers are a Solanaceous fruit belonging to the <i>Capsicum annuum</i> 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 additio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/15/5097 |
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| author | Marcos-Jesús Villaseñor-Aguilar Micael-Gerardo Bravo-Sánchez José-Alfredo Padilla-Medina Jorge Luis Vázquez-Vera Ramón-Gerardo Guevara-González Francisco-Javier García-Rodríguez Alejandro-Israel Barranco-Gutiérrez |
| author_facet | Marcos-Jesús Villaseñor-Aguilar Micael-Gerardo Bravo-Sánchez José-Alfredo Padilla-Medina Jorge Luis Vázquez-Vera Ramón-Gerardo Guevara-González Francisco-Javier García-Rodríguez Alejandro-Israel Barranco-Gutiérrez |
| author_sort | Marcos-Jesús Villaseñor-Aguilar |
| collection | DOAJ |
| description | Sweet bell peppers are a Solanaceous fruit belonging to the <i>Capsicum annuum</i> 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. |
| first_indexed | 2024-03-10T18:13:41Z |
| format | Article |
| id | doaj.art-189c34deec49464dac9089dbc1f33bce |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T18:13:41Z |
| publishDate | 2020-07-01 |
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| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-189c34deec49464dac9089dbc1f33bce2023-11-20T07:50:44ZengMDPI AGApplied Sciences2076-34172020-07-011015509710.3390/app10155097A Maturity Estimation of Bell Pepper (<i>Capsicum annuum</i> L.) by Artificial Vision System for Quality ControlMarcos-Jesús Villaseñor-Aguilar0Micael-Gerardo Bravo-Sánchez1José-Alfredo Padilla-Medina2Jorge Luis Vázquez-Vera3Ramón-Gerardo Guevara-González4Francisco-Javier García-Rodríguez5Alejandro-Israel Barranco-Gutiérrez6Doctorado en Ciencias de la Ingeniería, Tecnológico Nacional de México en Celaya, Celaya 38010, MexicoDoctorado en Ciencias de la Ingeniería, Tecnológico Nacional de México en Celaya, Celaya 38010, MexicoDoctorado en Ciencias de la Ingeniería, Tecnológico Nacional de México en Celaya, Celaya 38010, MexicoDoctorado en Ciencias de la Ingeniería, Tecnológico Nacional de México en Celaya, Celaya 38010, MexicoGrupo de Bioingeniería Básica y Aplicada, Facultad de Ingeniería, Faculta de Ingeniería, Universidad Autónoma de Querétaro, El Marques 76265, MexicoDoctorado en Ciencias de la Ingeniería, Tecnológico Nacional de México en Celaya, Celaya 38010, MexicoDoctorado en Ciencias de la Ingeniería, Tecnológico Nacional de México en Celaya, Celaya 38010, MexicoSweet bell peppers are a Solanaceous fruit belonging to the <i>Capsicum annuum</i> 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.https://www.mdpi.com/2076-3417/10/15/5097bell peppermaturityfuzzy logiccomputational vision |
| spellingShingle | Marcos-Jesús Villaseñor-Aguilar Micael-Gerardo Bravo-Sánchez José-Alfredo Padilla-Medina Jorge Luis Vázquez-Vera Ramón-Gerardo Guevara-González Francisco-Javier García-Rodríguez Alejandro-Israel Barranco-Gutiérrez A Maturity Estimation of Bell Pepper (<i>Capsicum annuum</i> L.) by Artificial Vision System for Quality Control Applied Sciences bell pepper maturity fuzzy logic computational vision |
| title | A Maturity Estimation of Bell Pepper (<i>Capsicum annuum</i> L.) by Artificial Vision System for Quality Control |
| title_full | A Maturity Estimation of Bell Pepper (<i>Capsicum annuum</i> L.) by Artificial Vision System for Quality Control |
| title_fullStr | A Maturity Estimation of Bell Pepper (<i>Capsicum annuum</i> L.) by Artificial Vision System for Quality Control |
| title_full_unstemmed | A Maturity Estimation of Bell Pepper (<i>Capsicum annuum</i> L.) by Artificial Vision System for Quality Control |
| title_short | A Maturity Estimation of Bell Pepper (<i>Capsicum annuum</i> L.) by Artificial Vision System for Quality Control |
| title_sort | maturity estimation of bell pepper i capsicum annuum i l by artificial vision system for quality control |
| topic | bell pepper maturity fuzzy logic computational vision |
| url | https://www.mdpi.com/2076-3417/10/15/5097 |
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