Raspberries-LITRP database : RGB images database for the industrial applications of red raspberries’ automatic quality estimation

Abstract: This work presents a free new database designed from a real industrial process to recognize, identify, and classify the quality of the red raspberry accurately, automatically, and in real time. Raspberry trays with recently harvested fresh fruit enter the industry’s selection and quality...

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Autores principales: Quintero-Rincón, Antonio, Mora, Marco, Naranjo Torres, José, Fredes, Claudio, Valenzuela, Andrés
Otros Autores: 0000-0003-0186-4049
Formato: Artículo
Lenguaje:Inglés
Publicado: Multidisciplinary Digital Publishing Institute 2023
Materias:
BASE DE DATOS
FRAMBUESAS
FRUTOS ROJOS
INDUSTRIA ALIMENTARIA
CONTROL DE CALIDAD
Acceso en línea:https://repositorio.uca.edu.ar/handle/123456789/16071
Aporte de:
Repositorio Institucional de la Universidad Católica Argentina (UCA) de Universidad Católica Argentina
id I33-R139-123456789-16071
record_format dspace
institution Universidad Católica Argentina
institution_str I-33
repository_str R-139
collection Repositorio Institucional de la Universidad Católica Argentina (UCA)
language Inglés
topic BASE DE DATOS
FRAMBUESAS
FRUTOS ROJOS
INDUSTRIA ALIMENTARIA
CONTROL DE CALIDAD
spellingShingle BASE DE DATOS
FRAMBUESAS
FRUTOS ROJOS
INDUSTRIA ALIMENTARIA
CONTROL DE CALIDAD
Quintero-Rincón, Antonio
Mora, Marco
Naranjo Torres, José
Fredes, Claudio
Valenzuela, Andrés
Raspberries-LITRP database : RGB images database for the industrial applications of red raspberries’ automatic quality estimation
topic_facet BASE DE DATOS
FRAMBUESAS
FRUTOS ROJOS
INDUSTRIA ALIMENTARIA
CONTROL DE CALIDAD
description Abstract: This work presents a free new database designed from a real industrial process to recognize, identify, and classify the quality of the red raspberry accurately, automatically, and in real time. Raspberry trays with recently harvested fresh fruit enter the industry’s selection and quality control process to be categorized and subsequently their purchase price is determined. This selection is carried out from a sample of a complete batch to evaluate the quality of the raspberry. This database aims to solve one of the major problems in the industry: evaluating the largest amount of fruit possible and not a single sample. This major dataset enables researchers in various disciplines to develop practical machine-learning (ML) algorithms to improve red raspberry quality in the industry, by identifying different diseases and defects in the fruit, and by overcoming limitations by increasing the performance detection rate accuracy and reducing computation time. This database is made up of two packages and can be downloaded free from the Laboratory of Technological Research in Pattern Recognition repository at the Catholic University of the Maule. The RGB image package contains 286 raw original images with a resolution of 3948 2748 pixels from raspberry trays acquired during a typical process in the industry. Furthermore, the labeled images are available with the annotations for two diseases (86 albinism labels and 164 fungus rust labels) and two defects (115 over-ripeness labels, and 244 peduncle labels). The MATLAB code package contains three well-known ML methodological approaches, which can be used to classify and detect the quality of red raspberries. Two are statistical-based learning methods for feature extraction coupled with a conventional artificial neural network (ANN) as a classifier and detector. The first method uses four predictive learning from descriptive statistical measures, such as variance, standard deviation, mean, and median. The second method uses three predictive learning from a statistical model based on the generalized extreme value distribution parameters, such as location, scale, and shape. The third ML approach uses a convolution neural network based on a pre-trained fastest region approach (Faster R-CNN) that extracts its features directly from images to classify and detect fruit quality. The classification performance metric was assessed in terms of true and false positive rates, and accuracy. On average, for all types of raspberries studied, the following accuracies were achieved: Faster R-CNN 91.2%, descriptive statistics 81%, and generalized extreme value 84.5%. These performance metrics were compared to manual data annotations by industry quality control staff, accomplishing the parameters and standards of agribusiness. This work shows promising results, which can shed a new light on fruit quality standards methodologies in the industry.
author2 0000-0003-0186-4049
author_facet 0000-0003-0186-4049
Quintero-Rincón, Antonio
Mora, Marco
Naranjo Torres, José
Fredes, Claudio
Valenzuela, Andrés
format Artículo
author Quintero-Rincón, Antonio
Mora, Marco
Naranjo Torres, José
Fredes, Claudio
Valenzuela, Andrés
author_sort Quintero-Rincón, Antonio
title Raspberries-LITRP database : RGB images database for the industrial applications of red raspberries’ automatic quality estimation
title_short Raspberries-LITRP database : RGB images database for the industrial applications of red raspberries’ automatic quality estimation
title_full Raspberries-LITRP database : RGB images database for the industrial applications of red raspberries’ automatic quality estimation
title_fullStr Raspberries-LITRP database : RGB images database for the industrial applications of red raspberries’ automatic quality estimation
title_full_unstemmed Raspberries-LITRP database : RGB images database for the industrial applications of red raspberries’ automatic quality estimation
title_sort raspberries-litrp database : rgb images database for the industrial applications of red raspberries’ automatic quality estimation
publisher Multidisciplinary Digital Publishing Institute
publishDate 2023
url https://repositorio.uca.edu.ar/handle/123456789/16071
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