Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods

Proper farming, transportation, and storage processes of Hass avocado are important owing to its recent increase in production, export, and economic activity in Colombia. Since Hass avocado pricing and utility depend on its consumption ripeness, related to changes in skin color, sensory properties,...

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Veröffentlicht in:International journal of food engineering 2020-12, Vol.16 (12)
Hauptverfasser: Jaramillo-Acevedo, César Augusto, Choque-Valderrama, William Enrique, Guerrero-Álvarez, Gloria Edith, Meneses-Escobar, Carlos Augusto
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container_issue 12
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container_title International journal of food engineering
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creator Jaramillo-Acevedo, César Augusto
Choque-Valderrama, William Enrique
Guerrero-Álvarez, Gloria Edith
Meneses-Escobar, Carlos Augusto
description Proper farming, transportation, and storage processes of Hass avocado are important owing to its recent increase in production, export, and economic activity in Colombia. Since Hass avocado pricing and utility depend on its consumption ripeness, related to changes in skin color, sensory properties, texture, and nutritional value, developing an Android mobile application, namely iHass for smartphones and tablets, which estimates the number of days in which the Hass avocado reaches its optimal ripening level during post-harvest storage, contributes toward improving the fruit quality and decreasing the export costs and losses. This study aims to monitor the ripening processes of Hass avocados in complex backgrounds and indoor environments using various digital image processing techniques. The proposed study uses the red, green, and blue color model based on the physical and chemical changes that are observed during the ripening process. Herein, the color, shape, and texture characteristics of the fruits are obtained, and the fruits are classified using an artificial neural network, which features three layers, four input parameters, six hidden neurons, and four output parameters. Furthermore, ripeness was monitored in two crops, which provided 65 samples each. The results provided a ripeness estimate accuracy of 88% and a regression value of 0.819 during the post-harvest period.
doi_str_mv 10.1515/ijfe-2019-0161
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subjects feature selection
fruit classification
image processing
non-destructive methods
title Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods
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