Abstract
Abstract 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.
Highlights
Worldwide, there is a growing trend toward the consumption of fruits and vegetables, due to their great contribution in vitamins, minerals, dietary fibers and bioactive components that help the proper functioning of the human body, provide a more balanced diet and prevent diseases in humans [1, 2].The freshness and maturity are factors related to the taste and aroma at the time of consumption; maturity refers to the point of highest edible quality [3] and is generally determined by visual inspection, relating pigment changes in the skin [4]
This study aims to monitor the ripening processes of Hass avocados in complex backgrounds and indoor environments using various digital image processing techniques
The artificial neural network (ANN) classification technique together with digital image processing (DIP) provide an intelligent system for the development of automated systems, which allows distinguishing fruits according to their type, variety, maturity and integrity [8]
Summary
There is a growing trend toward the consumption of fruits and vegetables, due to their great contribution in vitamins, minerals, dietary fibers and bioactive components that help the proper functioning of the human body, provide a more balanced diet and prevent diseases in humans [1, 2].The freshness and maturity are factors related to the taste and aroma at the time of consumption; maturity refers to the point of highest edible quality [3] and is generally determined by visual inspection, relating pigment changes in the skin [4]. While vision is a method used by the human brain for the physical classification of food [5], it is subjective and inconsistent, which generates the need to search for tools for the precise, rapid, non-destructive and objective determination of maturity. The techniques based on the analysis and processing of images have different applications in the food industry. They allow to determine the quality of fruits with high precision [6] and with a wide use in the determination of maturity in fruits [7]. The artificial neural network (ANN) classification technique together with digital image processing (DIP) provide an intelligent system for the development of automated systems, which allows distinguishing fruits according to their type, variety, maturity and integrity [8]
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