Control de maduración de aguacate mediante análisis de imagen

Abstract

The state of maturity of fresh fruits is a fundamental parameter in commercialization or industrial processing. Traditionally, the determination of maturity has been carried out in laboratories, analyzing each fruit according to its nature with a specific methodology and parameters. For the avocado fruit, the firmness or dry matter content is quantified to estimate its maturity, these tests have been adopted for their high reliability and reproducibility. The state of maturity in the fruits is also diagnosed by panels of tasters or expert farmers, leaving room for subjectivity. Agrifood chains have incorporated technology as a support for agriculture 4.0, sustainability and organic production, main pillars of quality and food safety. Non-invasive methods of analysis are technological tools in the food chain that make it possible to reduce errors due to subjectivity and accurately detect parameters not perceptible to the human eye. Image analysis technology allows optical parameters to be quantified and correlated with biological phenomena to automate agricultural processes. This document examines the application of image analysis to control the state of maturity in avocado fruits. Different methodologies used for the prediction of maturity are addressed, mentioning hardware & software, correlation data. The methods developed by researchers differ in their fundamentals, despite this, they maintain high performance values in classification by maturity. The results indicate that, by combining artificial intelligence, peripherals and post-harvest data, the state of maturity can be efficiently predicted by image analysis.