Artificial vision strategy for Ripeness assessment of Blueberries on Images taken during Pre-harvest stage in Agroindustrial Environments using Deep Learning Techniques

Cubas Munoz P.B.; Huaman Kemper R.J.; Prado Gardini S.R. · Proceedings of the 2023 IEEE 30th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2023 (2023) · DOI: 10.1109/intercon59652.2023.10326058

Resumen

This article introduces a methodology aimed at detecting and categorizing blueberries within an agro-industrial farm. The methodology employs a combination of YOLOv8 and a custom convolutional neural network. The process initiates with the creation of a dataset used for training and validating the proposed approach. Subsequently, the manual generation of masks for blueberries in the captured images is carried out, ensuring precise labeling that corresponds to five distinct ripeness levels. A model is developed to detect and classify the blueberries based on their respective ripeness levels. The evaluation of these models demonstrates a notable accuracy of 91.14% in the task of classifying the blueberries.

Instituciones: Universidad Privada Antenor Orrego