Development of an Algorithm with Artificial Vision using Convolutional Neural Networks for Blueberry Quality Control

León León R.A.; Rentería Davila M.A. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2024) · DOI: 10.18687/leird2024.1.1.854

Resumen

The paper presents the development of a computer vision algorithm using convolutional neural networks for blueberry quality control. The YOLOv8 network was used for the detection and classification of blueberries according to their quality. A total of 840 blueberry images were collected and labeled using the Roboflow platform. After training and evaluating the model, an accuracy between 91% and 98%, and F1-Scores between 90% and 97% were obtained in classifying blueberries as good or bad in seven different production areas. The results demonstrate the effectiveness of the YOLOv8-based machine vision system for accurate detection of blueberry quality, optimizing the sorting process and reducing human intervention.

Instituciones: Universidad Privada del Norte