Sistema de clasificación automático de fresas según su calidad empleando inteligencia artificial

Abstract

Strawberry cultivation in Ecuador has significant economic and social relevance. Thanks to their quality, nutritional value, and culinary versatility, the country has positioned itself as an exporter to the United States, Europe, and Latin America, markets that demand high quality standards and advanced technologies. This project proposes an automatic strawberry classification system using artificial intelligence, structured in four stages. The first stage consisted of acquiring images with the rear camera of a Samsung A10 smartphone. These images were labeled using Roboflow and organized into a database. In the second stage, a YOLOv11l model was trained in Google Colab, and Python and libraries such as tkinter (graphical interface), PIL (image visualization), OpenCV (video processing), ultralytics. YOLO (AI model), serial (communication with Arduino), and time to run the AI model, classifying the strawberries into “Extra,” “First,” “Second,” and “Discard.” For efficient system execution, an HP OMEN 15 laptop was used, taking advantage of its NVIDIA GeForce GTX 1660 Ti graphics card, which significantly improves the model's processing speed. The third stage included the construction of an automated sorting machine with Arduino Mega, actuators, and buttons for the physical separation of strawberries. Finally, a user-friendly interface was developed to display results in real time. The system achieved 83.05% sorting efficiency, reducing human error, optimizing processes, and ensuring uniform quality control. This improves the competitiveness of Ecuadorian producers in local and international markets.