Nicolás Encalada

This paper elucidates the development of an advanced maturity detection system for cherry tomatoes, employing state-of-the-art computer vision technologies alongside automated irrigation systems. The proposed solution integrates sophisticated software with hardware components, incorporating moisture sensors and solenoid valves to ensure precise irrigation management. Central to this system is the YOLOv8 model, which has been specifically trained to differentiate between ripe and unripe tomatoes. A custom dataset comprising 1,442 images was meticulously curated and processed to facilitate the creation of the detection model utilizing the Roboflow platform. The training of the model was executed on Google Colab, capitalizing on its computational power to enhance model performance. Following comprehensive optimization, the YOLOv8 model attained an accuracy rate of 78.8%, establishing it as a reliable asset for agricultural applications. A user-friendly graphical interface, developed in Python with Tkinter, enables farmers to effectively monitor crop maturity and manage irrigation processes in real time. The validation results underscore the system’s remarkable efficacy in practical agricultural settings, emphasizing its potential to augment productivity and resource efficiency. This system signifies a considerable advancement in innovative agricultural technologies.