Detection of Tomato Ripening Stages using Yolov3-tiny

TL;DR

This paper presents a lightweight computer vision system using YOLOv3-tiny to detect and classify tomato ripening stages, achieving high accuracy in a custom dataset for agricultural automation.

Contribution

It introduces the application of YOLOv3-tiny for tomato ripeness detection, including hyperparameter tuning, to improve agricultural monitoring.

Findings

Achieved 90% F1-score in ripening stage detection

Demonstrated effectiveness of YOLOv3-tiny for fruit classification

Optimized hyperparameters through grid search

Abstract

One of the most important agricultural products in Mexico is the tomato (Solanum lycopersicum), which occupies the 4th place national most produced product . Therefore, it is necessary to improve its production, building automatic detection system that detect, classify an keep tacks of the fruits is one way to archieve it. So, in this paper, we address the design of a computer vision system to detect tomatoes at different ripening stages. To solve the problem, we use a neural network-based model for tomato classification and detection. Specifically, we use the YOLOv3-tiny model because it is one of the lightest current deep neural networks. To train it, we perform two grid searches testing several combinations of hyperparameters. Our experiments showed an f1-score of 90.0% in the localization and classification of ripening stages in a custom dataset.