Tomato Ripeness Identification Using Recurrent Neural Network Algorithm

Abstract

Tomatoes undergo distinct ripeness stages, typically categorized into ripe, semi-ripe, and unripe phases. Traditional methods for assessing ripeness often face challenges in accuracy due to difficulties in comparing variables and subjective interpretations. This study proposes an innovative approach to classify tomato ripeness using a dataset of 200 tomato images and employs a Recurrent Neural Network (RNN) for precise classification. The experimental results demonstrate that the RNN-based model achieves a 95.0% accuracy rate in identifying ripeness stages, significantly outperforming conventional methods. This high level of accuracy highlights the model's potential to minimize errors and provide reliable assessments of tomato maturity. The proposed method offers a robust and efficient solution for agricultural applications, enabling improved quality control and harvest timing. Future research could explore the integration of additional data sources or advanced machine learning techniques to further enhance the model's performance and applicability across diverse agricultural contexts.