A Fruit Ripeness Detection Method using Adapted Deep Learning-based Approach

Abstract

Fruit ripeness detection plays a crucial role in precise agriculture, enabling optimal harvesting and post-harvest handling. Various methods have been investigated in the literature for fruit ripeness detection in vision-based systems, with deep learning approaches demonstrating superior accuracy compared to other approaches. However, the current research challenge lies in achieving high accuracy rates in deep learning-based fruit ripeness detection. In this study proposes a method based on the YOLOv8 algorithm to address this challenge. The proposed method involves generating a model using a custom dataset and conducting training, validation, and testing processes. Experimental results and performance evaluation demonstrate the effectiveness of the proposed method in achieving accurate fruit ripeness detection. The proposed method surpasses existing approaches through extensive experiments and performance analysis, providing a reliable solution for fruit ripeness detection in precise agriculture.