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 learningbased 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.

Automated Paper-based Multiple Choice Scoring Framework using Fast Object Detection Algorithm

“…This article addresses the above research limitations by proposing a deep learning method based on the YOLO (You Only Look Once) algorithm to score multiple choice tests accurately. We use YOLOv8 because it stands out as the fastest model with lower parameters compared to the other versions [31]. This study uses a data set of real-life multiple choice test sets and training and testing processes to create a powerful and effective model.…”