Performance of Yolov8 Algorithm and Real-Time Detection Transformer in Tomato Ripeness Detection System

Intan Safira; Wahyu Fuadi; Lidya Rosnita

doi:10.59934/jaiea.v5i3.2429

Authors

Intan Safira Universitas Malikussaleh
Wahyu Fuadi Universitas Malikussaleh
Lidya Rosnita Universitas Malikussaleh

DOI:

https://doi.org/10.59934/jaiea.v5i3.2429

Keywords:

YOLOv8, RT-DETR, object detection, tomato ripeness, computer vision

Abstract

Tomato ripeness sorting is still widely carried out manually and subjectively, which can lead to inconsistencies in the quality of the sorting results. In addition, the manual process requires more time and has the potential to cause errors in classifying tomato ripeness levels. Therefore, an automatic detection system based on digital images is needed to provide more accurate and consistent detection results. This study aims to analyze and compare the performance of the You Only Look Once (YOLOv8) and Real-Time Detection Transformer (RT-DETR) algorithms in detecting and classifying tomato ripeness levels based on digital images. The research method used is an experimental method consisting of dataset collection, data labeling, image augmentation, data splitting into training, validation, and testing sets, as well as model training using Google Colab. The tomato ripeness levels were classified into six classes to provide a more detailed representation compared to previous studies. Model performance evaluation was carried out using accuracy, precision, and recall metrics. The results showed that YOLOv8 achieved a precision value of 64.8%, recall of 70%, and accuracy of 50.7%. Meanwhile, RT-DETR demonstrated better performance with a precision of 80.8%, recall of 84%, and accuracy of 70%. Based on these results, RT-DETR is considered superior in providing more accurate and consistent predictions, making it more potential to be implemented in a digital image-based tomato sorting system to improve the efficiency and quality of agricultural products.

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