• Amrutha G S
• Bharath Raj S
• Chandana S R
• Lekhana M Ichangi
• Victor Jeyaseelan D

This project explores the viability of utilizing You Only Look Once version 5 (YOLOv5), a state-of-the-art object detection algorithm, for classifying tomato ripeness. Accurate and automated tomato quality assessment based on ripeness is essential in the agricultural sector for optimizing harvesting, sorting, and pricing. Traditional methods often rely on manual inspection, which is labour-intensive, subjective, and susceptible to human error. This project proposes a deep learning-based approach using YOLOv5 to address these limitations. YOLOv5 offers a good balance between accuracy and real-time performance, making it appropriate for deployment in automated sorting systems. The project will involve creating a comprehensive tomato image dataset encompassing various stages of ripeness (half ripe, green, fully ripe) with diverse lighting conditions, backgrounds, and potential blemishes. This dataset will be utilized for training a YOLOv5 model for identifying and categorizing individual tomatoes within an image while simultaneously assigning a ripeness stage based on colour and morphological features. The project will evaluate the model's performance using metrics like precision. The achievement of this project would demonstrate the possibilities of YOLOv5 for real-time, non-destructive tomato quality assessment based on ripeness, laying the groundwork for its integration into smart agriculture solutions for improved efficiency and reduced waste within the food supply chain

Tomato; YOLOv5; Quality; Classification; Ripeness

"Tomato Quality Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.17-24, May-2024, Available :http://www.jetir.org/papers/JETIRGG06004.pdf

"Tomato Quality Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. pp17-24, May-2024, Available at : http://www.jetir.org/papers/JETIRGG06004.pdf