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A Deep Learning Approach to Identify Fresh and Stale Fruits and Vegetables with Yolo

Publication Type : Conference Paper

Publisher : IEEE

Source : 2023 International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), Bangalore, India, 2023, pp. 606-610

Url : https://ieeexplore.ieee.org/document/10170163

Campus : Chennai

School : School of Engineering

Department : Electronics and Communication

Year : 2023

Abstract : Consuming fresh fruits and vegetables is very important not just because they taste good but are also a rich source of vitamins, minerals and dietary fiber. About 20% of the fruits and vegetables that are cultivated for human needs are wasted due to spoilage. Hence, it is important for a farmer to remove the damaged ones so that the other ones do not get spoiled. Identification of fresh fruits and vegetables also helps a person in picking good ones from the shop. This project focuses on identifying if a fruit or vegetable is fresh or stale using two different versions of YOLO namely Yolov4 and Yolov5. Images of six different types of fruits and vegetables: apple, banana, orange, tomato, capsicum and bitter gourd are considered in this work. As there are 6 varieties involved, it is a multiclass classification model with 12 classes. The overall results show that the trained Neural Network achieved a classification accuracy of 95.9% for Yolov4 and 99.9% for Yolov5 on a dataset of 1200 images.

Cite this Research Publication : Sai Shree Akshitha Reddy and Aishwarya N, “A Deep Learning Approach to Identify Fresh and Stale Fruits and Vegetables with Yolo, 2023 International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), Bangalore, India, 2023, pp. 606-610

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