Fruit and Vegetable Detection Using Machine Learning on Embedded Systems

Fruit and Vegetable Detection Using Machine Learning on Embedded Systems
Author(s):
Rohan Jairam, Gunadeep MB, Praveen Ajakkanavar, Bivas Bhattacharya
Keywords:
Keywords: Raspberry Pi; Fruit and Vegetable Detection; YOLO v5; Deep Learning
Abstract
This paper presents the results of a deep learning system developed to detect fruits and vegetables using the YOLO v5 model on a Raspberry Pi. The system was tested in a simulated environment, with the goal of detecting objects in real-time and with high accuracy. The model was trained on a dataset of images of different fruits and vegetables, then evaluated using precision-recall metrics. The results show that the YOLO v5 model was able to detect fruits and vegetables with high accuracy with a mean average precision of 99.9%. This system can be used in various applications such as tracking produce in supermarkets, agricultural monitoring, and robotic harvesting. Furthermore, the Raspberry Pi platform provides an economic, energy-efficient, and low-power solution for low-cost, portable, and energy-efficient fruit and vegetable detection.
Article Details
Unique Paper ID: 159092 Publication Volume & Issue: Volume 9, Issue 11 Page(s): 308 - 312
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NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

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