Crop Disease Detection using Yolo V5 on Raspberry Pi

Abstract

This research paper presents a novel approach for detecting crop diseases using YOLO v5 and Raspberry Pi. The proposed method employs YOLO v5, a state-of-the-art object detection algorithm, to analyse images of crops and detect infected leaves. The results are then processed by a Raspberry Pi, a low-cost and low-power computer, to make predictions about the presence and type of disease. The experiment was conducted on a dataset of crop images, and the results showed that the proposed method achieved high accuracy in detecting and classifying crop diseases. This work demonstrates the potential of using YOLO v5 and Raspberry Pi for efficient and cost-effective disease detection in agriculture, this paper outlines the procedures deployed in the implementation and the different techniques deployed to increase its efficiency.