Citation

Gan, Kok Beng and Teoh, Charis Yi En (2025) An Edge Convolution Neural Network Model for Plant Health Classification Using Camera. International Journal on Robotics, Automation and Sciences, 7 (1). pp. 1-6. ISSN 2682-860X

Text View of An Edge Convolution Neural Network Model for Plant Health Classification Using Camera.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

As per the Food and Agricultural Organization (FAO), plant diseases infect approximately 1.3 billion tonnes of crops. Historically, farmers relied on visual inspection for disease detection and classification. In this study, a Convolutional Neural Network (CNN) with five convolutional layers was used to accurately recognize plant diseases. A deployable CNN model was developed for classifying plant diseases, integrated into a web application with a camera, forming a vision systemintegrated with CNN model. The CNN model was trained using a public dataset comprising 19,384 images of potatoes, peppers, and tomatoes, collected under controlled conditions. These plants were chosen due to their common occurrence in Malaysia.The evaluation metrics F1 score were used to assess the model’s performance. The accuracy and F1-score of the trained model were97.2% and 97%, respectively

Item Type:	Article
Uncontrolled Keywords:	Convolution Neural Network, diseases
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	26 Jun 2025 01:09
Last Modified:	26 Jun 2025 01:09
URII:	http://shdl.mmu.edu.my/id/eprint/14060

Downloads

Edit (login required)