An Edge Convolution Neural Network Model for Plant Health Classification Using Camera
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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 system integrated 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 were 97.2% and 97%, respectively.
Manuscript received: 26 Nov 2024 | Revised: 3 Jan 2025 | Accepted: 11 Jan 2025 | Published:: 31 Mar 2025
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