Summary
Open Access [Topical Article]
Image-based Classification of Leaf Diseases Using Convolutional Neural Networks
Muhammad Umair Wooi-Haw Tan Yee-Loo Foo
Vol.106 No.10 pp.878-885
Publication Date:2023/10/01
Online ISSN:2188-2355
Print ISSN:0913-5693
Type of Manuscript:Special Section on Artificial Intelligence of Things (AIoT) for Smart Farming
Category:
Keyword:
leaf diseases, image classification, convolutional neural networks,
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Summary:
Smart farming is on the uprising demand all over the world. Artificial Intelligence (AI) is considered as one of the latest tools to be utilized for smart farming. However, the practical implementation of AI for smart farming is often a challenge. One of the challenges is to optimize the algorithms for accurate classification of plant diseases. In this study, we have proposed a Convolutional Neural Network (CNN) for the classification of leaf diseases. The framework of the proposed CNN is designed using the Depthwise Separable Convolution (DWS) technique that consists of two stages, i. e., depthwise and pointwise feature extractions. We have compared the model with the classical convolutional approach. Results show that the proposed model outperformed the conventional CNN model with a precision of 0.932, recall 0.992, F1 score of 0.961 and a test accuracy of 95.25% whereas the conventional model achieved precision 0.941, recall 0.961, F1 score 0.951 and 93.76% of test accuracy.
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Online ISSN:2188-2355