Detection of fungal diseases of plants from leaf images based on neural network technologies

Jamil Alsayaydeh, Jamil Abedalrahim and Fedorchenko, Ievgen and Yusof, Mohd Faizal and Oliinyk, Andrii and Chornobuk, Maksym and Khokhlov, Mykola (2024) Detection of fungal diseases of plants from leaf images based on neural network technologies. International Journal Of Electrical And Computer Engineering (IJECE), 14 (5). pp. 5866-5873. ISSN 2088-8708

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Abstract

The paper addresses the issue of automating the detection of fungal diseases in plants using digital images of their leaves. The spread of diseases among agricultural and horticultural crops causes significant economic losses worldwide, making the development of an effective and affordable solution to this problem highly valuable. Literature analysis suggests the viability of employing a convolutional neural network (CNN) to tackle this issue. The 'Fungus recognition' model was developed based on a custom CNN architecture using the TensorFlow library. The model underwent training and testing on a publicly available dataset. Test results show that 'Fungus recognition' achieves a classification accuracy level of 90%, surpassing similar models considered. The developed model can be adapted for deployment on mobile computing devices, paving the way for its practical implementation in agriculture and horticulture.

Item Type:	Article
Uncontrolled Keywords:	Classification, Convolutional neural network, Machine learning, Machine vision, Pattern recognition, TensorFlow
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	11 Dec 2025 02:39
Last Modified:	11 Dec 2025 02:39
URI:	http://eprints.utem.edu.my/id/eprint/29186
Statistic Details:	View Download Statistic

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