Open Access

ARTICLE

Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

by Uzair Aslam Bhatti^1,*, Sibghat Ullah Bazai², Shumaila Hussain¹, Shariqa Fakhar³, Chin Soon Ku^4,*, Shah Marjan⁵, Por Lip Yee⁶, Liu Jing¹

1 College of Information and Communication Engineering, Hainan University, Haikou, 570228, China
2 Department of Computer Engineering, Balochistan University of Information Technology, Engineering, and Management Sciences (BUITEMS), Quetta, Pakistan
3 Department of Computer Science, Sardar Bahadur Khan Women’s University, Quetta, Pakistan
4 Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar, 31900, Malaysia
5 Department of Software Engineering, Balochistan University of Information Technology, Engineering, and Management Sciences (BUITEMS), Quetta, Pakistan
6 Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, 50603, Malaysia

* Corresponding Authors: Uzair Aslam Bhatti. Email: ; Chin Soon Ku. Email:

Computers, Materials & Continua 2023, 77(1), 681-697. https://doi.org/10.32604/cmc.2023.037958

Received 23 November 2022; Accepted 15 June 2023; Issue published 31 October 2023

Abstract

Crop diseases have a significant impact on plant growth and can lead to reduced yields. Traditional methods of disease detection rely on the expertise of plant protection experts, which can be subjective and dependent on individual experience and knowledge. To address this, the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification. In this paper, we propose a novel approach that utilizes a convolutional neural network (CNN) model in conjunction with Inception v3 to identify plant leaf diseases. The research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific diseases. The models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct classes. Through rigorous training and evaluation, the proposed system achieved an impressive accuracy rate of 99%. This mobile application serves as a convenient and valuable advisory tool, providing early detection and guidance in real agricultural environments. The significance of this research lies in its potential to revolutionize plant disease detection and management practices. By automating the identification process through deep learning algorithms, the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual expertise. The integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

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Keywords

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