Journal of Intelligent Systems and Internet of Things

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https://doi.org/10.54216/JISIoT

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2690-6791ISSN (Online) 2769-786XISSN (Print)

Volume 16 , Issue 2 , PP: 246-256, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Efficient Plant Disease Detection Using Lightweight Deep Learning Model

Abdalrahman Fatikhan Ataalla 1 * , Karam Hatem Alkhater 2 , Qusay Hatem Alsultan 3 , Zaid Sami Mohsen 4 , Munther Naif Thiyab 5 , Mohammed Waheeb Hamad 6 , Ahmed Jumaah Yas 7

  • 1 Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq - (engrahumi@uoa.edu.iq)
  • 2 Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq - (Karamkhater.92@gmail.com)
  • 3 Renewable Energy Research Center, University of Anbar, Al Anbar, 31001, Iraq - ( qusay.alsultan@uoanbar.edu.iq)
  • 4 Department of Computer Science and Information Technology, College of Science, University of Hilla, 51001 Babil, Iraq - (zaid.sami2020@gmail.com)
  • 5 Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq - (whybmhmd627@gmail.com)
  • 6 College of Applied Sciences – Heet, University of Anbar, Al Anbar, 31001, Iraq - (whybmhmd627@gmail.com)
  • 7 College of computer science and IT, University of Anbar, Al-Ramdi, Iraq - (ahm.jumaah@uoanbar.edu.iq)
  • Doi: https://doi.org/10.54216/JISIoT.160218

    Received: January 07, 2025 Revised: February 06, 2025 Accepted: March 04, 2025
    Abstract

    Early detection of plant diseases is critical to minimizing their adverse effects on agricultural productivity. In particular, machine vision and deep learning approaches (e.g., convolutional neural networks, CNNs) have been increasingly applied for automatic plant disease identification. Although existing deep learning models achieve satisfying classification accuracy, they often consist of millions of parameters that significantly lead to the lengthy training time, prohibitive calculation costs and deployment obstacles at the resource-constrained edge devices. In order to overcome those constraints, we introduce a new deep learning architecture, which uses adaptations of Inception layers and residual connections that can help both with feature extraction and efficiency. In addition, depthwise separable convolutions are used to drastically reduce the amount of trainable parameters with small loss of representational power. We perform training and evaluation of the proposed model on three located benchmark plant disease datasets, PlantVillage dataset, the Rice Disease dataset. Experimental results show that our model achieves state-of-the-art classification accuracy of 99.39% on the PlantVillage dataset, 98.66% on the Rice Disease dataset. In contrast to the state-of-the-art deep learning models, our method obtains higher accuracy with fewer parameters so that it could be better suited for real-time applications on mobile and embedded systems. We explore an application of deep learning with the use of optimized architectures and present the viability of this technique in precision agriculture for faster and more accurate diagnosis of diseases in plants with lower computational load.

    Keywords :

    Diseases Plant , Deep Learning , convolutional neural networks , PlantVillage dataset

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    Cite This Article As :
    Fatikhan, Abdalrahman. , Hatem, Karam. , Hatem, Qusay. , Sami, Zaid. , Naif, Munther. , Waheeb, Mohammed. , Jumaah, Ahmed. Efficient Plant Disease Detection Using Lightweight Deep Learning Model. Journal of Intelligent Systems and Internet of Things, vol. , no. , 2025, pp. 246-256. DOI: https://doi.org/10.54216/JISIoT.160218
    Fatikhan, A. Hatem, K. Hatem, Q. Sami, Z. Naif, M. Waheeb, M. Jumaah, A. (2025). Efficient Plant Disease Detection Using Lightweight Deep Learning Model. Journal of Intelligent Systems and Internet of Things, (), 246-256. DOI: https://doi.org/10.54216/JISIoT.160218
    Fatikhan, Abdalrahman. Hatem, Karam. Hatem, Qusay. Sami, Zaid. Naif, Munther. Waheeb, Mohammed. Jumaah, Ahmed. Efficient Plant Disease Detection Using Lightweight Deep Learning Model. Journal of Intelligent Systems and Internet of Things , no. (2025): 246-256. DOI: https://doi.org/10.54216/JISIoT.160218
    Fatikhan, A. , Hatem, K. , Hatem, Q. , Sami, Z. , Naif, M. , Waheeb, M. , Jumaah, A. (2025) . Efficient Plant Disease Detection Using Lightweight Deep Learning Model. Journal of Intelligent Systems and Internet of Things , () , 246-256 . DOI: https://doi.org/10.54216/JISIoT.160218
    Fatikhan A. , Hatem K. , Hatem Q. , Sami Z. , Naif M. , Waheeb M. , Jumaah A. [2025]. Efficient Plant Disease Detection Using Lightweight Deep Learning Model. Journal of Intelligent Systems and Internet of Things. (): 246-256. DOI: https://doi.org/10.54216/JISIoT.160218
    Fatikhan, A. Hatem, K. Hatem, Q. Sami, Z. Naif, M. Waheeb, M. Jumaah, A. "Efficient Plant Disease Detection Using Lightweight Deep Learning Model," Journal of Intelligent Systems and Internet of Things, vol. , no. , pp. 246-256, 2025. DOI: https://doi.org/10.54216/JISIoT.160218