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Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach

Author

Listed:
  • Ammar Kamal Abasi

    (Department of Machine Learning, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi P.O. Box 131818, United Arab Emirates)

  • Sharif Naser Makhadmeh

    (Department of Data Science and Artificial Intelligence, University of Petra, Amman P.O. Box 11196, Jordan)

  • Osama Ahmad Alomari

    (Department of Computer Science and Information Technology, College of Engineering, Abu Dhabi University, Abu Dhabi P.O. Box 59911, United Arab Emirates)

  • Mohammad Tubishat

    (College of Technological Innovation, Zayed University, Abu Dhabi P.O. Box 144534, United Arab Emirates)

  • Husam Jasim Mohammed

    (Department of Business Administration, College of Administration and Financial Sciences, Imam Ja’afar Al-Sadiq University, Baghdad 10001, Iraq)

Abstract

In modern agriculture, correctly identifying rice leaf diseases is crucial for maintaining crop health and promoting sustainable food production. This study presents a detailed methodology to enhance the accuracy of rice leaf disease classification. We achieve this by employing a Convolutional Neural Network (CNN) model specifically designed for rice leaf images. The proposed method achieved an accuracy of 0.914 during the final epoch, demonstrating highly competitive performance compared to other models, with low loss and minimal overfitting. A comparison was conducted with Transfer Learning Inception-v3 and Transfer Learning EfficientNet-B2 models, and the proposed method showed superior accuracy and performance. With the increasing demand for precision agriculture, models like the proposed one show great potential in accurately detecting and managing diseases, ultimately leading to improved crop yields and ecological sustainability.

Suggested Citation

  • Ammar Kamal Abasi & Sharif Naser Makhadmeh & Osama Ahmad Alomari & Mohammad Tubishat & Husam Jasim Mohammed, 2023. "Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach," Sustainability, MDPI, vol. 15(20), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:15039-:d:1262792
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    References listed on IDEAS

    as
    1. Poonam Dhiman & Amandeep Kaur & Yasir Hamid & Eatedal Alabdulkreem & Hela Elmannai & Nedal Ababneh, 2023. "Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing," Sustainability, MDPI, vol. 15(5), pages 1-18, March.
    2. Vinay Gautam & Naresh K. Trivedi & Aman Singh & Heba G. Mohamed & Irene Delgado Noya & Preet Kaur & Nitin Goyal, 2022. "A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    3. He Liu & Yuduo Cui & Jiamu Wang & Helong Yu, 2023. "Analysis and Research on Rice Disease Identification Method Based on Deep Learning," Sustainability, MDPI, vol. 15(12), pages 1-13, June.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Jianjun Huang & Jindong Xu & Weiqing Yan & Peng Wu & Haihua Xing, 2023. "Detection of Black and Odorous Water in Gaofen-2 Remote Sensing Images Using the Modified DeepLabv3+ Model," Sustainability, MDPI, vol. 16(1), pages 1-21, December.

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