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A Mobile-Based System for Detecting Ginger Leaf Disorders Using Deep Learning

Author

Listed:
  • Hamna Waheed

    (University Institute of Information Technology, Pir Mehr Ali Shah, Arid Agriculture University—PMAS AAUR, Rawalpindi 46000, Pakistan)

  • Waseem Akram

    (Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi 127788, United Arab Emirates)

  • Saif ul Islam

    (Department of Computer Science, Institute of Space Technology, Islamabad 44000, Pakistan)

  • Abdul Hadi

    (University Institute of Information Technology, Pir Mehr Ali Shah, Arid Agriculture University—PMAS AAUR, Rawalpindi 46000, Pakistan)

  • Jalil Boudjadar

    (Department of Electrical and Computer Engineering, Aarhus University, 8200 Aarhus, Denmark)

  • Noureen Zafar

    (University Institute of Information Technology, Pir Mehr Ali Shah, Arid Agriculture University—PMAS AAUR, Rawalpindi 46000, Pakistan)

Abstract

The agriculture sector plays a crucial role in supplying nutritious and high-quality food. Plant disorders significantly impact crop productivity, resulting in an annual loss of 33%. The early and accurate detection of plant disorders is a difficult task for farmers and requires specialized knowledge, significant effort, and labor. In this context, smart devices and advanced artificial intelligence techniques have significant potential to pave the way toward sustainable and smart agriculture. This paper presents a deep learning-based android system that can diagnose ginger plant disorders such as soft rot disease, pest patterns, and nutritional deficiencies. To achieve this, state-of-the-art deep learning models were trained on a real dataset of 4,394 ginger leaf images with diverse backgrounds. The trained models were then integrated into an Android-based mobile application that takes ginger leaf images as input and performs the real-time detection of crop disorders. The proposed system shows promising results in terms of accuracy, precision, recall, confusion matrices, computational cost, Matthews correlation coefficient (MCC), mAP, and F1-score.

Suggested Citation

  • Hamna Waheed & Waseem Akram & Saif ul Islam & Abdul Hadi & Jalil Boudjadar & Noureen Zafar, 2023. "A Mobile-Based System for Detecting Ginger Leaf Disorders Using Deep Learning," Future Internet, MDPI, vol. 15(3), pages 1-23, February.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:3:p:86-:d:1075078
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    References listed on IDEAS

    as
    1. Khadijeh Alibabaei & Eduardo Assunção & Pedro D. Gaspar & Vasco N. G. J. Soares & João M. L. P. Caldeira, 2022. "Real-Time Detection of Vine Trunk for Robot Localization Using Deep Learning Models Developed for Edge TPU Devices," Future Internet, MDPI, vol. 14(7), pages 1-16, June.
    2. Hamna Waheed & Noureen Zafar & Waseem Akram & Awais Manzoor & Abdullah Gani & Saif ul Islam, 2022. "Deep Learning Based Disease, Pest Pattern and Nutritional Deficiency Detection System for “Zingiberaceae” Crop," Agriculture, MDPI, vol. 12(6), pages 1-17, May.
    3. Bulent Tugrul & Elhoucine Elfatimi & Recep Eryigit, 2022. "Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review," Agriculture, MDPI, vol. 12(8), pages 1-21, August.
    4. Haiqing Wang & Shuqi Shang & Dongwei Wang & Xiaoning He & Kai Feng & Hao Zhu, 2022. "Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model," Agriculture, MDPI, vol. 12(7), pages 1-23, June.
    Full references (including those not matched with items on IDEAS)

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