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Deep Learning Based Disease, Pest Pattern and Nutritional Deficiency Detection System for “Zingiberaceae” Crop

Author

Listed:
  • Hamna Waheed

    (Department of Computer Science, Pir Mehr Ali Shah Arid Agriculture University-PMAS AAUR, Rawalpindi 46000, Pakistan)

  • Noureen Zafar

    (Department of Computer Science, Pir Mehr Ali Shah Arid Agriculture University-PMAS AAUR, Rawalpindi 46000, Pakistan)

  • Waseem Akram

    (Department of Informatics, Modeling, Electronics, and Systems (DIMES), University of Calabria, 87036 Rende, Italy)

  • Awais Manzoor

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan)

  • Abdullah Gani

    (Faculty of Computing and Informatics, University Malaysia Sabah, Labuan 88400, Malaysia)

  • Saif ul Islam

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

Abstract

Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired production rate. Deep learning-based methods are helpful for the identification and classification of problems in this domain. This paper presents deep artificial neural network and deep learning-based methods for the early detection of diseases, pest patterns, and nutritional deficiencies. We have used a real-field dataset consisting of healthy and affected ginger plant leaves. The results show that the convolutional neural network (CNN) has achieved the highest accuracy of 99 % for disease rhizomes detection. For pest pattern leaves, VGG-16 models showed the highest accuracy of 96 % . For nutritional deficiency-affected leaves, ANN has achieved the highest accuracy ( 96 % ). The experimental results achieved are comparable with other existing techniques in the literature. In addition, the results demonstrated the potential in improving the yield of ginger using the proposed disease detection methods and an essential consideration for the design of real-time disease detection applications. However, the results are specific to the dataset used in this work and may yield different results for the other datasets.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:6:p:742-:d:822901
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    References listed on IDEAS

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    1. Feng Qin & Dongxia Liu & Bingda Sun & Liu Ruan & Zhanhong Ma & Haiguang Wang, 2016. "Identification of Alfalfa Leaf Diseases Using Image Recognition Technology," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-26, December.
    2. Ewa Ropelewska & Xiang Cai & Zhan Zhang & Kadir Sabanci & Muhammet Fatih Aslan, 2022. "Benchmarking Machine Learning Approaches to Evaluate the Cultivar Differentiation of Plum ( Prunus domestica L.) Kernels," Agriculture, MDPI, vol. 12(2), pages 1-12, February.
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    Cited by:

    1. Xin Zuo & Jiao Chu & Jifeng Shen & Jun Sun, 2022. "Multi-Granularity Feature Aggregation with Self-Attention and Spatial Reasoning for Fine-Grained Crop Disease Classification," Agriculture, MDPI, vol. 12(9), pages 1-22, September.
    2. 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.

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