IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i6p742-d822901.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/6/742/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/6/742/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mariana Rockenbach de à vila & Raquel Esteban & Miguel Dall Agnol & José F Morán, 2020. "Physiological traits involved in grazing tolerance of alfalfa genotypes," Agricultural Research & Technology: Open Access Journal, Juniper Publishers Inc., vol. 25(2), pages 102-106, November.
    2. Sen Lin & Yucheng Xiu & Jianlei Kong & Chengcai Yang & Chunjiang Zhao, 2023. "An Effective Pyramid Neural Network Based on Graph-Related Attentions Structure for Fine-Grained Disease and Pest Identification in Intelligent Agriculture," Agriculture, MDPI, vol. 13(3), pages 1-20, February.
    3. Goksu Tuysuzoglu & Kokten Ulas Birant & Derya Birant, 2023. "Rainfall Prediction Using an Ensemble Machine Learning Model Based on K-Stars," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    4. Jinzhu Lu & Lijuan Tan & Huanyu Jiang, 2021. "Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification," Agriculture, MDPI, vol. 11(8), pages 1-18, July.
    5. Ewa Ropelewska & Kadir Sabanci & Muhammet Fatih Aslan & Necati Çetin, 2023. "Rapid Detection of Changes in Image Textures of Carrots Caused by Freeze-Drying using Image Processing Techniques and Machine Learning Algorithms," Sustainability, MDPI, vol. 15(8), pages 1-14, April.
    6. Ewa Ropelewska & Ahmed M. Rady & Nicholas J. Watson, 2023. "Apricot Stone Classification Using Image Analysis and Machine Learning," Sustainability, MDPI, vol. 15(12), pages 1-14, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:6:p:742-:d:822901. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.