IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i4p580-d748265.html
   My bibliography  Save this article

Enhanced Convolutional Neural Network Model for Cassava Leaf Disease Identification and Classification

Author

Listed:
  • Umesh Kumar Lilhore

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India)

  • Agbotiname Lucky Imoize

    (Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria
    Department of Electrical Engineering and Information Technology, Institute of Digital Communication, Ruhr University, 44801 Bochum, Germany)

  • Cheng-Chi Lee

    (Research and Development Center for Physical Education, Health, and Information Technology, Department of Library and Information Science, Fu Jen Catholic Univesity, New Taipei 24205, Taiwan
    Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan)

  • Sarita Simaiya

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India)

  • Subhendu Kumar Pani

    (Krupajal Engineering College, Biju Patnaik University of Technology (BPUT), Rourkela 751002, India)

  • Nitin Goyal

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India)

  • Arun Kumar

    (Panipat Institute of Engineering and Technology, Panipat, Samalkha 132102, India)

  • Chun-Ta Li

    (Department of Information Management, Tainan University of Technology, 529 Zhongzheng Road, Tainan 710302, Taiwan)

Abstract

Cassava is a crucial food and nutrition security crop cultivated by small-scale farmers and it can survive in a brutal environment. It is a significant source of carbohydrates in African countries. Sometimes, Cassava crops can be infected by leaf diseases, affecting the overall production and reducing farmers’ income. The existing Cassava disease research encounters several challenges, such as poor detection rate, higher processing time, and poor accuracy. This research provides a comprehensive learning strategy for real-time Cassava leaf disease identification based on enhanced CNN models (ECNN). The existing Standard CNN model utilizes extensive data processing features, increasing the computational overhead. A depth-wise separable convolution layer is utilized to resolve CNN issues in the proposed ECNN model. This feature minimizes the feature count and computational overhead. The proposed ECNN model utilizes a distinct block processing feature to process the imbalanced images. To resolve the color segregation issue, the proposed ECNN model uses a Gamma correction feature. To decrease the variable selection process and increase the computational efficiency, the proposed ECNN model uses global average election polling with batch normalization. An experimental analysis is performed over an online Cassava image dataset containing 6256 images of Cassava leaves with five disease classes. The dataset classes are as follows: class 0: “Cassava Bacterial Blight (CBB)”; class 1: “Cassava Brown Streak Disease (CBSD)”; class 2: “Cassava Green Mottle (CGM)”; class 3: “Cassava Mosaic Disease (CMD)”; and class 4: “Healthy”. Various performance measuring parameters, i.e., precision, recall, measure, and accuracy, are calculated for existing Standard CNN and the proposed ECNN model. The proposed ECNN classifier significantly outperforms and achieves 99.3% accuracy for the balanced dataset. The test findings prove that applying a balanced database of images improves classification performance.

Suggested Citation

  • Umesh Kumar Lilhore & Agbotiname Lucky Imoize & Cheng-Chi Lee & Sarita Simaiya & Subhendu Kumar Pani & Nitin Goyal & Arun Kumar & Chun-Ta Li, 2022. "Enhanced Convolutional Neural Network Model for Cassava Leaf Disease Identification and Classification," Mathematics, MDPI, vol. 10(4), pages 1-19, February.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:580-:d:748265
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/4/580/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/4/580/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Deepalakshmi P. & Prudhvi Krishna T. & Siri Chandana S. & Lavanya K. & Parvathaneni Naga Srinivasu, 2021. "Plant Leaf Disease Detection Using CNN Algorithm," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 12(1), pages 1-21, January.
    2. Mohamed Loey & Ahmed ElSawy & Mohamed Afify, 2020. "Deep Learning in Plant Diseases Detection for Agricultural Crops: A Survey," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 11(2), pages 41-58, April.
    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. Yunlong Ding & Di-Rong Chen, 2023. "Optimization Based Layer-Wise Pruning Threshold Method for Accelerating Convolutional Neural Networks," Mathematics, MDPI, vol. 11(15), pages 1-13, July.
    2. Balaji Natesan & Anandakumar Singaravelan & Jia-Lien Hsu & Yi-Hsien Lin & Baiying Lei & Chuan-Ming Liu, 2022. "Channel–Spatial Segmentation Network for Classifying Leaf Diseases," Agriculture, MDPI, vol. 12(11), pages 1-20, November.

    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.

      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:jmathe:v:10:y:2022:i:4:p:580-:d:748265. 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.