IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i20p13610-d948722.html
   My bibliography  Save this article

A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment

Author

Listed:
  • Vinay Gautam

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

  • Naresh K. Trivedi

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

  • Aman Singh

    (Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
    Faculty of Engineering, Universidade Internacional do Cuanza, Estrada Nacional 250, Bairro Kaluapanda, Cuito-Bié 250, Angola)

  • Heba G. Mohamed

    (Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

  • Irene Delgado Noya

    (Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
    Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico)

  • Preet Kaur

    (Electronics Engineering Department, J.C. Bose University of Science and Technology, YMCA (Formerly YMCA UST), Faridabad 121006, Haryana, India)

  • Nitin Goyal

    (Department of Computer Science and Engineering, Central University of Haryana, Mahendragarh 123031, Haryana, India)

Abstract

The paddy crop is the most essential and consumable agricultural produce. Leaf disease impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have been essential in identifying and classifying leaf disease. Deep learning is used to observe patterns in disease in crop leaves. For instance, organizing a crop’s leaf according to its shape, size, and color is significant. To facilitate farmers, this study proposed a Convolutional Neural Networks-based Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural research. In this study, different TL architectures, viz. InceptionV3, VGG16, ResNet, SqueezeNet, and VGG19, were considered to carry out disease detection in paddy plants. The approach started with preprocessing the leaf image; afterward, semantic segmentation was used to extract a region of interest. Consequently, TL architectures were tuned with segmented images. Finally, the extra, fully connected layers of the Deep Neural Network (DNN) are used to classify and identify leaf disease. The proposed model was concerned with the biotic diseases of paddy leaves due to fungi and bacteria. The proposed model showed an accuracy rate of 96.4%, better than state-of-the-art models with different variants of TL architectures. After analysis of the outcomes, the study concluded that the anticipated model outperforms other existing models.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13610-:d:948722
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/20/13610/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/20/13610/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guang Li & Fangfang Liu & Ashutosh Sharma & Osamah Ibrahim Khalaf & Youseef Alotaibi & Abdulmajeed Alsufyani & Saleh Alghamdi, 2021. "Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-13, May.
    2. Ozguven, Mehmet Metin & Adem, Kemal, 2019. "Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    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. 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.

    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. Salil Bharany & Sandeep Sharma & Sumit Badotra & Osamah Ibrahim Khalaf & Youseef Alotaibi & Saleh Alghamdi & Fawaz Alassery, 2021. "Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol," Energies, MDPI, vol. 14(19), pages 1-20, September.
    2. Kuruva Lakshmanna & Neelakandan Subramani & Youseef Alotaibi & Saleh Alghamdi & Osamah Ibrahim Khalafand & Ashok Kumar Nanda, 2022. "Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    3. Shubham Joshi & T.P Anithaashri & Ravi Rastogi & Gaurav Choudhary & Nicola Dragoni, 2022. "IEDA-HGEO: Improved Energy Efficient with Clustering-Based Data Aggregation and Transmission Protocol for Underwater Wireless Sensor Networks," Energies, MDPI, vol. 16(1), pages 1-13, December.
    4. Nishant Jha & Deepak Prashar & Osamah Ibrahim Khalaf & Youseef Alotaibi & Abdulmajeed Alsufyani & Saleh Alghamdi, 2021. "Blockchain Based Crop Insurance: A Decentralized Insurance System for Modernization of Indian Farmers," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    5. 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.
    6. Iwona Jaskulska & Jarosław Kamieniarz & Dariusz Jaskulski & Maja Radziemska & Martin Brtnický, 2023. "Fungicidal Protection as Part of the Integrated Cultivation of Sugar Beet: An Assessment of the Influence on Root Yield in a Long-Term Study," Agriculture, MDPI, vol. 13(7), pages 1-10, July.

    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:jsusta:v:14:y:2022:i:20:p:13610-:d:948722. 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.