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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
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    References listed on IDEAS

    as
    1. 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).
    2. 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.
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    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.

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