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Plant Disease Classification Using Hybrid Features

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Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1776))

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Abstract

Deep learning has shown remarkable performances in image classification, including those of plants and leaves. However, high-performing networks in terms of accuracy may not be using the salient regions for making the prediction and could be prone to biases. This work proposes a neural network architecture incorporating handcrafted features and fusing them with the learned features. Using hybrid features provides better control and understanding of the feature space while leveraging deep learning capabilities. Furthermore, a new IoU-based metric is introduced to assess the CNN-based classifier’s performance based on the regions focused on making predictions. Experiments over multiple leaf disease datasets demonstrate the performance improvement with the model using hybrid features. Classification using hybrid features performed better than the baseline models in terms of P@1 and also on the IoU-based metric.

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Authors and Affiliations

  1. International Institute of Information Technology, Hyderabad, India

    Vamsidhar Muthireddy & C. V. Jawahar

Authors
  1. Vamsidhar Muthireddy
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  2. C. V. Jawahar
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Correspondence to Vamsidhar Muthireddy .

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Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

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Muthireddy, V., Jawahar, C.V. (2023). Plant Disease Classification Using Hybrid Features. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1776. Springer, Cham. https://doi.org/10.1007/978-3-031-31407-0_36

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  • DOI: https://doi.org/10.1007/978-3-031-31407-0_36

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  • Publisher Name: Springer, Cham

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