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Systematic review of deep learning techniques in plant disease detection

Author

Listed:
  • M. Nagaraju

    (Lovely Professional University)

  • Priyanka Chawla

    (Lovely Professional University)

Abstract

Automatic identification of diseases through hyperspectral images is a very critical and primary challenge for sustainable farming and gained the attention of researchers during the past few years. The technologies proposed, and techniques adopted so far are slighted in their scope and utterly contingent on deep learning models. The performance of convolutional neural networks is emerging as the most powerful tool to diagnose and predict the infections from the crop images. The present article has reviewed some of the existing neural network's techniques that are used to process image data with prominence on detecting crop diseases. First, a review of data acquisition sources, deep learning models/architectures, and different image processing techniques used to process the imaging data provided. Second, the study highlighted the results acquired from the evaluation of various existing deep learning models and finally mentioned the future scope for hyperspectral data analysis. The preparation of this survey is to allow future research to learn larger capabilities of deep learning while detecting plant diseases by improving system performance and accuracy.

Suggested Citation

  • M. Nagaraju & Priyanka Chawla, 0. "Systematic review of deep learning techniques in plant disease detection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-14.
    • Handle: RePEc:spr:ijsaem:v::y::i::d:10.1007_s13198-020-00972-1
    DOI: 10.1007/s13198-020-00972-1
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    References listed on IDEAS

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    1. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
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