Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques

Tej Bahadur Shahi, Cheng Yuan Xu, Arjun Neupane, William Guo

Research output: Contribution to journalReview articlepeer-review

44 Citations (Scopus)
237 Downloads (Pure)


Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers and stakeholders for estimating traits such as crop yield and diseases. Early detection of crop disease is essential to prevent possible losses on crop yield and ultimately increasing the benefits. However, accurate estimation of crop disease requires modern data analysis techniques such as machine learning and deep learning. This work aims to review the actual progress in crop disease detection, with an emphasis on machine learning and deep learning techniques using UAV-based remote sensing. First, we present the importance of different sensors and image-processing techniques for improving crop disease estimation with UAV imagery. Second, we propose a taxonomy to accumulate and categorize the existing works on crop disease detection with UAV imagery. Third, we analyze and summarize the performance of various machine learning and deep learning methods for crop disease detection. Finally, we underscore the challenges, opportunities and research directions of UAV-based remote sensing for crop disease detection.

Original languageEnglish
Article number2450
Pages (from-to)1-29
Number of pages29
JournalRemote Sensing
Issue number9
Publication statusPublished - May 2023

Bibliographical note

Funding Information:
The authors would like to acknowledge the Research Training Program (RTP) scholarship funded by the Australian Government and the support and resources provided by CQUniversity. We also express our thankful words to Ram Khadka (Scientist—Plant Pathology, Nepal Agricultural Research Council) for his expert feedback on crop diseases.

Publisher Copyright:
© 2023 by the authors.


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