{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T23:09:50Z","timestamp":1776467390312,"version":"3.51.2"},"reference-count":124,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T00:00:00Z","timestamp":1683331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs15092450","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T02:03:31Z","timestamp":1683511411000},"page":"2450","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":235,"title":["Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0616-3180","authenticated-orcid":false,"given":"Tej Bahadur","family":"Shahi","sequence":"first","affiliation":[{"name":"School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, Australia"}]},{"given":"Cheng-Yuan","family":"Xu","sequence":"additional","affiliation":[{"name":"Research Institute for Northern Agriculture, Faculty of Science and Technology, Charles Darwin University, Brinkin, NT 0909, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1010-7552","authenticated-orcid":false,"given":"Arjun","family":"Neupane","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3134-3327","authenticated-orcid":false,"given":"William","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1111\/nph.12797","article-title":"Abiotic and biotic stress combinations","volume":"203","author":"Suzuki","year":"2014","journal-title":"New Phytol."},{"key":"ref_2","first-page":"012002","article-title":"Traditional and current-prospective methods of agricultural plant diseases detection: A review","volume":"Volume 951","author":"Khakimov","year":"2022","journal-title":"Proceedings of the IOP Conference Series: Earth and Environmental Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1094\/PDIS-08-18-1373-RE","article-title":"An improved crop scouting technique incorporating unmanned aerial vehicle\u2013assisted multispectral crop imaging into conventional scouting practice for gummy stem blight in watermelon","volume":"103","author":"Kalischuk","year":"2019","journal-title":"Plant Dis."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, Y.M., Ostendorf, B., Gautam, D., Habili, N., and Pagay, V. 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