{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T16:16:06Z","timestamp":1774714566545,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,29]],"date-time":"2020-12-29T00:00:00Z","timestamp":1609200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Research Partnership for Sustainable Development (SATREPS), Japan Science and Technology Agency (JST)\/Japan International Cooperation Agency (JICA)","award":["JPMJSA1907"],"award-info":[{"award-number":["JPMJSA1907"]}]},{"name":"Japan Society for the Promotion of Science KAKENHI","award":["JP20H02968 and JP18H02295"],"award-info":[{"award-number":["JP20H02968 and JP18H02295"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Leaf area index (LAI) is a vital parameter for predicting rice yield. Unmanned aerial vehicle (UAV) surveillance with an RGB camera has been shown to have potential as a low-cost and efficient tool for monitoring crop growth. Simultaneously, deep learning (DL) algorithms have attracted attention as a promising tool for the task of image recognition. The principal aim of this research was to evaluate the feasibility of combining DL and RGB images obtained by a UAV for rice LAI estimation. In the present study, an LAI estimation model developed by DL with RGB images was compared to three other practical methods: a plant canopy analyzer (PCA); regression models based on color indices (CIs) obtained from an RGB camera; and vegetation indices (VIs) obtained from a multispectral camera. The results showed that the estimation accuracy of the model developed by DL with RGB images (R2 = 0.963 and RMSE = 0.334) was higher than those of the PCA (R2 = 0.934 and RMSE = 0.555) and the regression models based on CIs (R2 = 0.802-0.947 and RMSE = 0.401\u20131.13), and comparable to that of the regression models based on VIs (R2 = 0.917\u20130.976 and RMSE = 0.332\u20130.644). Therefore, our results demonstrated that the estimation model using DL with an RGB camera on a UAV could be an alternative to the methods using PCA and a multispectral camera for rice LAI estimation.<\/jats:p>","DOI":"10.3390\/rs13010084","type":"journal-article","created":{"date-parts":[[2020,12,29]],"date-time":"2020-12-29T10:04:46Z","timestamp":1609236286000},"page":"84","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice"],"prefix":"10.3390","volume":"13","author":[{"given":"Tomoaki","family":"Yamaguchi","sequence":"first","affiliation":[{"name":"Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan"}]},{"given":"Yukie","family":"Tanaka","sequence":"additional","affiliation":[{"name":"Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan"}]},{"given":"Yuto","family":"Imachi","sequence":"additional","affiliation":[{"name":"Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan"}]},{"given":"Megumi","family":"Yamashita","sequence":"additional","affiliation":[{"name":"Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan"}]},{"given":"Keisuke","family":"Katsura","sequence":"additional","affiliation":[{"name":"Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,29]]},"reference":[{"key":"ref_1","unstructured":"Lahoz, W. 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