{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T02:42:58Z","timestamp":1775097778687,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,29]],"date-time":"2021-06-29T00:00:00Z","timestamp":1624924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["18K14452"],"award-info":[{"award-number":["18K14452"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The OGAWA Science and Technology Foundation Research Grant","award":["-"],"award-info":[{"award-number":["-"]}]},{"DOI":"10.13039\/501100003993","name":"Ministry of Agriculture, Forestry and Fisheries","doi-asserted-by":"publisher","award":["JP J008719"],"award-info":[{"award-number":["JP J008719"]}],"id":[{"id":"10.13039\/501100003993","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The plant density of soybean is a critical factor affecting plant canopy structure and yield. Predicting the spatial variability of plant density would be valuable for improving agronomic practices. The objective of this study was to develop a model for plant density measurement using several data sets with different spatial resolutions, including unmanned aerial vehicle (UAV) imagery, PlanetScope satellite imagery, and climate data. The model establishment process includes (1) performing the high-throughput measurement of actual plant density from UAV imagery with the You Only Look Once version 3 (YOLOv3) object detection algorithm, which was further treated as a response variable of the estimation models in the next step, and (2) developing regression models to estimate plant density in the extended areas using various combinations of predictors derived from PlanetScope imagery and climate data. Our results showed that the YOLOv3 model can accurately measure actual soybean plant density from UAV imagery data with a root mean square error (RMSE) value of 0.96 plants m\u22122. Furthermore, the two regression models, partial least squares and random forest (RF), successfully expanded the plant density prediction areas with RMSE values ranging from 1.78 to 3.67 plant m\u22122. Model improvement was conducted using the variable importance feature in RF, which improved prediction accuracy with an RMSE value of 1.72 plant m\u22122. These results demonstrated that the established model had an acceptable prediction accuracy for estimating plant density. Although the model could not often evaluate the within-field spatial variability of soybean plant density, the predicted values were sufficient for informing the field-specific status.<\/jats:p>","DOI":"10.3390\/rs13132548","type":"journal-article","created":{"date-parts":[[2021,6,29]],"date-time":"2021-06-29T22:39:43Z","timestamp":1625006383000},"page":"2548","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Machine Learning Techniques to Predict Soybean Plant Density Using UAV and Satellite-Based Remote Sensing"],"prefix":"10.3390","volume":"13","author":[{"given":"Luthfan Nur","family":"Habibi","sequence":"first","affiliation":[{"name":"Graduate School of Natural Science and Technology, Gifu University, Gifu 5011193, Japan"}]},{"given":"Tomoya","family":"Watanabe","sequence":"additional","affiliation":[{"name":"Graduate School of Mathematics, Kyushu University, Fukuoka 8190395, Japan"}]},{"given":"Tsutomu","family":"Matsui","sequence":"additional","affiliation":[{"name":"Faculty of Applied Biological Sciences, Gifu University, Gifu 5011193, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7116-6962","authenticated-orcid":false,"given":"Takashi S. T.","family":"Tanaka","sequence":"additional","affiliation":[{"name":"Faculty of Applied Biological Sciences, Gifu University, Gifu 5011193, Japan"},{"name":"Artificial Intelligence Advanced Research Center, Gifu University, Gifu 5011193, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"757","DOI":"10.2135\/cropsci2000.403757x","article-title":"Optimizing Soybean Plant Population for a Short-Season Production System in the Southern USA","volume":"40","author":"Ball","year":"2000","journal-title":"Crop Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/S0378-4290(01)00212-X","article-title":"Physiological response of soybean genotypes to plant density","volume":"74","author":"Gan","year":"2002","journal-title":"Field Crops Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.2135\/cropsci2009.02.0063","article-title":"New and Old Soybean Cultivar Responses to Plant Density and Intercepted Light","volume":"49","author":"Pedersen","year":"2009","journal-title":"Crop Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"125972","DOI":"10.1016\/j.eja.2019.125972","article-title":"Analysis of soybean germination, emergence, and prediction of a possible northward establishment of the crop under climate change","volume":"113","author":"Lamichhane","year":"2020","journal-title":"Eur. 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