{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:29:44Z","timestamp":1775230184396,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T00:00:00Z","timestamp":1644278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001700","name":"Ministry of Education, Culture, Sports, Science and Technology","doi-asserted-by":"publisher","award":["the Program for Building Regional Innovation Ecosystems"],"award-info":[{"award-number":["the Program for Building Regional Innovation Ecosystems"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Early detection of basal stem rot (BSR) disease in oil palm trees is important for the sustainable production of palm oil in the limited land for plantation in Southeast Asia. However, previous studies based on satellite and aircraft hyperspectral remote sensing could not discriminate oil palm trees in the early-stage of the BSR disease from healthy or late-stage trees. In this study, hyperspectral imaging of oil palm trees from an unmanned aerial vehicle (UAV) and machine learning using a random forest algorithm were employed for the classification of four infection categories of the BSR disease: healthy, early-stage, late-stage, and dead trees. A concentric disk segmentation was applied to tree crown segmentation at the sub-plant scale, and recursive feature elimination was used for feature selection. The results revealed that the classification performance for the early-stage trees is maximum at the specific tree crown segments, and only a few spectral bands in the red-edge region are sufficient to classify the infection categories. These findings will be useful for future UAV-based multispectral imaging to efficiently cover a wide area of oil palm plantations for the early detection of BSR disease.<\/jats:p>","DOI":"10.3390\/rs14030799","type":"journal-article","created":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T23:42:20Z","timestamp":1644363740000},"page":"799","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Early Detection of Basal Stem Rot Disease in Oil Palm Tree Using Unmanned Aerial Vehicle-Based Hyperspectral Imaging"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3896-7252","authenticated-orcid":false,"given":"Junichi","family":"Kurihara","sequence":"first","affiliation":[{"name":"Faculty of Science, Hokkaido University, Sapporo 001-0021, Japan"}]},{"given":"Voon-Chet","family":"Koo","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia"}]},{"given":"Cheaw Wen","family":"Guey","sequence":"additional","affiliation":[{"name":"iRadar Sdn Bhd, Jalan Eco 1, Zon Industri Ayer Keroh Baru, Melaka 75450, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2750-6701","authenticated-orcid":false,"given":"Yang Ping","family":"Lee","sequence":"additional","affiliation":[{"name":"FGV R&D Sdn Bhd, Jalan Raja Laut, Kuala Lumpur 50350, Malaysia"}]},{"given":"Haryati","family":"Abidin","sequence":"additional","affiliation":[{"name":"FGV R&D Sdn Bhd, Jalan Raja Laut, Kuala Lumpur 50350, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"190","DOI":"10.3389\/fpls.2015.00190","article-title":"Oil Palm Natural Diversity and the Potential for Yield Improvement","volume":"6","author":"Barcelos","year":"2015","journal-title":"Front. 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