{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T04:08:55Z","timestamp":1774498135468,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T00:00:00Z","timestamp":1646179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Major Project of Guangxi, China","award":["Gui Ke AA18118037"],"award-info":[{"award-number":["Gui Ke AA18118037"]}]},{"name":"Science and Technology Major Project of Guangxi, China","award":["Gui Ke 2018-266-Z01"],"award-info":[{"award-number":["Gui Ke 2018-266-Z01"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31760342"],"award-info":[{"award-number":["31760342"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Banana Fusarium wilt (BFW) is a devastating disease with no effective cure methods. Timely and effective detection of the disease and evaluation of its spreading trend will help farmers in making right decisions on plantation management. The main purpose of this study was to find the spectral features of the BFW-infected canopy and build the optimal BFW classification models for different stages of infection. A RedEdge-MX camera mounted on an unmanned aerial vehicle (UAV) was used to collect multispectral images of a banana plantation infected with BFW in July and August 2020. Three types of spectral features were used as the inputs of classification models, including three-visible-band images, five-multispectral-band images, and vegetation indices (VIs). Four supervised methods including Support Vector Machine (SVM), Random Forest (RF), Back Propagation Neural Networks (BPNN) and Logistic Regression (LR), and two unsupervised methods including Hotspot Analysis (HA) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) were adopted to detect the BFW-infected canopies. Comparing to the healthy canopies, the BFW-infected canopies had higher reflectance in the visible region, but lower reflectance in the NIR region. The classification results showed that most of the supervised and unsupervised methods reached excellent accuracies. Among all the supervised methods, RF based on the five-multispectral-band was considered as the optimal model, with higher overall accuracy (OA) of 97.28% and faster running time of 22 min. For the unsupervised methods, HA reached high and balanced OAs of more than 95% based on the selected VIs derived from the red and NIR band, especially for WDRVI, NDVI, and TDVI. By comprehensively evaluating the classification results of different metrics, the unsupervised method HA was recommended for BFW recognition, especially in the late stage of infection; the supervised method RF was recommended in the early stage of infection to reach a slightly higher accuracy. The results found in this study could give advice for banana plantation management and provide approaches for plant disease detection.<\/jats:p>","DOI":"10.3390\/rs14051231","type":"journal-article","created":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T22:53:25Z","timestamp":1646261605000},"page":"1231","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Banana Fusarium Wilt Disease Detection by Supervised and Unsupervised Methods from UAV-Based Multispectral Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3144-1883","authenticated-orcid":false,"given":"Shimin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Guangxi University, Nanning 530004, China"},{"name":"Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530004, China"}]},{"given":"Xiuhua","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Guangxi University, Nanning 530004, China"},{"name":"Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530004, China"}]},{"given":"Yuxuan","family":"Ba","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Guangxi University, Nanning 530004, China"},{"name":"Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530004, China"}]},{"given":"Xuegang","family":"Lyu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Guangxi University, Nanning 530004, China"},{"name":"Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530004, China"}]},{"given":"Muqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530004, China"},{"name":"IRREC-IFAS, University of Florida, Fort Pierce, FL 34945, USA"}]},{"given":"Minzan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory for Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.isprsjprs.2020.08.025","article-title":"Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin","volume":"169","author":"Selvaraj","year":"2020","journal-title":"ISPRS J. 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