{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T04:45:59Z","timestamp":1774413959493,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T00:00:00Z","timestamp":1692921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Agriculture Taiwan","award":["110AS-8.3.2-ST-a6"],"award-info":[{"award-number":["110AS-8.3.2-ST-a6"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Phalaenopsis, an essential flower for export, is significantly affected by fusarium wilt, which impacts its export quality. Hyperspectral imaging technology offers the potential to detect fusarium wilt on Phalaenopsis. The goal of this study was to establish an automated platform for the rapid detection of fusarium wilt on Phalaenopsis. In this research, the automatic target generation process (ATGP) method was employed to identify outliers in the hyperspectral spectrum. Subsequently, the Spectral Angle Mapper (SAM) method was utilized to detect signals similar to the outliers. To suppress background noise and extract the region of interest (ROI), the Constrained Energy Minimization (CEM) method was implemented. For ROI classification and detection, a deep neural network (DNN), a support vector machine (SVM), and a Random Forest Classifier (RFC) were employed. Model performance was evaluated using three-dimensional receiver operating characteristics (3D ROC), and the automated identification system was integrated into hyperspectrometers. The proposed system achieved an accuracy of 95.77% with a total detection time of 3380 ms \u00b1 86.36 ms, proving to be a practical and effective tool for detecting fusarium wilt on Phalaenopsis in the industry.<\/jats:p>","DOI":"10.3390\/rs15174174","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T08:33:09Z","timestamp":1692952389000},"page":"4174","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The Automated Detection of Fusarium Wilt on Phalaenopsis Using VIS-NIR and SWIR Hyperspectral Imaging"],"prefix":"10.3390","volume":"15","author":[{"given":"Min-Shao","family":"Shih","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan"}]},{"given":"Kai-Chun","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan"}]},{"given":"Shao-An","family":"Chou","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan"}]},{"given":"Tsang-Sen","family":"Liu","sequence":"additional","affiliation":[{"name":"Taiwan Agricultural Research Institute, Ministry of Agriculture, Taichung 413, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4221-2787","authenticated-orcid":false,"given":"Yen-Chieh","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.micres.2017.12.002","article-title":"Fusarium species as pathogen on orchids","volume":"207","author":"Srivastava","year":"2018","journal-title":"Microbiol. 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