{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:36:59Z","timestamp":1780501019865,"version":"3.54.1"},"reference-count":73,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T00:00:00Z","timestamp":1627344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NRC-IRAP","award":["Project Number 927849"],"award-info":[{"award-number":["Project Number 927849"]}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["CRDPJ507141-16"],"award-info":[{"award-number":["CRDPJ507141-16"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study used close-range multispectral imagery over cucumber plants inside a commercial greenhouse to detect powdery mildew due to Podosphaera xanthii. It was collected using a MicaSense\u00ae RedEdge camera at 1.5 m over the top of the plant. Image registration was performed using Speeded-Up Robust Features (SURF) with an affine geometric transformation. The image background was removed using a binary mask created with the aligned NIR band of each image, and the illumination was corrected using Cheng et al.\u2019s algorithm. Different features were computed, including RGB, image reflectance values, and several vegetation indices. For each feature, a fine Gaussian Support Vector Machines algorithm was trained and validated to classify healthy and infected pixels. The data set to train and validate the SVM was composed of 1000 healthy and 1000 infected pixels, split 70\u201330% into training and validation datasets, respectively. The overall validation accuracy was 89, 73, 82, 51, and 48%, respectively, for blue, green, red, red-edge, and NIR band image. With the RGB images, we obtained an overall validation accuracy of 89%, while the best vegetation index image was the PMVI-2 image which produced an overall accuracy of 81%. Using the five bands together, overall accuracy dropped from 99% in the training to 57% in the validation dataset. While the results of this work are promising, further research should be considered to increase the number of images to achieve better training and validation datasets.<\/jats:p>","DOI":"10.3390\/rs13152948","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T22:35:02Z","timestamp":1627425302000},"page":"2948","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Detecting Infected Cucumber Plants with Close-Range Multispectral Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4567-6904","authenticated-orcid":false,"given":"Claudio I.","family":"Fern\u00e1ndez","sequence":"first","affiliation":[{"name":"Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, NB E3B 5A3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brigitte","family":"Leblon","sequence":"additional","affiliation":[{"name":"Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, NB E3B 5A3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8404-0530","authenticated-orcid":false,"given":"Jinfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geography and Environment, University of Western Ontario, London, ON N6G 2V4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ata","family":"Haddadi","sequence":"additional","affiliation":[{"name":"A&L Canada Laboratories, London, ON N5V 3P5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keri","family":"Wang","sequence":"additional","affiliation":[{"name":"A&L Canada Laboratories, London, ON N5V 3P5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,27]]},"reference":[{"key":"ref_1","unstructured":"(2021, June 06). 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