{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T07:34:44Z","timestamp":1772523284411,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T00:00:00Z","timestamp":1667088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001217","name":"Horticulture Innovation and the Department of Agriculture and Water Resources, Australian Government","doi-asserted-by":"publisher","award":["RnD4Profit-14-01-008"],"award-info":[{"award-number":["RnD4Profit-14-01-008"]}],"id":[{"id":"10.13039\/501100001217","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This research investigates the capability of field-based spectroscopy (350\u20132500 nm) for discriminating banana plants (Cavendish subgroup Williams) infested with spider mites from those unaffected. Spider mites are considered a major threat to agricultural production, as they occur on over 1000 plant species, including banana plant varieties. Plants were grown under a controlled glasshouse environment to remove any influence other than the imposed treatment (presence or absence of spider mites). The spectroradiometer measurements were undertaken with a leaf clip over three infestation events. From the resultant spectral data, various classification models were evaluated including partial least squares discriminant analysis (PLSDA), K-nearest neighbour, support vector machines and back propagation neural network. Wavelengths found to have a significant response to the presence of spider mites were extracted using competitive adaptive reweighted sampling (CARS), sub-window permutation analysis (SPA) and random frog (RF) and benchmarked using the classification models. CARS and SPA provided high detection success (86% prediction accuracy), with the wavelengths found to be significant corresponding with the red edge and near-infrared portions of the spectrum. As there is limited access to operational commercial hyperspectral imaging and additional complexity, a multispectral camera (Sequoia) was assessed for detecting spider mite impacts on banana plants. Simulated multispectral bands were able to provide a high level of detection accuracy (prediction accuracy of 82%) based on a PLSDA model, with the near-infrared band being most important, followed by the red edge, green and red bands. Multispectral vegetation indices were trialled using a simple threshold-based classification method using the green normalised difference vegetation index (GNDVI), which achieved 82% accuracy. This investigation determined that remote sensing approaches can provide an accurate method of detecting mite infestations, with multispectral sensors having the potential to provide a more commercially accessible means of detecting outbreaks.<\/jats:p>","DOI":"10.3390\/rs14215467","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T10:47:57Z","timestamp":1667126877000},"page":"5467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Comparison of Analytical Approaches for the Spectral Discrimination and Characterisation of Mite Infestations on Banana Plants"],"prefix":"10.3390","volume":"14","author":[{"given":"Aaron","family":"Aeberli","sequence":"first","affiliation":[{"name":"Applied Agricultural Remote Sensing Centre, School of Science and Technology, University of New England, Armidale, NSW 2351, Australia"},{"name":"Remote Sensing Research Centre, School of Earth and Environmental Sciences, University of Queensland, St. Lucia, QLD 4072, Australia"}]},{"given":"Andrew","family":"Robson","sequence":"additional","affiliation":[{"name":"Applied Agricultural Remote Sensing Centre, School of Science and Technology, University of New England, Armidale, NSW 2351, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2605-6104","authenticated-orcid":false,"given":"Stuart","family":"Phinn","sequence":"additional","affiliation":[{"name":"Remote Sensing Research Centre, School of Earth and Environmental Sciences, University of Queensland, St. Lucia, QLD 4072, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2917-2231","authenticated-orcid":false,"given":"David W.","family":"Lamb","sequence":"additional","affiliation":[{"name":"Food Agility Cooperative Research Centre Ltd., Pitt Str., Sydney, NSW 2001, Australia"},{"name":"Precision Agriculture Research Group, University of New England, Armidale, NSW 2351, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1889-9336","authenticated-orcid":false,"given":"Kasper","family":"Johansen","sequence":"additional","affiliation":[{"name":"Hydrology, Agricultural and Land Observation Group, Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1111\/ijfs.14778","article-title":"A review of root, tuber and banana crops in developing countries: Past, present and future","volume":"56","author":"Scott","year":"2021","journal-title":"Int. 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