{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:20:10Z","timestamp":1775326810547,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T00:00:00Z","timestamp":1720483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"WIPOTEC, Kaiserslautern"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Varroa mite infestation poses a severe threat to honeybee colonies globally. This study investigates the feasibility of utilizing the HS-Cam and machine learning techniques for Varroa mite counting. The methodology involves image acquisition, dimensionality reduction through Principal Component Analysis (PCA), and machine learning-based segmentation and classification algorithms. Specifically, a k-Nearest Neighbors (kNNs) model distinguishes Varroa mites from other objects in the images, while a Support Vector Machine (SVM) classifier enhances shape detection. The final phase integrates a dedicated counting algorithm, leveraging outputs from the SVM classifier to quantify Varroa mite populations in hyperspectral images. The preliminary results demonstrate segmentation accuracy exceeding 99% and an average precision of 0.9983 and recall of 0.9947 across all the classes. The results obtained from our machine learning-based approach for Varroa mite counting were compared against ground-truth labels obtained through manual counting, demonstrating a high degree of agreement between the automated counting and manual ground truth. Despite working with a limited dataset, the HS-Cam showcases its potential for Varroa counting, delivering superior performance compared to traditional RGB images. Future research directions include validating the proposed hyperspectral imaging methodology with a more extensive and diverse dataset. Additionally, the effectiveness of using a near-infrared (NIR) excitation source for Varroa detection will be explored, along with assessing smartphone integration feasibility.<\/jats:p>","DOI":"10.3390\/s24144437","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T15:27:20Z","timestamp":1720538840000},"page":"4437","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Varroa Mite Counting Based on Hyperspectral Imaging"],"prefix":"10.3390","volume":"24","author":[{"given":"Amira","family":"Ghezal","sequence":"first","affiliation":[{"name":"Fachbereich Elektrotechnik und Informationstechnik, Lehrstuhl Kognitive Integrierte Sensorsysteme, RPTU Kaiserslautern, 67663 Kaiserslautern, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian Jair Luis","family":"Pe\u00f1a","sequence":"additional","affiliation":[{"name":"Fachbereich Elektrotechnik und Informationstechnik, Lehrstuhl Kognitive Integrierte Sensorsysteme, RPTU Kaiserslautern, 67663 Kaiserslautern, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"K\u00f6nig","sequence":"additional","affiliation":[{"name":"Fachbereich Elektrotechnik und Informationstechnik, Lehrstuhl Kognitive Integrierte Sensorsysteme, RPTU Kaiserslautern, 67663 Kaiserslautern, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1051\/apido\/2010024","article-title":"Landscape enhancement of floral resources for honey bees in agro-ecosystems","volume":"41","author":"Decourtye","year":"2010","journal-title":"Apidologie"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1098\/rspb.2006.3721","article-title":"Importance of pollinators in changing landscapes for world crops","volume":"274","author":"Klein","year":"2007","journal-title":"Proc. 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