{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:45:40Z","timestamp":1761129940567,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003130","name":"Research Foundation - Flanders","doi-asserted-by":"publisher","award":["G0F9216N"],"award-info":[{"award-number":["G0F9216N"]}],"id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Regional Government of Andalusia","award":["US-1263678"],"award-info":[{"award-number":["US-1263678"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The potential of hyperspectral measurements for early disease detection has been investigated by many experts over the last 5 years. One of the difficulties is obtaining enough data for training and building a hyperspectral training library. When the goal is to detect disease at a previsible stage, before the pathogen has manifested either its first symptoms or in the area surrounding the existing symptoms, it is impossible to objectively delineate the regions of interest containing the previsible pathogen growth from the areas without the pathogen growth. To overcome this, we propose an image labelling and segmentation algorithm that is able to (a) more objectively label the visible symptoms for the construction of a training library and (b) extend this labelling to the pre-visible symptoms. This algorithm is used to create hyperspectral training libraries for late blight disease (Phytophthora infestans) in potatoes and two types of leaf rust (Puccinia triticina and Puccinia striiformis) in wheat. The model training accuracies were compared between the automatic labelling algorithm and the classic visual delineation of regions of interest using a logistic regression machine learning approach. The modelling accuracies of the automatically labelled datasets were higher than those of the manually labelled ones for both potatoes and wheat, at 98.80% for P. infestans in potato, 97.69% for P. striiformis in soft wheat, and 96.66% for P. triticina in durum wheat.<\/jats:p>","DOI":"10.3390\/rs13234735","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4735","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["The Automation of Hyperspectral Training Library Construction: A Case Study for Wheat and Potato Crops"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0344-0971","authenticated-orcid":false,"given":"Simon","family":"Appeltans","sequence":"first","affiliation":[{"name":"Department of Environment, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8311-0579","authenticated-orcid":false,"given":"Orly Enrique","family":"Apolo-Apolo","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Aeroespacial y Mec\u00e1nica de Fluidos \u00c1rea Agroforestal, University of Sevilla, 41013 Sevilla, Spain"}]},{"given":"Jaime Nolasco","family":"Rodr\u00edguez-V\u00e1zquez","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Aeroespacial y Mec\u00e1nica de Fluidos \u00c1rea Agroforestal, University of Sevilla, 41013 Sevilla, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3681-1572","authenticated-orcid":false,"given":"Manuel","family":"P\u00e9rez-Ruiz","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Aeroespacial y Mec\u00e1nica de Fluidos \u00c1rea Agroforestal, University of Sevilla, 41013 Sevilla, Spain"}]},{"given":"Jan","family":"Pieters","sequence":"additional","affiliation":[{"name":"Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0354-0067","authenticated-orcid":false,"given":"Abdul M.","family":"Mouazen","sequence":"additional","affiliation":[{"name":"Department of Environment, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s12571-020-01016-z","article-title":"Pathogens which threaten food security: Puccinia striiformis, the wheat stripe rust pathogen","volume":"12","author":"Chen","year":"2020","journal-title":"Food Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s10658-011-9878-z","article-title":"Recent advances in sensing plant diseases for precision crop protection","volume":"133","author":"Mahlein","year":"2012","journal-title":"Eur. 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