dc.creator | Appeltans, Simon | es |
dc.creator | Apolo Apolo, Orly Enrique | es |
dc.creator | Rodríguez Vázquez, Jaime Nolasco | es |
dc.creator | Pérez Ruiz, Manuel | es |
dc.creator | Pieters, Jan | es |
dc.creator | Mouazen, Abdul M. | es |
dc.date.accessioned | 2022-07-26T11:21:32Z | |
dc.date.available | 2022-07-26T11:21:32Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Appeltans, S., Apolo Apolo, O.E., Rodríguez Vázquez, J.N., Pérez Ruiz, M., Pieters, J. y Mouazen, A.M. (2021). The Automation of Hyperspectral Training Library Construction: A Case Study for Wheat and Potato Crops. Remote Sensing, 2021 (13) (2021 (23)), 1 p.-12 p.. | |
dc.identifier.issn | 2072-4292 | es |
dc.identifier.uri | https://hdl.handle.net/11441/135792 | |
dc.description.abstract | The potential of hyperspectral measurements for early disease detection has been inves tigated 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 previsi ble 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. | es |
dc.format | application/pdf | es |
dc.format.extent | 12 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Remote Sensing, 2021 (13) (2021 (23)), 1 p.-12 p.. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Hyperspectral | es |
dc.subject | Wheat | es |
dc.subject | Potato | es |
dc.subject | Machine learning | es |
dc.subject | Labelling | es |
dc.title | The Automation of Hyperspectral Training Library Construction: A Case Study for Wheat and Potato Crops | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Aeroespacial y Mecánica de Fluidos | es |
dc.relation.publisherversion | https://www.mdpi.com/2072-4292/13/23/4735 | es |
dc.identifier.doi | 10.3390/rs13234735 | es |
dc.contributor.group | Universidad de Sevilla. AGR278: Smart Biosystems Laboratory . | es |
dc.journaltitle | Remote Sensing | es |
dc.publication.volumen | 2021 (13) | es |
dc.publication.issue | 2021 (23) | es |
dc.publication.initialPage | 1 p. | es |
dc.publication.endPage | 12 p. | es |
dc.identifier.sisius | 13382 | es |