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Applications of sensing for disease detection
dc.creator | Castro, Ana Isabel de | es_ES |
dc.creator | Pérez Roncal, Claudia | es_ES |
dc.creator | Thomasson, J. Alex | es_ES |
dc.creator | Ehsani, Reza | es_ES |
dc.creator | López Maestresalas, Ainara | es_ES |
dc.creator | Yang, Chenghai | es_ES |
dc.creator | Jarén Ceballos, Carmen | es_ES |
dc.creator | Wang, Tianyi | es_ES |
dc.creator | Cribben, Curtis | es_ES |
dc.creator | Marín Ederra, Diana | es_ES |
dc.creator | Isakeit, Thomas | es_ES |
dc.creator | Urrestarazu Vidart, Jorge | es_ES |
dc.creator | López Molina, Carlos | es_ES |
dc.creator | Wang, Xiwei | es_ES |
dc.creator | Nichols, Robert L. | es_ES |
dc.creator | Santesteban García, Gonzaga | es_ES |
dc.creator | Arazuri Garín, Silvia | es_ES |
dc.creator | Peña, José Manuel | es_ES |
dc.date.accessioned | 2023-09-27T09:07:12Z | |
dc.date.available | 2023-09-27T09:07:12Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | De Castro Megías, A. I., Pérez-Roncal, C., Thomasson, J. A., Ehsani, R., López-Maestresalas, A., Yang, C., Jarén, C., Wang, T., Cribben, C., Marin, D., Isakeit, T., Urrestarazu, J., Lopez-Molina, C., Wang, X., Nichols, R. L., Santesteban, G., Arazuri, S., & Peña, J. M. (2021). Applications of sensing for disease detection. En R. Kerry & A. Escolà (Eds.), Sensing Approaches for Precision Agriculture (pp. 369-398). Springer International Publishing. https://doi.org/10.1007/978-3-030-78431-7_13 | en |
dc.identifier.isbn | 978-3-030-78431-7 | |
dc.identifier.uri | https://hdl.handle.net/2454/46415 | |
dc.description.abstract | The potential loss of world crop production from the effect of pests, including weeds, animal pests, pathogens and viruses has been quantifed as around 40%. In addition to the economic threat, plant diseases could have disastrous consequences for the environment. Accurate and timely disease detection requires the use of rapid and reliable techniques capable of identifying infected plants and providing the tools required to implement precision agriculture strategies. The combination of suitable remote sensing (RS) data and advanced analysis algorithms makes it possible to develop prescription maps for precision disease control. This chapter shows some case studies on the use of remote sensing technology in some of the world’s major crops; namely cotton, avocado and grapevines. In these case studies, RS has been applied to detect disease caused by fungi using different acquisition platforms at different scales, such as leaf-level hyperspectral data and canopy-level remote imagery taken from satellites, manned airplanes or helicopter, and UAVs. The results proved that remote sensing is useful, effcient and effective for identifying cotton root rot zones in cotton felds, laurel wilt-infested avocado trees and escaaffected vines, which would allow farmers to optimize inputs and feld operations, resulting in reduced yield losses and increased profts. | en |
dc.description.sponsorship | The research presented here was partly financed by the USDA Specialty Block Grant No. 019730 (Florida Department of Agriculture and Consumer Services, USA), AGL2017-83325-C4-1R and AGL2017-83325-C4-4R Projects (Spanish Ministry of Science, Innovation and Universities and AEI/EU-FEDER funds), Public University of Navarre postgraduate scholarships (FPI-UPNA-2017, Res.654/2017), Project DECIVID (Res.104E/2017, Department of Economic Development of the Navarre Government-Spain), and the Spanish MINECO project TIN2016-77356-P (AEI, Feder/UE). | en |
dc.format.mimetype | application/pdf | en |
dc.language.iso | eng | en |
dc.publisher | Springer | en |
dc.relation.ispartof | Kerry, R.; Escolà, A. (Eds.). Sensing approaches for precision agricultura. Cham: Springer; 2021. p.369-398 978-3-030-78431-7 | en |
dc.rights | © Springer Nature Switzerland AG 2021 | en |
dc.subject | Crop disease | en |
dc.subject | Image analysis | en |
dc.subject | Spectral analysis | en |
dc.subject | Multispectral imaging | en |
dc.subject | Hyperspectral imaging | en |
dc.subject | Prescription map | en |
dc.title | Applications of sensing for disease detection | en |
dc.type | Capítulo de libro / Liburuen kapitulua | es |
dc.type | info:eu-repo/semantics/bookPart | en |
dc.date.updated | 2023-09-26T08:15:53Z | |
dc.contributor.department | Agronomía, Biotecnología y Alimentación | es_ES |
dc.contributor.department | Agronomia, Bioteknologia eta Elikadura | eu |
dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
dc.contributor.department | Ingeniería | es_ES |
dc.contributor.department | Ingeniaritza | eu |
dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
dc.identifier.doi | 10.1007/978-3-030-78431-7_13 | |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-83325-C4-1-R/ES/ | en |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-83325-C4-4-R/ES/ | en |
dc.relation.projectID | info:eu-repo/grantAgreement/ES/1PE/TIN2016-77356-P | en |
dc.relation.publisherversion | https://doi.org/10.1007/978-3-030-78431-7_13 | |
dc.type.version | Versión aceptada / Onetsi den bertsioa | es |
dc.type.version | info:eu-repo/semantics/acceptedVersion | en |
dc.contributor.funder | Gobierno de Navarra / Nafarroako Gobernua | es |
dc.contributor.funder | Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa | es |
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