Imágenes térmicas y respuestas espectrales para identificar condiciones de estrés hídrico y estado nutricional con relación al nitrógeno en papa amarilla diploide (Solanum tuberosum Grupo Phureja)

ilustraciones, graficas

Autores:: Velandia Sánchez, Edisson Andrés

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

id	UNACIONAL2_798f37b49f48578e742eccd28cf52222
oai_identifier_str	oai:repositorio.unal.edu.co:unal/82236
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Imágenes térmicas y respuestas espectrales para identificar condiciones de estrés hídrico y estado nutricional con relación al nitrógeno en papa amarilla diploide (Solanum tuberosum Grupo Phureja)
dc.title.translated.eng.fl_str_mv	Thermal imaging and spectral responses to identify water stress conditions and nutritional status in relation to nitrogen in diploid yellow potato (Solanum tuberosum tuberosum Phureja Group)
title	Imágenes térmicas y respuestas espectrales para identificar condiciones de estrés hídrico y estado nutricional con relación al nitrógeno en papa amarilla diploide (Solanum tuberosum Grupo Phureja)
spellingShingle	Imágenes térmicas y respuestas espectrales para identificar condiciones de estrés hídrico y estado nutricional con relación al nitrógeno en papa amarilla diploide (Solanum tuberosum Grupo Phureja) 570 - Biología::571 - Fisiología y temas relacionados Estrés de sequia drought stress Solanum tuberosum Temperatura del dosel Índices espectrales Estado hídrico foliar Estrés por nitrógeno Canopy temperature Spectral indices Leaf water status Nitrogen stress
title_short	Imágenes térmicas y respuestas espectrales para identificar condiciones de estrés hídrico y estado nutricional con relación al nitrógeno en papa amarilla diploide (Solanum tuberosum Grupo Phureja)
title_full	Imágenes térmicas y respuestas espectrales para identificar condiciones de estrés hídrico y estado nutricional con relación al nitrógeno en papa amarilla diploide (Solanum tuberosum Grupo Phureja)
title_fullStr	Imágenes térmicas y respuestas espectrales para identificar condiciones de estrés hídrico y estado nutricional con relación al nitrógeno en papa amarilla diploide (Solanum tuberosum Grupo Phureja)
title_full_unstemmed	Imágenes térmicas y respuestas espectrales para identificar condiciones de estrés hídrico y estado nutricional con relación al nitrógeno en papa amarilla diploide (Solanum tuberosum Grupo Phureja)
title_sort	Imágenes térmicas y respuestas espectrales para identificar condiciones de estrés hídrico y estado nutricional con relación al nitrógeno en papa amarilla diploide (Solanum tuberosum Grupo Phureja)
dc.creator.fl_str_mv	Velandia Sánchez, Edisson Andrés
dc.contributor.advisor.none.fl_str_mv	Martínez Martínez, Luis Joel Rodríguez Molano, Luis Ernesto
dc.contributor.author.none.fl_str_mv	Velandia Sánchez, Edisson Andrés
dc.subject.ddc.spa.fl_str_mv	570 - Biología::571 - Fisiología y temas relacionados
topic	570 - Biología::571 - Fisiología y temas relacionados Estrés de sequia drought stress Solanum tuberosum Temperatura del dosel Índices espectrales Estado hídrico foliar Estrés por nitrógeno Canopy temperature Spectral indices Leaf water status Nitrogen stress
dc.subject.agrovoc.spa.fl_str_mv	Estrés de sequia
dc.subject.agrovoc.eng.fl_str_mv	drought stress
dc.subject.agrovoc.none.fl_str_mv	Solanum tuberosum
dc.subject.proposal.spa.fl_str_mv	Temperatura del dosel Índices espectrales Estado hídrico foliar Estrés por nitrógeno
dc.subject.proposal.eng.fl_str_mv	Canopy temperature Spectral indices Leaf water status Nitrogen stress
description	ilustraciones, graficas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-09-01T16:08:40Z
dc.date.available.none.fl_str_mv	2022-09-01T16:08:40Z
dc.date.issued.none.fl_str_mv	2022
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/82236
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/82236 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.indexed.spa.fl_str_mv	RedCol LaReferencia
dc.relation.references.spa.fl_str_mv	Allen, R., Pereira, L., Raes, D., & Smith, M. (1998). Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56. FAO - Food and Agriculture Organization of the United Nations, 110–115. https://doi.org/10.1016/S0141-1187(05)80058-6 Anderson, M. C., Hain, C., Otkin, J., Zhan, X., Mo, K., Svoboda, M., Wardlow, B., & Pimstein, A. (2013). An Intercomparison of Drought Indicators Based on Thermal Remote Sensing and NLDAS-2 Simulations with U.S. Drought Monitor Classifications. Journal of Hydrometeorology, 14(4), 1035–1056. https://doi.org/10.1175/JHM-D-12-0140.1 Ariza, W. (2017). Respuestas fisiológicas, bioquímicas y rendimiento en tres variedades de papa criolla (Solanum tuberosums grupo Phureja) en déficit hídrico. Ariza, W., Rodríguez, L. E., Moreno-Echeverry, D., Guerrero, C. A., & Moreno, L. P. (2020). Effect of water deficit on some physiological and biochemical responses of the yellow diploid potato (Solanum tuberosum L. group phureja). Agronomia Colombiana, 38(1), 48–56. https://doi.org/10.15446/agron.colomb.v38n1.78982 Babich, G. A., & Camps, O. I. (1996). Weighted Parzen windows for pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(5), 567–570. https://doi.org/10.1109/34.494647 Banerjee, K., Krishnan, P., & Mridha, N. (2018). Application of thermal imaging of wheat crop canopy to estimate leaf area index under different moisture stress conditions. Biosystems Engineering, 166, 13–27. https://doi.org/10.1016/j.biosystemseng.2017.10.012 Barragán, J. N. (2019). La Papa Incluida - Desempeño y perspectivas económicas del subsector papa 2018-2019. Revista Papa, 47, pag 45-48. https://fedepapa.com/wp-content/uploads/2017/01/REVISTA-47-COMPLETA.pdf Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M. L., & Bareth, G. (2015). Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39, 79–87. https://doi.org/10.1016/j.jag.2015.02.012 Berni, J. A. J., Zarco-Tejada, P. J., Sepulcre-Cantó, G., Fereres, E., & Villalobos, F. (2009). Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sensing of Environment, 113(11), 2380–2388. https://doi.org/10.1016/j.rse.2009.06.018 Borhan, M. S., Panigrahi, S., Satter, M. A., & Gu, H. (2017). Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves. Information Processing in Agriculture, 4(4), 275–282. https://doi.org/10.1016/j.inpa.2017.07.005 Buitrago, M. F., Groen, T. A., Hecker, C. A., & Skidmore, A. K. (2016). Changes in thermal infrared spectra of plants caused by temperature and water stress. ISPRS Journal of Photogrammetry and Remote Sensing, 111, 22–31. https://doi.org/10.1016/j.isprsjprs.2015.11.003 Campos, H., & Ortíz, O. (2020). The Potato Crop: Its Agricultural, Nutritional and Social Contribution to Humankind (H. Campos & O. Ortíz, Eds.). Springer. https://doi.org/https://doi.org/10.1007/978-3-030-28683-5 Cao, Q., Miao, Y., Feng, G., Gao, X., Li, F., Liu, B., Yue, S., Cheng, S., Ustin, S. L., & Khosla, R. (2015). Active canopy sensing of winter wheat nitrogen status: An evaluation of two sensor systems. Computers and Electronics in Agriculture, 112, 54–67. https://doi.org/10.1016/j.compag.2014.08.012 Cavender-Bares, J., Gamon, J. A., & Townsend, P. A. (2020). Remote sensing of plant biodiversity. In Remote Sensing of Plant Biodiversity. https://doi.org/10.1007/978-3-030-33157-3 Cho, M. A., & Skidmore, A. K. (2006). A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sensing of Environment, 101(2), 181–193. https://doi.org/10.1016/j.rse.2005.12.011 Cilia, C., Panigada, C., Rossini, M., Meroni, M., Busetto, L., Amaducci, S., Boschetti, M., Picchi, V., & Colombo, R. (2014). Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sensing, 6(7), 6549–6565. https://doi.org/10.3390/rs6076549 Çolak, Y., Yazar, A., Sesveren, S., & Çolak, I. (2017). Evaluation of yield and leaf water potantial ( LWP ) for eggplant under varying irrigation regimes using surface and subsurface drip systems Yes. Scientia Horticulturae, 219, 10–21. https://doi.org/10.1016/j.scienta.2017.02.051 Costa, J. M., Egipto, R., Sánchez-Virosta, A., Lopes, C. M., & Chaves, M. M. (2019). Canopy and soil thermal patterns to support water and heat stress management in vineyards. Agricultural Water Management, 216(November 2017), 484–496. https://doi.org/10.1016/j.agwat.2018.06.001 Cruz De Carvalho, M. H. (2008a). Drought stress and reactive oxygen species: Production, scavenging and signaling. Plant Signaling and Behavior, 3(3), 156–165. https://doi.org/10.4161/psb.3.3.5536 Cruz De Carvalho, M. H. (2008b). Drought stress and reactive oxygen species: Production, scavenging and signaling. Plant Signaling and Behavior, 3(3), 156–165. https://doi.org/10.4161/psb.3.3.5536 Cucho-Padin, G., Rinza, J., Ninanya, J., Loayza, H., Quiroz, R., & Ramírez, D. A. (2020). Development of an open-source thermal image processing software for improving irrigation management in potato crops (Solanum tuberosum L.). Sensors (Switzerland), 20(2), 1–17. https://doi.org/10.3390/s20020472 Cunliffe, A. M., Brazier, R. E., & Anderson, K. (2016). Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sensing of Environment, 183, 129–143. https://doi.org/10.1016/j.rse.2016.05.019 Dalla Costa, L., Delle Vedove, G., Gianquinto, G., Giovanardi, R., & Peressotti, A. (1997). Yield, water use efficiency and nitrogen uptake in potato: Influence of drought stress. Potato Research, 40(1), 19–34. https://doi.org/10.1007/BF02407559 DeJonge, K. C., Taghvaeian, S., Trout, T. J., & Comas, L. H. (2015). Comparison of canopy temperature-based water stress indices for maize. Agricultural Water Management, 156, 51–62. https://doi.org/10.1016/j.agwat.2015.03.023 Devaux, A., Kromann, P., & Ortiz, O. (2014). Potatoes for Sustainable Global Food Security. Potato Research, 57(3–4), 185–199. https://doi.org/10.1007/s11540-014-9265-1 Duan, T., Chapman, S. C., Guo, Y., & Zheng, B. (2017). Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle. Field Crops Research, 210(May), 71–80. https://doi.org/10.1016/j.fcr.2017.05.025 Egea, G., Padilla, C. M., Martinez, J., Fernández, J. E., & Pérez, M. (2017). Assessing a crop water stress index derived from aerial thermal imaging and infrared thermometry in super-high density olive orchards. Agricultural Water Management, 187, 210–221. https://doi.org/10.1016/J.AGWAT.2017.03.030 Ezenne, G. I., Jupp, L., Mantel, S. K., & Tanner, J. L. (2019). Current and potential capabilities of UAS for crop water productivity in precision agriculture. Agricultural Water Management, 218(March), 158–164. https://doi.org/10.1016/j.agwat.2019.03.034 FAO. (2016). Flying robots for food security. Food and Agriculture Organization of the United Nations. http://www.fao.org/zhc/detail-events/en/c/428256/ Far, S. T., & Rezaei-Moghaddam, K. (2018). Impacts of the precision agricultural technologies in Iran: An analysis experts’ perception & their determinants. Information Processing in Agriculture, 5(1), 173–184. https://doi.org/10.1016/j.inpa.2017.09.001 Feng, R., Zhang, Y., Yu, W., Hu, W., Wu, J., Ji, R., Wang, H., & Zhao, X. (2013). Analysis of the relationship between the spectral characteristics of maize canopy and leaf area index under drought stress. Acta Ecologica Sinica, 33(6), 301–307. https://doi.org/10.1016/j.chnaes.2013.09.001 Gabriel, J. L., Zarco-tejada, P. J., Juan, P. L., Alonso-ayuso, M., Quemada, M., Enrique, P., & Obispo, S. (2017). Airborne and ground level sensors for monitoring nitrogen status in a maize crop. Biosystems Engineering, 160, 124–133. https://doi.org/10.1016/j.biosystemseng.2017.06.003 Gao, B.-C. (1996). NDWI A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space. Remote Sens. Environ, 7212(April), 257–266. García-Tejero, I. F., Gutiérrez-Gordillo, S., Ortega-Arévalo, C., Iglesias-Contreras, M., Moreno, J. M., Souza-Ferreira, L., & Durán-Zuazo, V. H. (2018). Thermal imaging to monitor the crop-water status in almonds by using the non-water stress baselines. Scientia Horticulturae, 238(April), 91–97. https://doi.org/10.1016/j.scienta.2018.04.045 García-Tejero, I., Rubio, A. E., Viñuela, I., Hernández, A., Gutiérrez-Gordillo, S., Rodríguez-Pleguezuelo, C. R., & Durán-Zuazo, V. H. (2018). Thermal imaging at plant level to assess the crop-water status in almond trees (cv. Guara) under deficit irrigation strategies. Agricultural Water Management, 208(May), 176–186. https://doi.org/10.1016/j.agwat.2018.06.002 George, T. S., Taylor, M. A., Dodd, I. C., & White, P. J. (2018). Climate Change and Consequences for Potato Production: a Review of Tolerance to Emerging Abiotic Stress. Potato Research, 60(3–4), 239–268. https://doi.org/10.1007/s11540-018-9366-3 Gerhards, M., Rock, G., Schlerf, M., & Udelhoven, T. (2016). Water stress detection in potato plants using leaf temperature, emissivity, and reflectance. International Journal of Applied Earth Observation and Geoinformation, 53, 27–39. https://doi.org/10.1016/j.jag.2016.08.004 Getahun, B. B. (2018). Potato Breeding for Nitrogen-Use Efficiency : Constraints , Achievements , and Future Prospects. 2018(10), 269–281. Giraldo, C., Velandia, E. A., Fischer, G., Martínez, L. J., & Gómez-Caro, S. (2020). Hyperspectral response of cape gooseberry (Physalis peruviana L.) plants inoculated with Fusarium oxysporum f. sp. physali for vascular wilt detection. Revista Colombiana de Ciencias Hortícolas, 14(November), 3–29. https://doi.org/10.17584/rcch.2020v14i3.10938 Gonzalez-Dugo, V., Goldhamer, D., Zarco-Tejada, P. J., & Fereres, E. (2015). Improving the precision of irrigation in a pistachio farm using an unmanned airborne thermal system. Irrigation Science. https://doi.org/10.1007/s00271-014-0447-z Gonzalez-Dugo, V., Zarco-Tejada, P., Berni, J. A. J., Suárez, L., Goldhamer, D., & Fereres, E. (2012). Almond tree canopy temperature reveals intra-crown variability that is water stress-dependent. Agricultural and Forest Meteorology, 154–155, 156–165. https://doi.org/10.1016/j.agrformet.2011.11.004 Goyer, A. (2017). Maximizing the Nutritional Potential of Potato: the Case of Folate. Potato Research, 60(3–4), 319–325. https://doi.org/10.1007/s11540-018-9374-3 Grant, O. M., Tronina, Ł., Jones, H. G., & Chaves, M. M. (2007). Exploring thermal imaging variables for the detection of stress responses in grapevine under different irrigation regimes. Journal of Experimental Botany, May 2014, 1–11. https://doi.org/10.1093/jxb/erl153 Guo, J., Tian, G., Zhou, Y., Wang, M., Ling, N., Shen, Q., & Guo, S. (2016). Evaluation of the grain yield and nitrogen nutrient status of wheat (Triticum aestivum L.) using thermal imaging. Field Crops Research, 196, 463–472. https://doi.org/10.1016/j.fcr.2016.08.008 Gupta, S. D., & Ibaraki, Y. (2015). Plant Image Analysis. In S. D. Gupta & Y. Ibaraki (Eds.), Plant Image Analysis. Taylor & Francis Group. https://doi.org/10.1201/b17441 Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2–3), 416–426. https://doi.org/10.1016/S0034-4257(02)00018-4 Han, M., Zhang, H., DeJonge, K. C., Comas, L. H., & Trout, T. J. (2016). Estimating maize water stress by standard deviation of canopy temperature in thermal imagery. Agricultural Water Management, 177, 400–409. https://doi.org/10.1016/j.agwat.2016.08.031 Hu, D. W., Sun, Z. P., Li, T. L., Yan, H. Z., & Zhang, H. (2014). Nitrogen nutrition index and its relationship with N use efficiency, tuber yield, radiation use efficiency, and leaf parameters in potatoes. Journal of Integrative Agriculture, 13(5), 1008–1016. https://doi.org/10.1016/S2095-3119(13)60408-6 Hunt, R., & Rock, B. (1989). Detection of changes in leaf water content using Near- and Middle-Infrared reflectances. Remote Sensing and Enviroment, 30(1), 43–54. https://doi.org/10.1016/0034-4257(89)90046-1 Hussain, H., Hussain, S., Khaliq, A., Ashraf, U., & Anjum, S. (2018). Chilling and Drought Stresses in Crop Plants : Implications , Cross Talk , and Potential Management Opportunities. Frontiers in Plant Science, 9(April), 1–21. https://doi.org/10.3389/fpls.2018.00393 Ihuoma, S. O., & Madramootoo, C. A. (2017). Recent advances in crop water stress detection. Computers and Electronics in Agriculture, 141, 267–275. https://doi.org/10.1016/j.compag.2017.07.026 Ishida, T., Kurihara, J., Viray, F. A., Namuco, S. B., Paringit, E. C., Perez, G. J., Takahashi, Y., & Marciano, J. J. (2018). A novel approach for vegetation classification using UAV-based hyperspectral imaging. Computers and Electronics in Agriculture, 144(November 2017), 80–85. https://doi.org/10.1016/j.compag.2017.11.027 Kassambara, A., & Mundt, F. (2017). Factoextra extract and visualize the results of multivariate data analyses (pp. 337–354). Khanal, S., Fulton, J., & Shearer, S. (2017). An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture, 139, 22–32. https://doi.org/10.1016/j.compag.2017.05.001 Khorsandi, A., Hemmat, A., Mireei, S. A., Amirfattahi, R., & Ehsanzadeh, P. (2018). Plant temperature-based indices using infrared thermography for detecting water status in sesame under greenhouse conditions. Agricultural Water Management, 204, 222–233. https://doi.org/10.1016/j.agwat.2018.04.012 Kim, Y., Glenn, D. M., Park, J., Ngugi, H. K., & Lehman, B. L. (2011). Hyperspectral image analysis for water stress detection of apple trees. Computers and Electronics in Agriculture, 77(2), 155–160. https://doi.org/10.1016/j.compag.2011.04.008 Kullberg, E. G., DeJonge, K. C., & Chávez, J. L. (2017). Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients. Agricultural Water Management, 179, 64–73. https://doi.org/10.1016/j.agwat.2016.07.007 Lahlou, O., Ouattar, S., & Ledent, J. (2003). The effect of drought and cultivar on growth parameters, yield and yield components of potato. Agronomie, 23, 257–268. https://doi.org/10.1051/agro Lê, S., Josse, J., & Husson, F. (2008). FactoMineR: An R package for multivariate analysis. Journal of Statistical Software, 25(1), 1–18. https://doi.org/10.18637/jss.v025.i01 Li, F., Miao, Y., Feng, G., Yuan, F., Yue, S., Gao, X., Liu, Y., Liu, B., Ustin, S. L., & Chen, X. (2014). Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Research, 157, 111–123. https://doi.org/10.1016/j.fcr.2013.12.018 Liu, F., Jensen, C. R., Shahanzari, A., Andersen, M. N., & Jacobsen, S. E. (2005). ABA regulated stomatal control and photosynthetic water use efficiency of potato (Solanum tuberosum L.) during progressive soil drying. Plant Science, 168(3), 831–836. https://doi.org/10.1016/j.plantsci.2004.10.016 Liu, T., Li, R., Zhong, X., Jiang, M., Jin, X., Zhou, P., Liu, S., Sun, C., & Guo, W. (2018). Estimates of rice lodging using indices derived from UAV visible and thermal infrared images. Agricultural and Forest Meteorology, 252, 144–154. https://doi.org/10.1016/J.AGRFORMET.2018.01.021 Mahlein, A. K., Rumpf, T., Welke, P., Dehne, H. W., Plümer, L., Steiner, U., & Oerke, E. C. (2013). Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128, 21–30. https://doi.org/10.1016/j.rse.2012.09.019 Mahmud, A., Hossain, M. M., Zakaria, M., Mian, M. A. K., & Karim, M. A. (2015). Effects of water stress on plant canopy, yield attributes and yield of potato. Kasetsart Journal - Natural Science, 49(4), 491–505. Mangus, D. L., Sharda, A., & Zhang, N. (2016). Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse. Computers and Electronics in Agriculture, 121, 149–159. https://doi.org/10.1016/J.COMPAG.2015.12.007 Martinez, L. J., & Ramos, A. (2015). Estimation of chlorophyll concentration in maize using spectral reflectance. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 40(7W3), 65–71. https://doi.org/10.5194/isprsarchives-XL-7-W3-65-2015 Mehrabi, F., & Sepaskhah, A. R. (2019). Partial root zone drying irrigation, planting methods and nitrogen fertilization influence on physiologic and agronomic parameters of winter wheat. Agricultural Water Management, 223(January), 105688. https://doi.org/10.1016/j.agwat.2019.105688 Milroy, S. P., Wang, P., & Sadras, V. (2019). Field Crops Research De fi ning upper limits of nitrogen uptake and nitrogen use e ffi ciency of potato in response to crop N supply. Field Crops Research, 239(May), 38–46. https://doi.org/10.1016/j.fcr.2019.05.011 Ministerio de Agricultura y Desarrollo Sostenible. (2019). ESTRATEGIA DE ORDENAMIENTO DE LA PRODUCCIÓN - CADENA PRODUCTIVA DE LA PAPA Y SU INDUSTRIA. In Plan de ordenamiento papa 2019-2023. https://sioc.minagricultura.gov.co/Papa/Normatividad/Plan de Ordenamiento papa 2019-2023.pdf Mohd Asaari, M. S., Mishra, P., Mertens, S., Dhondt, S., Inzé, D., Wuyts, N., & Scheunders, P. (2018). Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform. ISPRS Journal of Photogrammetry and Remote Sensing, 138, 121–138. https://doi.org/10.1016/j.isprsjprs.2018.02.003 Mompié, E., Martín, R., & Morales, D. (2015). Comportamiento de la acumulación y distribución de masa seca en tres variedades de papa (Solanum tuberosum L .). Cultivos Tropicales, 36(4), 70–76. Motalebifard, R., Najafi, N., Oustan, S., Nyshabouri, M. R., & Valizadeh, M. (2013). The combined effects of phosphorus and zinc on evapotranspiration, leaf water potential, water use efficiency and tuber attributes of potato under water deficit conditions. Scientia Horticulturae, 162, 31–38. https://doi.org/10.1016/j.scienta.2013.07.043 Munnaf, M. A., Haesaert, G., van Meirvenne, M., & Mouazen, A. M. (2020). Map-based site-specific seeding of consumption potato production using high-resolution soil and crop data fusion. Computers and Electronics in Agriculture, 178(July), 105752. https://doi.org/10.1016/j.compag.2020.105752 O’Shaughnessy, S. A., Evett, S. R., Colaizzi, P. D., & Howell, T. A. (2011). Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton. Agricultural Water Management, 98(10), 1523–1535. https://doi.org/10.1016/j.agwat.2011.05.005 Pancorbo, J. L., Camino, C., Alonso-Ayuso, M., Raya-Sereno, M. D., Gonzalez-Fernandez, I., Gabriel, J. L., Zarco-Tejada, P. J., & Quemada, M. (2021). Simultaneous assessment of nitrogen and water status in winter wheat using hyperspectral and thermal sensors. European Journal of Agronomy, 127(August 2020), 126287. https://doi.org/10.1016/j.eja.2021.126287 Panigada, C., Rossini, M., Meroni, M., Cilia, C., Busetto, L., Amaducci, S., Boschetti, M., Cogliati, S., Picchi, V., Pinto, F., Marchesi, A., & Colombo, R. (2014). Fluorescence, PRI and canopy temperature for water stress detection in cereal crops. International Journal of Applied Earth Observation and Geoinformation, 30(1), 167–178. https://doi.org/10.1016/j.jag.2014.02.002 Peñuelas, J., & Inoue, Y. (1999). Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves. Photosynthetica, 36(3), 355–360. https://doi.org/10.1023 / A: 1007033503276 Peñuelas, J., Pinol, J., Ogaya, R., & Filella, I. (1997). Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). International Journal of Remote Sensing, 18(13), 2869–2875. https://doi.org/10.1080/014311697217396 Perakis, K., Lampathaki, F., Nikas, K., Georgiou, Y., Marko, O., & Maselyne, J. (2020). CYBELE – Fostering precision agriculture & livestock farming through secure access to large-scale HPC enabled virtual industrial experimentation environments fostering scalable big data analytics. Computer Networks, 168. https://doi.org/10.1016/j.comnet.2019.107035 Poblete, T., Ortega-Farías, S., & Ryu, D. (2018). Automatic coregistration algorithm to remove canopy shaded pixels in UAV-borne thermal images to improve the estimation of crop water stress index of a drip-irrigated cabernet sauvignon vineyard. Sensors (Switzerland). https://doi.org/10.3390/s18020397 Poirier-Pocovi, M., Volder, A., & Bailey, B. N. (2020). Modeling of reference temperatures for calculating crop water stress indices from infrared thermography. Agricultural Water Management, 233(December 2019), 106070. https://doi.org/10.1016/j.agwat.2020.106070 Pou, A., Diago, M. P., Medrano, H., Baluja, J., & Tardaguila, J. (2014). Validation of thermal indices for water status identification in grapevine. Agricultural Water Management, 134, 60–72. https://doi.org/10.1016/j.agwat.2013.11.010 Quebrajo, L., Perez-Ruiz, M., Pérez-Urrestarazu, L., Martínez, G., & Egea, G. (2018). Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet. Biosystems Engineering, 165, 77–87. https://doi.org/10.1016/J.BIOSYSTEMSENG.2017.08.013 Ray, S. S., & Jain, N. (2011). Utility of Hyperspectral Data for Potato Late Blight Disease Detection. 39(June), 161–169. https://doi.org/10.1007/s12524-011-0094-2 Raza, S. E. A., Prince, G., Clarkson, J. P., & Rajpoot, N. M. (2015). Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS ONE, 10(4), 1–20. https://doi.org/10.1371/journal.pone.0123262 Raza, S. E. A., Smith, H. K., Clarkson, G. J. J., Taylor, G., Thompson, A. J., Clarkson, J., & Rajpoot, N. M. (2014). Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery. PLoS ONE, 9(6), 1–10. https://doi.org/10.1371/journal.pone.0097612 Ribeiro da Luz, B., & Crowley, J. K. (2007). Spectral reflectance and emissivity features of broad leaf plants: Prospects for remote sensing in the thermal infrared (8.0-14.0 μm). Remote Sensing of Environment, 109(4), 393–405. https://doi.org/10.1016/j.rse.2007.01.008 Ribera-Fonseca, A., Jorquera-Fontena, E., Castro, M., Acevedo, P., Parra, J. C., & Reyes-Diaz, M. (2019). Exploring VIS/NIR reflectance indices for the estimation of water status in highbush blueberry plants grown under full and deficit irrigation. Scientia Horticulturae, 256(April), 108557. https://doi.org/10.1016/j.scienta.2019.108557 Rodríguez, L. E., Ñustez, C., & Estrada, N. (2009). Criolla Latina, Criolla Paisa y Criolla Colombia, nuevos cultivares de papa criolla para el departamento de Antioquia (Colombia). Agronomia Colombiana, 27(3), 289–303. Rodríguez-Pérez, L., Ñústez L., C. E., & Moreno F., L. P. (2017). El estrés por sequía afecta los parámetros fisiológicos, pero no el rendimiento de los tubérculos en tres cultivares andinos de papa (Solanum tuberosum L.). Agronomia Colombiana, 35(2), 158–170. https://doi.org/10.15446/agron.colomb.v35n2.65901 Romero, A. P., Alarcón, A., Valbuena, R. I., & Galeano, C. H. (2017). Physiological assessment of water stress in potato using spectral information. Frontiers in Plant Science, 8(September). https://doi.org/10.3389/fpls.2017.01608 Rouse, J. W. J., Haas, R. H., Deering, D. W., Shell, J. A., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation, NASA/GSFC Type III Final Report: Greenbelt, MD, USA. 371. Rud, R., Cohen, Y., Alchanatis, V., Levi, A., Brikman, R., Shenderey, C., Heuer, B., Markovitch, T., Dar, Z., Rosen, C., Mulla, D., & Nigon, T. (2014). Crop water stress index derived from multi-year ground and aerial thermal images as an indicator of potato water status. Precision Agriculture, 15, 273–289. https://doi.org/10.1007/s11119-014-9351-z Salgadoe, A. S. A., Robson, A. J., Lamb, D. W., & Schneider, D. (2019). A non-reference temperature histogram method for determining Tc from ground-based thermal imagery of orchard tree canopies. Remote Sensing, 11(6). https://doi.org/10.3390/RS11060714 Santesteban, L. G., di Gennaro, S. F., Herrero-Langreo, A., Miranda, C., Royo, J. B., & Matese, A. (2017). High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2016.08.026 Schellberg, J., Hill, M. J., Gerhards, R., Rothmund, M., & Braun, M. (2008). Precision agriculture on grassland: Applications, perspectives and constraints. European Journal of Agronomy, 29(2–3), 59–71. https://doi.org/10.1016/j.eja.2008.05.005 Scholander, P. F., Hammel, H. T., Bradstreet, E. D., & Hemmingsen, E. A. (1965). Sap pressure in vascular plants. Science, 148(3668), 339–346. https://doi.org/10.1126/science.148.3668.339 Seelig, H. D., Hoehn, A., Stodieck, L. S., Klaus, D. M., Adams, W. W., & Emery, W. J. (2008). Relations of remote sensing leaf water indices to leaf water thickness in cowpea, bean, and sugarbeet plants. Remote Sensing of Environment, 112(2), 445–455. https://doi.org/10.1016/j.rse.2007.05.002 Senthilnath, J., Kandukuri, M., Dokania, A., & Ramesh, K. N. (2017). Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods. Computers and Electronics in Agriculture, 140, 8–24. https://doi.org/10.1016/j.compag.2017.05.027 Stark, B., Smith, B., & Chen, Y. (2014). Survey of thermal infrared remote sensing for Unmanned Aerial Systems. 2014 International Conference on Unmanned Aircraft Systems (ICUAS), 1294–1299. https://doi.org/10.1109/ICUAS.2014.6842387 Struthers, R., Ivanova, A., Tits, L., Swennen, R., & Coppin, P. (2015). Thermal infrared imaging of the temporal variability in stomatal conductance for fruit trees. International Journal of Applied Earth Observation and Geoinformation, 39, 9–17. https://doi.org/10.1016/j.jag.2015.02.006 Tilling, A. K., O’Leary, G. J., Ferwerda, J. G., Jones, S. D., Fitzgerald, G. J., Rodriguez, D., & Belford, R. (2007). Remote sensing of nitrogen and water stress in wheat. Field Crops Research, 104(1–3), 77–85. https://doi.org/10.1016/j.fcr.2007.03.023 Tu, Y.-H., Johansen, K., Phinn, S., & Robson, A. J. (2019). Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment. Remote Sensing, 11(269), 15–17. https://doi.org/10.3390/rs11030269 Varo-Martínez, M. Á., Navarro-Cerrillo, R. M., Hernández-Clemente, R., & Duque-Lazo, J. (2017). Semi-automated stand delineation in Mediterranean Pinus sylvestris plantations through segmentation of LiDAR data: The influence of pulse density. International Journal of Applied Earth Observation and Geoinformation, 56, 54–64. https://doi.org/10.1016/j.jag.2016.12.002 Vergara-Díaz, O., Zaman-Allah, M. A., Masuka, B., Hornero, A., Zarco-Tejada, P., Prasanna, B. M., Cairns, J. E., & Araus, J. L. (2016). A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2016.00666 Vollmer, M., & Möllmann, K.-P. (2018). Infrared Thermal Imaging (Second Edi). WILEY-VCH Verlag GmbH & Co.KGaA. Wang, X., Yang, W., Wheaton, A., Cooley, N., & Moran, B. (2010). Automated canopy temperature estimation via infrared thermography: A first step towards automated plant water stress monitoring. Computers and Electronics in Agriculture, 73(1), 74–83. https://doi.org/10.1016/j.compag.2010.04.007 Zarco-Tejada, P. J., González-Dugo, V., & Berni, J. A. J. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment, 117, 322–337. https://doi.org/10.1016/j.rse.2011.10.007 Zarco-Tejada, P. J., Rueda, C. A., & Ustin, S. L. (2003). Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85(1), 109–124. https://doi.org/10.1016/S0034-4257(02)00197-9 Zhou, J., Pavek, M. J., Shelton, S. C., Holden, Z. J., & Sankaran, S. (2016). Aerial multispectral imaging for crop hail damage assessment in potato. Computers and Electronics in Agriculture, 127, 406–412. https://doi.org/10.1016/j.compag.2016.06.019 Zhou, X., Huang, W., Kong, W., Ye, H., Luo, J., & Chen, P. (2016). Remote estimation of canopy nitrogen content in winter wheat using airborne hyperspectral reflectance measurements. Advances in Space Research, 58(9), 1627–1637. https://doi.org/10.1016/j.asr.2016.06.034 Zhou, Z., Majeed, Y., Diverres Naranjo, G., & Gambacorta, E. M. T. (2021). Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications. Computers and Electronics in Agriculture, 182(February). https://doi.org/10.1016/j.compag.2021.106019 Zia, S., Spohrer, K., Merkt, N., Wenyong, D., He, X., & Joachim, M. (2014). Non-invasive water status detection in grapevine ( Vitis vinifera L .) by thermography Non-invasive water status detection in grapevine ( Vitis vinifera L .) by thermography. January 2010. https://doi.org/10.3965/j.issn.1934-6344.2009.04.046-054
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	xvi, 80 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias Agrarias - Maestría en Geomática
dc.publisher.department.spa.fl_str_mv	Departamento de Agronomía
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias Agrarias
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/82236/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/82236/2/1030578888.2022.pdf
bitstream.checksum.fl_str_mv	8a4605be74aa9ea9d79846c1fba20a33 f1db14fc3f0378848c90fba4947da5d7
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
_version_	1806886626722316288
spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Martínez Martínez, Luis Joel94d011bd9a7f169197ab0a1837a443b9Rodríguez Molano, Luis Ernesto31367cf7e0e2a380de113594da90c09fVelandia Sánchez, Edisson Andrése4b541cd77aa15feb9fb6105f8b8704f2022-09-01T16:08:40Z2022-09-01T16:08:40Z2022https://repositorio.unal.edu.co/handle/unal/82236Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficasLa papa amarilla diploide (Solanum tuberosum Grupo Phureja) es susceptible a condiciones de déficit hídrico, afectando negativamente el potencial de rendimiento. La variabilidad climática aumenta la frecuencia de la sequía, por lo que es necesario generar estrategias que permitan diagnosticar a tiempo y así mitigar los efectos causados por el estrés hídrico en el cultivo. El objetivo de este trabajo fue evaluar el uso de imágenes térmicas y la respuesta espectral para identificar condiciones de estrés hídrico y estado nutricional con relación al N en papa amarilla diploide (Solanum tuberosum Grupo Phureja) cv. Criolla Colombia bajo invernadero. Se establecieron tubérculos-semilla en bolsas con suelo de siete litros de capacidad regadas cada tercer día a capacidad de campo hasta el inicio de tuberización 45 dds (días después de siembra), sometidas a dos regímenes hídricos: i) riego continuo (CW) y, ii) déficit hídrico por suspensión de riego total (SW) durante 13 días, las dosis de fertilización con N fueron 0%, 50%, 100% y 150% de la dosis comercial utilizada para el cultivo. Se usó un modelo factorial completamente al azar de medidas repetidas y análisis descriptivo. Se encontró que a partir de la TD se pudo determinar la deficiencia de agua en las plantas destacando que, bajo condiciones de invernadero, desde el día cinco ddt fue posible detectar el déficit hídrico que presentaron las plantas del cv. Criolla Colombia por medio de la temperatura proveniente de las imágenes térmicas, y con mayor claridad hacia los siete ddt. Se propuso el índice MED556 como importante para la determinación de N en las plantas. Los resultados revelaron índices espectrales como el NDVI y PRInorm presentaron una relación con el LN desde el primer muestreo a los 3 ddt, siendo parámetros que favorablemente se puede usar para determinar el estado del N en las plantas, mientras que índices como el WI representaron mejor el experimento para la determinación del estado hídrico de las plantas. (Texto tomado de la fuente)Diploid yellow potato (Solanum tuberosum Phureja Group) is susceptible to water deficit conditions, negatively affecting yield potential. Climate variability increases the frequency of drought, so it is necessary to generate strategies that allow early diagnosis and thus mitigate the effects caused by water stress on the crop. The objective of this work was to evaluate the use of thermal imaging and spectral response to identify water stress conditions and nutritional status in relation to N in yellow diploid potato (Solanum tuberosum Phureja Group) cv. Criolla Colombia in greenhouse conditions. Seed tubers were established in seven-liter bags with soil, irrigated every third day at field capacity until the onset of tuberization 45 dds (days after planting), subjected to two water regimes: i) continuous irrigation (CW) and, ii) water deficit by suspension of total irrigation (SW) for 13 days, the N fertilization doses were 0%, 50%, 100% and 150% of the commercial dose used for the crop. A completely randomized factorial model with repeated measures and descriptive analysis was used. It was found that from the TD it was possible to determine the water deficiency in the plants, highlighting that, under greenhouse conditions, from day five ddt it was possible to detect the water deficit in the plants of the Criolla Colombia cv. by means of the temperature from the thermal images, and with greater clarity at seven ddt. The MED556 index was proposed as important for the determination of N in the plants. The results revealed spectral indices such as NDVI and PRInorm presented a relationship with LN from the first sampling at 3 ddt, being parameters that can be favorably used to determine the N status of the plants, while indices such as WI better represented the experiment for the determination of the water status of the plants.MaestríaMagíster en GeomáticaGeoinformación para el uso sostenible de los recursos naturalesxvi, 80 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en GeomáticaDepartamento de AgronomíaFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá570 - Biología::571 - Fisiología y temas relacionadosEstrés de sequiadrought stressSolanum tuberosumTemperatura del doselÍndices espectralesEstado hídrico foliarEstrés por nitrógenoCanopy temperatureSpectral indicesLeaf water statusNitrogen stressImágenes térmicas y respuestas espectrales para identificar condiciones de estrés hídrico y estado nutricional con relación al nitrógeno en papa amarilla diploide (Solanum tuberosum Grupo Phureja)Thermal imaging and spectral responses to identify water stress conditions and nutritional status in relation to nitrogen in diploid yellow potato (Solanum tuberosum tuberosum Phureja Group)Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMRedColLaReferenciaAllen, R., Pereira, L., Raes, D., & Smith, M. (1998). Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56. FAO - Food and Agriculture Organization of the United Nations, 110–115. https://doi.org/10.1016/S0141-1187(05)80058-6Anderson, M. C., Hain, C., Otkin, J., Zhan, X., Mo, K., Svoboda, M., Wardlow, B., & Pimstein, A. (2013). An Intercomparison of Drought Indicators Based on Thermal Remote Sensing and NLDAS-2 Simulations with U.S. Drought Monitor Classifications. Journal of Hydrometeorology, 14(4), 1035–1056. https://doi.org/10.1175/JHM-D-12-0140.1Ariza, W. (2017). Respuestas fisiológicas, bioquímicas y rendimiento en tres variedades de papa criolla (Solanum tuberosums grupo Phureja) en déficit hídrico.Ariza, W., Rodríguez, L. E., Moreno-Echeverry, D., Guerrero, C. A., & Moreno, L. P. (2020). Effect of water deficit on some physiological and biochemical responses of the yellow diploid potato (Solanum tuberosum L. group phureja). Agronomia Colombiana, 38(1), 48–56. https://doi.org/10.15446/agron.colomb.v38n1.78982Babich, G. A., & Camps, O. I. (1996). Weighted Parzen windows for pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(5), 567–570. https://doi.org/10.1109/34.494647Banerjee, K., Krishnan, P., & Mridha, N. (2018). Application of thermal imaging of wheat crop canopy to estimate leaf area index under different moisture stress conditions. Biosystems Engineering, 166, 13–27. https://doi.org/10.1016/j.biosystemseng.2017.10.012Barragán, J. N. (2019). La Papa Incluida - Desempeño y perspectivas económicas del subsector papa 2018-2019. Revista Papa, 47, pag 45-48. https://fedepapa.com/wp-content/uploads/2017/01/REVISTA-47-COMPLETA.pdfBendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M. L., & Bareth, G. (2015). Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39, 79–87. https://doi.org/10.1016/j.jag.2015.02.012Berni, J. A. J., Zarco-Tejada, P. J., Sepulcre-Cantó, G., Fereres, E., & Villalobos, F. (2009). Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sensing of Environment, 113(11), 2380–2388. https://doi.org/10.1016/j.rse.2009.06.018Borhan, M. S., Panigrahi, S., Satter, M. A., & Gu, H. (2017). Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves. Information Processing in Agriculture, 4(4), 275–282. https://doi.org/10.1016/j.inpa.2017.07.005Buitrago, M. F., Groen, T. A., Hecker, C. A., & Skidmore, A. K. (2016). Changes in thermal infrared spectra of plants caused by temperature and water stress. ISPRS Journal of Photogrammetry and Remote Sensing, 111, 22–31. https://doi.org/10.1016/j.isprsjprs.2015.11.003Campos, H., & Ortíz, O. (2020). The Potato Crop: Its Agricultural, Nutritional and Social Contribution to Humankind (H. Campos & O. Ortíz, Eds.). Springer. https://doi.org/https://doi.org/10.1007/978-3-030-28683-5Cao, Q., Miao, Y., Feng, G., Gao, X., Li, F., Liu, B., Yue, S., Cheng, S., Ustin, S. L., & Khosla, R. (2015). Active canopy sensing of winter wheat nitrogen status: An evaluation of two sensor systems. Computers and Electronics in Agriculture, 112, 54–67. https://doi.org/10.1016/j.compag.2014.08.012Cavender-Bares, J., Gamon, J. A., & Townsend, P. A. (2020). Remote sensing of plant biodiversity. In Remote Sensing of Plant Biodiversity. https://doi.org/10.1007/978-3-030-33157-3Cho, M. A., & Skidmore, A. K. (2006). A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sensing of Environment, 101(2), 181–193. https://doi.org/10.1016/j.rse.2005.12.011Cilia, C., Panigada, C., Rossini, M., Meroni, M., Busetto, L., Amaducci, S., Boschetti, M., Picchi, V., & Colombo, R. (2014). Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sensing, 6(7), 6549–6565. https://doi.org/10.3390/rs6076549Çolak, Y., Yazar, A., Sesveren, S., & Çolak, I. (2017). Evaluation of yield and leaf water potantial ( LWP ) for eggplant under varying irrigation regimes using surface and subsurface drip systems Yes. Scientia Horticulturae, 219, 10–21. https://doi.org/10.1016/j.scienta.2017.02.051Costa, J. M., Egipto, R., Sánchez-Virosta, A., Lopes, C. M., & Chaves, M. M. (2019). Canopy and soil thermal patterns to support water and heat stress management in vineyards. Agricultural Water Management, 216(November 2017), 484–496. https://doi.org/10.1016/j.agwat.2018.06.001Cruz De Carvalho, M. H. (2008a). Drought stress and reactive oxygen species: Production, scavenging and signaling. Plant Signaling and Behavior, 3(3), 156–165. https://doi.org/10.4161/psb.3.3.5536Cruz De Carvalho, M. H. (2008b). Drought stress and reactive oxygen species: Production, scavenging and signaling. Plant Signaling and Behavior, 3(3), 156–165. https://doi.org/10.4161/psb.3.3.5536Cucho-Padin, G., Rinza, J., Ninanya, J., Loayza, H., Quiroz, R., & Ramírez, D. A. (2020). Development of an open-source thermal image processing software for improving irrigation management in potato crops (Solanum tuberosum L.). Sensors (Switzerland), 20(2), 1–17. https://doi.org/10.3390/s20020472Cunliffe, A. M., Brazier, R. E., & Anderson, K. (2016). Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sensing of Environment, 183, 129–143. https://doi.org/10.1016/j.rse.2016.05.019Dalla Costa, L., Delle Vedove, G., Gianquinto, G., Giovanardi, R., & Peressotti, A. (1997). Yield, water use efficiency and nitrogen uptake in potato: Influence of drought stress. Potato Research, 40(1), 19–34. https://doi.org/10.1007/BF02407559DeJonge, K. C., Taghvaeian, S., Trout, T. J., & Comas, L. H. (2015). Comparison of canopy temperature-based water stress indices for maize. Agricultural Water Management, 156, 51–62. https://doi.org/10.1016/j.agwat.2015.03.023Devaux, A., Kromann, P., & Ortiz, O. (2014). Potatoes for Sustainable Global Food Security. Potato Research, 57(3–4), 185–199. https://doi.org/10.1007/s11540-014-9265-1Duan, T., Chapman, S. C., Guo, Y., & Zheng, B. (2017). Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle. Field Crops Research, 210(May), 71–80. https://doi.org/10.1016/j.fcr.2017.05.025Egea, G., Padilla, C. M., Martinez, J., Fernández, J. E., & Pérez, M. (2017). Assessing a crop water stress index derived from aerial thermal imaging and infrared thermometry in super-high density olive orchards. Agricultural Water Management, 187, 210–221. https://doi.org/10.1016/J.AGWAT.2017.03.030Ezenne, G. I., Jupp, L., Mantel, S. K., & Tanner, J. L. (2019). Current and potential capabilities of UAS for crop water productivity in precision agriculture. Agricultural Water Management, 218(March), 158–164. https://doi.org/10.1016/j.agwat.2019.03.034FAO. (2016). Flying robots for food security. Food and Agriculture Organization of the United Nations. http://www.fao.org/zhc/detail-events/en/c/428256/Far, S. T., & Rezaei-Moghaddam, K. (2018). Impacts of the precision agricultural technologies in Iran: An analysis experts’ perception & their determinants. Information Processing in Agriculture, 5(1), 173–184. https://doi.org/10.1016/j.inpa.2017.09.001Feng, R., Zhang, Y., Yu, W., Hu, W., Wu, J., Ji, R., Wang, H., & Zhao, X. (2013). Analysis of the relationship between the spectral characteristics of maize canopy and leaf area index under drought stress. Acta Ecologica Sinica, 33(6), 301–307. https://doi.org/10.1016/j.chnaes.2013.09.001Gabriel, J. L., Zarco-tejada, P. J., Juan, P. L., Alonso-ayuso, M., Quemada, M., Enrique, P., & Obispo, S. (2017). Airborne and ground level sensors for monitoring nitrogen status in a maize crop. Biosystems Engineering, 160, 124–133. https://doi.org/10.1016/j.biosystemseng.2017.06.003Gao, B.-C. (1996). NDWI A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space. Remote Sens. Environ, 7212(April), 257–266.García-Tejero, I. F., Gutiérrez-Gordillo, S., Ortega-Arévalo, C., Iglesias-Contreras, M., Moreno, J. M., Souza-Ferreira, L., & Durán-Zuazo, V. H. (2018). Thermal imaging to monitor the crop-water status in almonds by using the non-water stress baselines. Scientia Horticulturae, 238(April), 91–97. https://doi.org/10.1016/j.scienta.2018.04.045García-Tejero, I., Rubio, A. E., Viñuela, I., Hernández, A., Gutiérrez-Gordillo, S., Rodríguez-Pleguezuelo, C. R., & Durán-Zuazo, V. H. (2018). Thermal imaging at plant level to assess the crop-water status in almond trees (cv. Guara) under deficit irrigation strategies. Agricultural Water Management, 208(May), 176–186. https://doi.org/10.1016/j.agwat.2018.06.002George, T. S., Taylor, M. A., Dodd, I. C., & White, P. J. (2018). Climate Change and Consequences for Potato Production: a Review of Tolerance to Emerging Abiotic Stress. Potato Research, 60(3–4), 239–268. https://doi.org/10.1007/s11540-018-9366-3Gerhards, M., Rock, G., Schlerf, M., & Udelhoven, T. (2016). Water stress detection in potato plants using leaf temperature, emissivity, and reflectance. International Journal of Applied Earth Observation and Geoinformation, 53, 27–39. https://doi.org/10.1016/j.jag.2016.08.004Getahun, B. B. (2018). Potato Breeding for Nitrogen-Use Efficiency : Constraints , Achievements , and Future Prospects. 2018(10), 269–281.Giraldo, C., Velandia, E. A., Fischer, G., Martínez, L. J., & Gómez-Caro, S. (2020). Hyperspectral response of cape gooseberry (Physalis peruviana L.) plants inoculated with Fusarium oxysporum f. sp. physali for vascular wilt detection. Revista Colombiana de Ciencias Hortícolas, 14(November), 3–29. https://doi.org/10.17584/rcch.2020v14i3.10938Gonzalez-Dugo, V., Goldhamer, D., Zarco-Tejada, P. J., & Fereres, E. (2015). Improving the precision of irrigation in a pistachio farm using an unmanned airborne thermal system. Irrigation Science. https://doi.org/10.1007/s00271-014-0447-zGonzalez-Dugo, V., Zarco-Tejada, P., Berni, J. A. J., Suárez, L., Goldhamer, D., & Fereres, E. (2012). Almond tree canopy temperature reveals intra-crown variability that is water stress-dependent. Agricultural and Forest Meteorology, 154–155, 156–165. https://doi.org/10.1016/j.agrformet.2011.11.004Goyer, A. (2017). Maximizing the Nutritional Potential of Potato: the Case of Folate. Potato Research, 60(3–4), 319–325. https://doi.org/10.1007/s11540-018-9374-3Grant, O. M., Tronina, Ł., Jones, H. G., & Chaves, M. M. (2007). Exploring thermal imaging variables for the detection of stress responses in grapevine under different irrigation regimes. Journal of Experimental Botany, May 2014, 1–11. https://doi.org/10.1093/jxb/erl153Guo, J., Tian, G., Zhou, Y., Wang, M., Ling, N., Shen, Q., & Guo, S. (2016). Evaluation of the grain yield and nitrogen nutrient status of wheat (Triticum aestivum L.) using thermal imaging. Field Crops Research, 196, 463–472. https://doi.org/10.1016/j.fcr.2016.08.008Gupta, S. D., & Ibaraki, Y. (2015). Plant Image Analysis. In S. D. Gupta & Y. Ibaraki (Eds.), Plant Image Analysis. Taylor & Francis Group. https://doi.org/10.1201/b17441Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2–3), 416–426. https://doi.org/10.1016/S0034-4257(02)00018-4Han, M., Zhang, H., DeJonge, K. C., Comas, L. H., & Trout, T. J. (2016). Estimating maize water stress by standard deviation of canopy temperature in thermal imagery. Agricultural Water Management, 177, 400–409. https://doi.org/10.1016/j.agwat.2016.08.031Hu, D. W., Sun, Z. P., Li, T. L., Yan, H. Z., & Zhang, H. (2014). Nitrogen nutrition index and its relationship with N use efficiency, tuber yield, radiation use efficiency, and leaf parameters in potatoes. Journal of Integrative Agriculture, 13(5), 1008–1016. https://doi.org/10.1016/S2095-3119(13)60408-6Hunt, R., & Rock, B. (1989). Detection of changes in leaf water content using Near- and Middle-Infrared reflectances. Remote Sensing and Enviroment, 30(1), 43–54. https://doi.org/10.1016/0034-4257(89)90046-1Hussain, H., Hussain, S., Khaliq, A., Ashraf, U., & Anjum, S. (2018). Chilling and Drought Stresses in Crop Plants : Implications , Cross Talk , and Potential Management Opportunities. Frontiers in Plant Science, 9(April), 1–21. https://doi.org/10.3389/fpls.2018.00393Ihuoma, S. O., & Madramootoo, C. A. (2017). Recent advances in crop water stress detection. Computers and Electronics in Agriculture, 141, 267–275. https://doi.org/10.1016/j.compag.2017.07.026Ishida, T., Kurihara, J., Viray, F. A., Namuco, S. B., Paringit, E. C., Perez, G. J., Takahashi, Y., & Marciano, J. J. (2018). A novel approach for vegetation classification using UAV-based hyperspectral imaging. Computers and Electronics in Agriculture, 144(November 2017), 80–85. https://doi.org/10.1016/j.compag.2017.11.027Kassambara, A., & Mundt, F. (2017). Factoextra extract and visualize the results of multivariate data analyses (pp. 337–354).Khanal, S., Fulton, J., & Shearer, S. (2017). An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture, 139, 22–32. https://doi.org/10.1016/j.compag.2017.05.001Khorsandi, A., Hemmat, A., Mireei, S. A., Amirfattahi, R., & Ehsanzadeh, P. (2018). Plant temperature-based indices using infrared thermography for detecting water status in sesame under greenhouse conditions. Agricultural Water Management, 204, 222–233. https://doi.org/10.1016/j.agwat.2018.04.012Kim, Y., Glenn, D. M., Park, J., Ngugi, H. K., & Lehman, B. L. (2011). Hyperspectral image analysis for water stress detection of apple trees. Computers and Electronics in Agriculture, 77(2), 155–160. https://doi.org/10.1016/j.compag.2011.04.008Kullberg, E. G., DeJonge, K. C., & Chávez, J. L. (2017). Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients. Agricultural Water Management, 179, 64–73. https://doi.org/10.1016/j.agwat.2016.07.007Lahlou, O., Ouattar, S., & Ledent, J. (2003). The effect of drought and cultivar on growth parameters, yield and yield components of potato. Agronomie, 23, 257–268. https://doi.org/10.1051/agroLê, S., Josse, J., & Husson, F. (2008). FactoMineR: An R package for multivariate analysis. Journal of Statistical Software, 25(1), 1–18. https://doi.org/10.18637/jss.v025.i01Li, F., Miao, Y., Feng, G., Yuan, F., Yue, S., Gao, X., Liu, Y., Liu, B., Ustin, S. L., & Chen, X. (2014). Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Research, 157, 111–123. https://doi.org/10.1016/j.fcr.2013.12.018Liu, F., Jensen, C. R., Shahanzari, A., Andersen, M. N., & Jacobsen, S. E. (2005). ABA regulated stomatal control and photosynthetic water use efficiency of potato (Solanum tuberosum L.) during progressive soil drying. Plant Science, 168(3), 831–836. https://doi.org/10.1016/j.plantsci.2004.10.016Liu, T., Li, R., Zhong, X., Jiang, M., Jin, X., Zhou, P., Liu, S., Sun, C., & Guo, W. (2018). Estimates of rice lodging using indices derived from UAV visible and thermal infrared images. Agricultural and Forest Meteorology, 252, 144–154. https://doi.org/10.1016/J.AGRFORMET.2018.01.021Mahlein, A. K., Rumpf, T., Welke, P., Dehne, H. W., Plümer, L., Steiner, U., & Oerke, E. C. (2013). Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128, 21–30. https://doi.org/10.1016/j.rse.2012.09.019Mahmud, A., Hossain, M. M., Zakaria, M., Mian, M. A. K., & Karim, M. A. (2015). Effects of water stress on plant canopy, yield attributes and yield of potato. Kasetsart Journal - Natural Science, 49(4), 491–505.Mangus, D. L., Sharda, A., & Zhang, N. (2016). Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse. Computers and Electronics in Agriculture, 121, 149–159. https://doi.org/10.1016/J.COMPAG.2015.12.007Martinez, L. J., & Ramos, A. (2015). Estimation of chlorophyll concentration in maize using spectral reflectance. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 40(7W3), 65–71. https://doi.org/10.5194/isprsarchives-XL-7-W3-65-2015Mehrabi, F., & Sepaskhah, A. R. (2019). Partial root zone drying irrigation, planting methods and nitrogen fertilization influence on physiologic and agronomic parameters of winter wheat. Agricultural Water Management, 223(January), 105688. https://doi.org/10.1016/j.agwat.2019.105688Milroy, S. P., Wang, P., & Sadras, V. (2019). Field Crops Research De fi ning upper limits of nitrogen uptake and nitrogen use e ffi ciency of potato in response to crop N supply. Field Crops Research, 239(May), 38–46. https://doi.org/10.1016/j.fcr.2019.05.011Ministerio de Agricultura y Desarrollo Sostenible. (2019). ESTRATEGIA DE ORDENAMIENTO DE LA PRODUCCIÓN - CADENA PRODUCTIVA DE LA PAPA Y SU INDUSTRIA. In Plan de ordenamiento papa 2019-2023. https://sioc.minagricultura.gov.co/Papa/Normatividad/Plan de Ordenamiento papa 2019-2023.pdfMohd Asaari, M. S., Mishra, P., Mertens, S., Dhondt, S., Inzé, D., Wuyts, N., & Scheunders, P. (2018). Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform. ISPRS Journal of Photogrammetry and Remote Sensing, 138, 121–138. https://doi.org/10.1016/j.isprsjprs.2018.02.003Mompié, E., Martín, R., & Morales, D. (2015). Comportamiento de la acumulación y distribución de masa seca en tres variedades de papa (Solanum tuberosum L .). Cultivos Tropicales, 36(4), 70–76.Motalebifard, R., Najafi, N., Oustan, S., Nyshabouri, M. R., & Valizadeh, M. (2013). The combined effects of phosphorus and zinc on evapotranspiration, leaf water potential, water use efficiency and tuber attributes of potato under water deficit conditions. Scientia Horticulturae, 162, 31–38. https://doi.org/10.1016/j.scienta.2013.07.043Munnaf, M. A., Haesaert, G., van Meirvenne, M., & Mouazen, A. M. (2020). Map-based site-specific seeding of consumption potato production using high-resolution soil and crop data fusion. Computers and Electronics in Agriculture, 178(July), 105752. https://doi.org/10.1016/j.compag.2020.105752O’Shaughnessy, S. A., Evett, S. R., Colaizzi, P. D., & Howell, T. A. (2011). Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton. Agricultural Water Management, 98(10), 1523–1535. https://doi.org/10.1016/j.agwat.2011.05.005Pancorbo, J. L., Camino, C., Alonso-Ayuso, M., Raya-Sereno, M. D., Gonzalez-Fernandez, I., Gabriel, J. L., Zarco-Tejada, P. J., & Quemada, M. (2021). Simultaneous assessment of nitrogen and water status in winter wheat using hyperspectral and thermal sensors. European Journal of Agronomy, 127(August 2020), 126287. https://doi.org/10.1016/j.eja.2021.126287Panigada, C., Rossini, M., Meroni, M., Cilia, C., Busetto, L., Amaducci, S., Boschetti, M., Cogliati, S., Picchi, V., Pinto, F., Marchesi, A., & Colombo, R. (2014). Fluorescence, PRI and canopy temperature for water stress detection in cereal crops. International Journal of Applied Earth Observation and Geoinformation, 30(1), 167–178. https://doi.org/10.1016/j.jag.2014.02.002Peñuelas, J., & Inoue, Y. (1999). Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves. Photosynthetica, 36(3), 355–360. https://doi.org/10.1023 / A: 1007033503276Peñuelas, J., Pinol, J., Ogaya, R., & Filella, I. (1997). Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). International Journal of Remote Sensing, 18(13), 2869–2875. https://doi.org/10.1080/014311697217396Perakis, K., Lampathaki, F., Nikas, K., Georgiou, Y., Marko, O., & Maselyne, J. (2020). CYBELE – Fostering precision agriculture & livestock farming through secure access to large-scale HPC enabled virtual industrial experimentation environments fostering scalable big data analytics. Computer Networks, 168. https://doi.org/10.1016/j.comnet.2019.107035Poblete, T., Ortega-Farías, S., & Ryu, D. (2018). Automatic coregistration algorithm to remove canopy shaded pixels in UAV-borne thermal images to improve the estimation of crop water stress index of a drip-irrigated cabernet sauvignon vineyard. Sensors (Switzerland). https://doi.org/10.3390/s18020397Poirier-Pocovi, M., Volder, A., & Bailey, B. N. (2020). Modeling of reference temperatures for calculating crop water stress indices from infrared thermography. Agricultural Water Management, 233(December 2019), 106070. https://doi.org/10.1016/j.agwat.2020.106070Pou, A., Diago, M. P., Medrano, H., Baluja, J., & Tardaguila, J. (2014). Validation of thermal indices for water status identification in grapevine. Agricultural Water Management, 134, 60–72. https://doi.org/10.1016/j.agwat.2013.11.010Quebrajo, L., Perez-Ruiz, M., Pérez-Urrestarazu, L., Martínez, G., & Egea, G. (2018). Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet. Biosystems Engineering, 165, 77–87. https://doi.org/10.1016/J.BIOSYSTEMSENG.2017.08.013Ray, S. S., & Jain, N. (2011). Utility of Hyperspectral Data for Potato Late Blight Disease Detection. 39(June), 161–169. https://doi.org/10.1007/s12524-011-0094-2Raza, S. E. A., Prince, G., Clarkson, J. P., & Rajpoot, N. M. (2015). Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS ONE, 10(4), 1–20. https://doi.org/10.1371/journal.pone.0123262Raza, S. E. A., Smith, H. K., Clarkson, G. J. J., Taylor, G., Thompson, A. J., Clarkson, J., & Rajpoot, N. M. (2014). Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery. PLoS ONE, 9(6), 1–10. https://doi.org/10.1371/journal.pone.0097612Ribeiro da Luz, B., & Crowley, J. K. (2007). Spectral reflectance and emissivity features of broad leaf plants: Prospects for remote sensing in the thermal infrared (8.0-14.0 μm). Remote Sensing of Environment, 109(4), 393–405. https://doi.org/10.1016/j.rse.2007.01.008Ribera-Fonseca, A., Jorquera-Fontena, E., Castro, M., Acevedo, P., Parra, J. C., & Reyes-Diaz, M. (2019). Exploring VIS/NIR reflectance indices for the estimation of water status in highbush blueberry plants grown under full and deficit irrigation. Scientia Horticulturae, 256(April), 108557. https://doi.org/10.1016/j.scienta.2019.108557Rodríguez, L. E., Ñustez, C., & Estrada, N. (2009). Criolla Latina, Criolla Paisa y Criolla Colombia, nuevos cultivares de papa criolla para el departamento de Antioquia (Colombia). Agronomia Colombiana, 27(3), 289–303.Rodríguez-Pérez, L., Ñústez L., C. E., & Moreno F., L. P. (2017). El estrés por sequía afecta los parámetros fisiológicos, pero no el rendimiento de los tubérculos en tres cultivares andinos de papa (Solanum tuberosum L.). Agronomia Colombiana, 35(2), 158–170. https://doi.org/10.15446/agron.colomb.v35n2.65901Romero, A. P., Alarcón, A., Valbuena, R. I., & Galeano, C. H. (2017). Physiological assessment of water stress in potato using spectral information. Frontiers in Plant Science, 8(September). https://doi.org/10.3389/fpls.2017.01608Rouse, J. W. J., Haas, R. H., Deering, D. W., Shell, J. A., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation, NASA/GSFC Type III Final Report: Greenbelt, MD, USA. 371.Rud, R., Cohen, Y., Alchanatis, V., Levi, A., Brikman, R., Shenderey, C., Heuer, B., Markovitch, T., Dar, Z., Rosen, C., Mulla, D., & Nigon, T. (2014). Crop water stress index derived from multi-year ground and aerial thermal images as an indicator of potato water status. Precision Agriculture, 15, 273–289. https://doi.org/10.1007/s11119-014-9351-zSalgadoe, A. S. A., Robson, A. J., Lamb, D. W., & Schneider, D. (2019). A non-reference temperature histogram method for determining Tc from ground-based thermal imagery of orchard tree canopies. Remote Sensing, 11(6). https://doi.org/10.3390/RS11060714Santesteban, L. G., di Gennaro, S. F., Herrero-Langreo, A., Miranda, C., Royo, J. B., & Matese, A. (2017). High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2016.08.026Schellberg, J., Hill, M. J., Gerhards, R., Rothmund, M., & Braun, M. (2008). Precision agriculture on grassland: Applications, perspectives and constraints. European Journal of Agronomy, 29(2–3), 59–71. https://doi.org/10.1016/j.eja.2008.05.005Scholander, P. F., Hammel, H. T., Bradstreet, E. D., & Hemmingsen, E. A. (1965). Sap pressure in vascular plants. Science, 148(3668), 339–346. https://doi.org/10.1126/science.148.3668.339Seelig, H. D., Hoehn, A., Stodieck, L. S., Klaus, D. M., Adams, W. W., & Emery, W. J. (2008). Relations of remote sensing leaf water indices to leaf water thickness in cowpea, bean, and sugarbeet plants. Remote Sensing of Environment, 112(2), 445–455. https://doi.org/10.1016/j.rse.2007.05.002Senthilnath, J., Kandukuri, M., Dokania, A., & Ramesh, K. N. (2017). Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods. Computers and Electronics in Agriculture, 140, 8–24. https://doi.org/10.1016/j.compag.2017.05.027Stark, B., Smith, B., & Chen, Y. (2014). Survey of thermal infrared remote sensing for Unmanned Aerial Systems. 2014 International Conference on Unmanned Aircraft Systems (ICUAS), 1294–1299. https://doi.org/10.1109/ICUAS.2014.6842387Struthers, R., Ivanova, A., Tits, L., Swennen, R., & Coppin, P. (2015). Thermal infrared imaging of the temporal variability in stomatal conductance for fruit trees. International Journal of Applied Earth Observation and Geoinformation, 39, 9–17. https://doi.org/10.1016/j.jag.2015.02.006Tilling, A. K., O’Leary, G. J., Ferwerda, J. G., Jones, S. D., Fitzgerald, G. J., Rodriguez, D., & Belford, R. (2007). Remote sensing of nitrogen and water stress in wheat. Field Crops Research, 104(1–3), 77–85. https://doi.org/10.1016/j.fcr.2007.03.023Tu, Y.-H., Johansen, K., Phinn, S., & Robson, A. J. (2019). Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment. Remote Sensing, 11(269), 15–17. https://doi.org/10.3390/rs11030269Varo-Martínez, M. Á., Navarro-Cerrillo, R. M., Hernández-Clemente, R., & Duque-Lazo, J. (2017). Semi-automated stand delineation in Mediterranean Pinus sylvestris plantations through segmentation of LiDAR data: The influence of pulse density. International Journal of Applied Earth Observation and Geoinformation, 56, 54–64. https://doi.org/10.1016/j.jag.2016.12.002Vergara-Díaz, O., Zaman-Allah, M. A., Masuka, B., Hornero, A., Zarco-Tejada, P., Prasanna, B. M., Cairns, J. E., & Araus, J. L. (2016). A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2016.00666Vollmer, M., & Möllmann, K.-P. (2018). Infrared Thermal Imaging (Second Edi). WILEY-VCH Verlag GmbH & Co.KGaA.Wang, X., Yang, W., Wheaton, A., Cooley, N., & Moran, B. (2010). Automated canopy temperature estimation via infrared thermography: A first step towards automated plant water stress monitoring. Computers and Electronics in Agriculture, 73(1), 74–83. https://doi.org/10.1016/j.compag.2010.04.007Zarco-Tejada, P. J., González-Dugo, V., & Berni, J. A. J. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment, 117, 322–337. https://doi.org/10.1016/j.rse.2011.10.007Zarco-Tejada, P. J., Rueda, C. A., & Ustin, S. L. (2003). Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85(1), 109–124. https://doi.org/10.1016/S0034-4257(02)00197-9Zhou, J., Pavek, M. J., Shelton, S. C., Holden, Z. J., & Sankaran, S. (2016). Aerial multispectral imaging for crop hail damage assessment in potato. Computers and Electronics in Agriculture, 127, 406–412. https://doi.org/10.1016/j.compag.2016.06.019Zhou, X., Huang, W., Kong, W., Ye, H., Luo, J., & Chen, P. (2016). Remote estimation of canopy nitrogen content in winter wheat using airborne hyperspectral reflectance measurements. Advances in Space Research, 58(9), 1627–1637. https://doi.org/10.1016/j.asr.2016.06.034Zhou, Z., Majeed, Y., Diverres Naranjo, G., & Gambacorta, E. M. T. (2021). Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications. Computers and Electronics in Agriculture, 182(February). https://doi.org/10.1016/j.compag.2021.106019Zia, S., Spohrer, K., Merkt, N., Wenyong, D., He, X., & Joachim, M. (2014). Non-invasive water status detection in grapevine ( Vitis vinifera L .) by thermography Non-invasive water status detection in grapevine ( Vitis vinifera L .) by thermography. January 2010. https://doi.org/10.3965/j.issn.1934-6344.2009.04.046-054USO DE IMÁGENES TÉRMICAS EN LA ESTIMACIÓN DEL ESTRÉS HÍDRICO EN PAPA (Solanum tuberosum Grupo Phureja)Centro de Investigación y Extensión Rural (CIER)EstudiantesInvestigadoresPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.unal.edu.co/bitstream/unal/82236/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51ORIGINAL1030578888.2022.pdf1030578888.2022.pdfTesis de Maestría en Geomáticaapplication/pdf8306410https://repositorio.unal.edu.co/bitstream/unal/82236/2/1030578888.2022.pdff1db14fc3f0378848c90fba4947da5d7MD52unal/82236oai:repositorio.unal.edu.co:unal/822362022-09-01 11:11:36.782Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Imágenes térmicas y respuestas espectrales para identificar condiciones de estrés hídrico y estado nutricional con relación al nitrógeno en papa amarilla diploide (Solanum tuberosum Grupo Phureja)

Publicaciones similares