Evaluación de la relación entre el estado nutricional y la respuesta espectral del cultivo de papa (Solanum tuberosum L.) para la estimación del contenido de nitrógeno y potasio
ilustraciones, diagramas, fotografías, tablas
- Autores:
-
Cristancho Rojas, Omar Yesid
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/86752
- Palabra clave:
- 630 - Agricultura y tecnologías relacionadas
640 - Gestión del hogar y vida familiar::641 - Alimentos y bebidas
PAPAS (TUBERCULOS)
HORTALIZAS DE RAIZ-CULTIVO
PAPAS (TUBERCULOS)-ABONOS Y FERTILIZANTES
CONTAMINACION DE SUELOS
PRODUCTOS QUIMICOS AGRICOLAS-ASPECTOS AMBIENTALES
NITROGENO COMO FERTILIZANTE
POTASIO COMO FERTILIZANTE
Potatoes
Root vegetables--Crops
Potatoes - fertilizers and manures
Soil pollution
Agricultural chemicals - environmental aspects
Nitrogen as fertilizer
Potassium as fertilizer
Índices de vegetación
Región Red-edge
Sensores de ion selectivo
Medidas repetidas en el tiempo
Vegetation indices
Red-edge region
Ion selective sensors
Repeated measures in time
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
id |
UNACIONAL2_b2ad72382a52397ef29ac3453589f7c7 |
---|---|
oai_identifier_str |
oai:repositorio.unal.edu.co:unal/86752 |
network_acronym_str |
UNACIONAL2 |
network_name_str |
Universidad Nacional de Colombia |
repository_id_str |
|
dc.title.spa.fl_str_mv |
Evaluación de la relación entre el estado nutricional y la respuesta espectral del cultivo de papa (Solanum tuberosum L.) para la estimación del contenido de nitrógeno y potasio |
dc.title.translated.eng.fl_str_mv |
Evaluation of the relationship between the nutritional status and the spectral response of the potato crop (Solanum tuberosum L.) for the estimation of nitrogen and potassium content |
title |
Evaluación de la relación entre el estado nutricional y la respuesta espectral del cultivo de papa (Solanum tuberosum L.) para la estimación del contenido de nitrógeno y potasio |
spellingShingle |
Evaluación de la relación entre el estado nutricional y la respuesta espectral del cultivo de papa (Solanum tuberosum L.) para la estimación del contenido de nitrógeno y potasio 630 - Agricultura y tecnologías relacionadas 640 - Gestión del hogar y vida familiar::641 - Alimentos y bebidas PAPAS (TUBERCULOS) HORTALIZAS DE RAIZ-CULTIVO PAPAS (TUBERCULOS)-ABONOS Y FERTILIZANTES CONTAMINACION DE SUELOS PRODUCTOS QUIMICOS AGRICOLAS-ASPECTOS AMBIENTALES NITROGENO COMO FERTILIZANTE POTASIO COMO FERTILIZANTE Potatoes Root vegetables--Crops Potatoes - fertilizers and manures Soil pollution Agricultural chemicals - environmental aspects Nitrogen as fertilizer Potassium as fertilizer Índices de vegetación Región Red-edge Sensores de ion selectivo Medidas repetidas en el tiempo Vegetation indices Red-edge region Ion selective sensors Repeated measures in time |
title_short |
Evaluación de la relación entre el estado nutricional y la respuesta espectral del cultivo de papa (Solanum tuberosum L.) para la estimación del contenido de nitrógeno y potasio |
title_full |
Evaluación de la relación entre el estado nutricional y la respuesta espectral del cultivo de papa (Solanum tuberosum L.) para la estimación del contenido de nitrógeno y potasio |
title_fullStr |
Evaluación de la relación entre el estado nutricional y la respuesta espectral del cultivo de papa (Solanum tuberosum L.) para la estimación del contenido de nitrógeno y potasio |
title_full_unstemmed |
Evaluación de la relación entre el estado nutricional y la respuesta espectral del cultivo de papa (Solanum tuberosum L.) para la estimación del contenido de nitrógeno y potasio |
title_sort |
Evaluación de la relación entre el estado nutricional y la respuesta espectral del cultivo de papa (Solanum tuberosum L.) para la estimación del contenido de nitrógeno y potasio |
dc.creator.fl_str_mv |
Cristancho Rojas, Omar Yesid |
dc.contributor.advisor.spa.fl_str_mv |
Martínez Martínez, Luis Joel Darghan Contreras, Aquiles Enrique |
dc.contributor.author.spa.fl_str_mv |
Cristancho Rojas, Omar Yesid |
dc.contributor.orcid.spa.fl_str_mv |
Cristancho Rojas, Omar Yesid [0000000246097632] |
dc.subject.ddc.spa.fl_str_mv |
630 - Agricultura y tecnologías relacionadas 640 - Gestión del hogar y vida familiar::641 - Alimentos y bebidas |
topic |
630 - Agricultura y tecnologías relacionadas 640 - Gestión del hogar y vida familiar::641 - Alimentos y bebidas PAPAS (TUBERCULOS) HORTALIZAS DE RAIZ-CULTIVO PAPAS (TUBERCULOS)-ABONOS Y FERTILIZANTES CONTAMINACION DE SUELOS PRODUCTOS QUIMICOS AGRICOLAS-ASPECTOS AMBIENTALES NITROGENO COMO FERTILIZANTE POTASIO COMO FERTILIZANTE Potatoes Root vegetables--Crops Potatoes - fertilizers and manures Soil pollution Agricultural chemicals - environmental aspects Nitrogen as fertilizer Potassium as fertilizer Índices de vegetación Región Red-edge Sensores de ion selectivo Medidas repetidas en el tiempo Vegetation indices Red-edge region Ion selective sensors Repeated measures in time |
dc.subject.lemb.spa.fl_str_mv |
PAPAS (TUBERCULOS) HORTALIZAS DE RAIZ-CULTIVO PAPAS (TUBERCULOS)-ABONOS Y FERTILIZANTES CONTAMINACION DE SUELOS PRODUCTOS QUIMICOS AGRICOLAS-ASPECTOS AMBIENTALES NITROGENO COMO FERTILIZANTE POTASIO COMO FERTILIZANTE |
dc.subject.lemb.eng.fl_str_mv |
Potatoes Root vegetables--Crops Potatoes - fertilizers and manures Soil pollution Agricultural chemicals - environmental aspects Nitrogen as fertilizer Potassium as fertilizer |
dc.subject.proposal.spa.fl_str_mv |
Índices de vegetación Región Red-edge Sensores de ion selectivo Medidas repetidas en el tiempo |
dc.subject.proposal.eng.fl_str_mv |
Vegetation indices Red-edge region Ion selective sensors Repeated measures in time |
description |
ilustraciones, diagramas, fotografías, tablas |
publishDate |
2023 |
dc.date.issued.none.fl_str_mv |
2023 |
dc.date.accessioned.none.fl_str_mv |
2024-08-26T14:05:24Z |
dc.date.available.none.fl_str_mv |
2024-08-26T14:05:24Z |
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/86752 |
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/86752 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 |
Agrosavia Agrovoc |
dc.relation.references.spa.fl_str_mv |
Abdel-Rahman, E. M., Mutanga, O., Odindi, J., Adam, E., Odindo, A., & Ismail, R. (2017). Estimating Swiss chard foliar macro- and micronutrient concentrations under different irrigation water sources using ground-based hyperspectral data and four partial least squares (PLS)-based (PLS1, PLS2, SPLS1 and SPLS2) regression algorithms. Computers and Electronics in Agriculture, 132, 21–33. https://doi.org/10.1016/j.compag.2016.11.008 Abutaleb, K., & Fatma Sayed, A. (2021a). Modeling Potato Yield Response to Different Nitrogen Application Rates Using Hyperspectral Data and PLS Regression. Journal of Horticultural Science & Ornamental Plants, 13(3), 301–310. https://doi.org/10.5829/idosi.jhsop.2021.301.310 Agisoft. (2019). Agisoft Metashape User Manual. 160. Alberto, R. T., Rivera, J. C. E., Biagtan, A. R., & Isip, M. F. (2019). Extraction of onion fields infected by anthracnose-twister disease in selected municipalities of Nueva Ecija using UAV imageries. Spatial Information Research. https://doi.org/10.1007/s41324-019-00302-z Asefpour Vakilian, K., & Massah, J. (2017). A farmer-assistant robot for nitrogen fertilizing management of greenhouse crops. Computers and Electronics in Agriculture, 139, 153–163. https://doi.org/10.1016/j.compag.2017.05.012 Basso, M., & Pignaton de Freitas, E. (2019). A UAV Guidance System Using Crop Row Detection and Line Follower Algorithms. Journal of Intelligent and Robotic Systems: Theory and Applications, 605–621. https://doi.org/10.1007/s10846-019-01006-0 Bautista, H. F., Ramírez, W. L., & Torres, J. (2012). Nutrient uptake of the diploid potato ( Solanum phureja ) variety Criolla Colombia , as a reference point to determine critical nutritional levels Absorción de nutrientes de la papa diploide ( Solanum phureja ) variedad Criolla Colombia , como punto de pa. Agronomia Colombiana, 30(3), 436–446. 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 Blackburn, G. A. (1998). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing, 19(4), 657–675. https://doi.org/10.1080/014311698215919 Botha, E. J., Leblon, B., Zebarth, B., & Watmough, J. (2007). Non-destructive estimation of potato leaf chlorophyll from canopy hyperspectral reflectance using the inverted PROSAIL model. 9, 360–374. https://doi.org/10.1016/j.jag.2006.11.003 Brunner, E. (2001). Asymptotic and approximate analysis of repeated measures designs under heteroscedasticity. Mathematical Statistics with Applications in Biometry. Cao, Q., Miao, Y., Wang, H., Huang, S., Cheng, S., Khosla, R., & Jiang, R. (2013). Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor. Field Crops Research, 154, 133–144. https://doi.org/10.1016/j.fcr.2013.08.005 Carter, G. A. (1994). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 15(3), 697–703. https://doi.org/10.1080/01431169408954109 Cen, H., Wan, L., Zhu, J., Li, Y., Li, X., Zhu, Y., Weng, H., Wu, W., Yin, W., Xu, C., Bao, Y., Feng, L., Shou, J., & He, Y. (2019). Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras. Plant Methods, 15(1), 1–16. https://doi.org/10.1186/s13007-019-0418-8 Chivasa, W., Mutanga, O., & Biradar, C. (2017). Application of remote sensing in estimating maize grain yield in heterogeneous african agricultural landscapes: A review. International Journal of Remote Sensing, 38(23), 6816–6845. https://doi.org/10.1080/01431161.2017.1365390 Corti, M., Cavalli, D., Cabassi, G., Vigoni, A., Degano, L., & Marino Gallina, P. (2019). Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables. Precision Agriculture, 20(4), 675–696. https://doi.org/10.1007/s11119-018-9609-y Cucho-Padin, G., Loayza, H., Palacios, S., Balcazar, M., Carbajal, M., & Quiroz, R. (2019). Development of low-cost remote sensing tools and methods for supporting smallholder agriculture. Applied Geomatics. https://doi.org/10.1007/s12518-019-00292-5 Datt, B. (1998). Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves. Remote Sensing of Environment, 66(2), 111–121. https://doi.org/10.1016/S0034-4257(98)00046-7 Delgado-Vera, C., Aguirre-Munizaga, M., Jiménez-Icaza, M., Manobanda-Herrera, N., & Rodríguez-Méndez, A. (2017). A Photogrammetry Software as a Tool for Precision Agriculture: A Case Study. In R. Valencia-García, K. Lagos-Ortiz, G. Alcaraz-Mármol, J. Del Cioppo, N. Vera-Lucio, & M. Bucaram-Leverone (Eds.), Technologies and Innovation (pp. 282–295). Springer International Publishing. Dutta Gupta, S., Ibaraki, Y., & Pattanayak, A. K. (2013). Development of a digital image analysis method for real-time estimation of chlorophyll content in micropropagated potato plants. Plant Biotechnology Reports, 7(1), 91–97. https://doi.org/10.1007/s11816-012-0240-5 FAOSTAT. (2018). Food and Agriculture Organization of United Nations. http://www.fao.org/faostat/es/#data/QC FEDEPAPA. (2018). Informe de gestión 2018. 188 FEDEPAPA. (2021). Federación Colombiana De Productores De Papa – Fedepapa Fondo Nacional De Fomento De La Papa – Fnfp. 555. Friedrich, S., Brunner, E., & Pauly, M. (2017). Permuting longitudinal data in spite of the dependencies. Journal of Multivariate Analysis, 153, 255–265. https://doi.org/10.1016/j.jmva.2016.10.004 Friedrich, S., & Pauly, M. (2018). MATS: Inference for potentially singular and heteroscedastic MANOVA. Journal of Multivariate Analysis, 165, 166–179. https://doi.org/10.1016/j.jmva.2017.12.008 Friedrich, Sarah., Konietschke, Frank., & Pauly, Markus. (2019). Resampling-based analysis of multivariate data and repeated measures designs with the R package MANOVA. RM. R Journal, 11(2), 380–400. https://doi.org/10.32614/rj-2019-051 Gamon, J. A., Serrano, L., & Surfus, J. S. (1997). The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia, 112(4), 492–501. https://doi.org/10.1007/s004420050337 Gandia, S., Fernández, G., García, J. C., & Moreno, J. F. (2004). RETRIEVAL OF VEGETATION BIOPHYSICAL VARIABLES FROM CHRIS/PROBA DATA IN THE SPARC CAMPAING. Gao, P., Zuo, Z., Zhang, R., Qiu, Y., He, R., Gao, R., & Gu, R. (2016). Optimum Nitrogen Fertilization for. Agronomy Journal, 108(1), 448–458. https://doi.org/10.2134/agronj2015.0324 Giletto, C. M., Reussi Calvo, N. I., Sandaña, P., Echeverría, H. E., & Bélanger, G. (2020). Shoot- and tuber-based critical nitrogen dilution curves for the prediction of the N status in potato. European Journal of Agronomy, 119(June), 126114. https://doi.org/10.1016/j.eja.2020.126114 Gitelson, A. A., Gritz, Y., & Merzlyak, M. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3), 271–282. Gitelson, A., Merzlyak, M., & Lichtenthaler, H. K. (1996). Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm. Journal of Plant Physiology, 148(3–4), 501–508. https://doi.org/10.1016/S0176-1617(96)80285-9 Gitelson, A., & Merzlyak, M. N. (1994). Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation. Journal of Plant Physiology, 143(3), 286–292. https://doi.org/10.1016/S0176-1617(11)81633-0 Gitelson, Anatoly., Kaufman, Y. J., & Merzlyak, Mark. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289–298. https://doi.org/10.1016/S0034-4257(96)00072-7 Gizaw, S. A., Garland-campbell, K., & Carter, A. H. (2016). Evaluation of agronomic traits and spectral reflectance in Pacific Northwest winter wheat under rain-fed and irrigated conditions. Field Crops Research. https://doi.org/10.1016/j.fcr.2016.06.018 Gómez, M. I., Magnitskiy, S., & Rodríguez, L. E. (2017). Diagnóstico de K + y NO 3- en savia para determinar el estado nutricional en papa ( Solanum tuberosum L . Diagnostics of K + and NO 3- in sap to determine nutritional status in potato ( Solanum tuberosum L . subsp . andigena ). Revista Colombiana de Ciencias Hortícolas, 11(3), 133–142. Gómez, M. I., Magnitskiy, S., & Rodríguez, L. E. (2019). Nitrogen, phosphorus and potassium accumulation and partitioning by the potato group Andigenum in Colombia. Nutrient Cycling in Agroecosystems, 113(3), 349–363. https://doi.org/10.1007/s10705-019-09986-z Gu, D.-D., Wang, W.-Z., Hu, J.-D., Zhang, X.-M., Wang, J.-B., & Wang, B.-S. (2016). Nondestructive Determination of Total Chlorophyll Content in Maize Using Three-Wavelength Diffuse Reflectance. Journal of Applied Spectroscopy, 83(4), 541–547. https://doi.org/10.1007/s10812-016-0325-y Guo, J., Zhang, J., Xiong, S., Zhang, Z., Wei, Q., Zhang, W., Feng, W., & Ma, X. (2021). Hyperspectral assessment of leaf nitrogen accumulation for winter wheat using different regression modeling. Precision Agriculture, 22(5), 1634–1658. https://doi.org/10.1007/s11119-021-09804-z 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 He, L., Song, X., Feng, W., Guo, B.-B., Zhang, Y.-S., Wang, Y.-H., Wang, C.-Y., & Guo, T.-C. (2016). Improved remote sensing of leaf nitrogen concentration in winter wheat using multi-angular hyperspectral data. Remote Sensing of Environment, 174, 122–133. https://doi.org/10.1016/j.rse.2015.12.007 Huete, A. R. (2004). R Emote S Ensing for. In M. L. Brusseau. anick F. Artiola, Ian L. Pepper (Ed.), Environmental Monitoring and Characterization (Firts, pp. 183–206). Academic Press. https://doi.org/10.1016/B978-0-12-064477-3.50013-8 Hunt, E. R., Donald, J., Spinelli, C. B., Turner, R. W., Bruce, A. E., Gadler, D. J., Brungardt, J. J., & Hamm, P. B. (2018). Monitoring nitrogen status of potatoes using small unmanned aerial vehicles. Precision Agriculture, 19(2), 314–333. https://doi.org/10.1007/s11119-017-9518-5 Hussain, A., Sahoo, R. N., Kumar, D., & Pradhan, S. (2017). Relationship of Hyperspectral Reflectance Indices with Leaf N and P Concentration, Dry Matter Accumulation and Grain Yield of Wheat. Journal of the Indian Society of Remote Sensing, 45(5), 773–784. https://doi.org/10.1007/s12524-016-0633-y Ierna, A., & Mauromicale, G. (2018). Potato growth, yield and water productivity response to different irrigation and fertilization regimes. Agricultural Water Management, 201(January), 21–26. https://doi.org/10.1016/j.agwat.2018.01.008 Ierna, A., & Mauromicale, G. (2019). Sustainable and profitable nitrogen fertilization management of potato. Agronomy, 9(10). https://doi.org/10.3390/agronomy9100582 Iglhaut, J., Cabo, C., Puliti, S., Piermattei, L., O’Connor, J., & Rosette, J. (2019). Structure from Motion Photogrammetry in Forestry: a Review. Current Forestry Reports, 5(3), 155–168. https://doi.org/10.1007/s40725-019-00094-3 Inostroza, L., Acuña, H., Munoz, P., Vásquez, C., Ibáñez, J., Tapia, G., Pino, M. T., & Aguilera, H. (2016). Using aerial images and canopy spectral reflectance for high-throughput phenotyping of white clover. Crop Science, 56(5), 2629–2637. https://doi.org/10.2135/cropsci2016.03.0156 Jay, S., Maupas, F., Bendoula, R., & Gorretta, N. (2017). Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping. Field Crops Research, 210(June), 33–46. https://doi.org/10.1016/j.fcr.2017.05.005 Jones, C. A., & Church, E. (2020). Photogrammetry is for everyone: Structure-from-motion software user experiences in archaeology. Journal of Archaeological Science: Reports, 30(March), 102261. https://doi.org/10.1016/j.jasrep.2020.102261 Kang, W., Fan, M., Ma, Z., Shi, X., & Zheng, H. (2014). Luxury absorption of potassium by potato plants. American Journal of Potato Research, 91(5), 573–578. https://doi.org/10.1007/s12230-014-9386-8 Kaur, R., Singh, B., Singh, M., & Thind, S. K. (2015). Hyperspectral Indices, Correlation and Regression Models for Estimating Growth Parameters of Wheat Genotypes. Journal of the Indian Society of Remote Sensing, 43(3), 551–558. https://doi.org/10.1007/s12524-014-0425-1 Koch, M., Naumann, M., Pawelzik, E., Gransee, A., & Thiel, H. (2019). The Importance of Nutrient Management for Potato Production Part I: Plant Nutrition and Yield. Potato Research, 97–119. https://doi.org/10.1007/s11540-019-09431-2 Konietschke, F., Bathke, A. C., Harrar, S. W., & Pauly, M. (2015). Parametric and nonparametric bootstrap methods for general MANOVA. Journal of Multivariate Analysis, 140, 291–301. https://doi.org/10.1016/j.jmva.2015.05.001 Laban, N., Abdellatif, B., Ebeid, H. M., Shedeed, H. A., & Tolba, M. F. (2019). Machine Learning for Enhancement Land Cover and Crop Types Classification. In A. E. Hassanien (Ed.), Machine Learning Paradigms: Theory and Application (pp. 71–87). Springer International Publishing. https://doi.org/10.1007/978-3-030-02357-7_4 Lee, J., & Sung, S. (2016). Evaluating spatial resolution for quality assurance of UAV images. Spatial Information Research, 24(2), 141–154. https://doi.org/10.1007/s41324-016-0015-0 Lehnert, L. W., Meyer, H., Obermeier, W. A., Silva, B., Regeling, B., Thies, B., & Bendix, J. (2019). Hyperspectral data analysis in R: The hsdar package. Journal of Statistical Software, 89(Ii). https://doi.org/10.18637/jss.v089.i12 Li, B., Xu, X., Han, J., Zhang, L., Bian, C., Jin, L., & Liu, J. (2019). The estimation of crop emergence in potatoes by UAV RGB imagery. Plant Methods, 15(1), 1–13. https://doi.org/10.1186/s13007-019-0399-7 Li, Bo., Xu, Xiangming., Zhang, Li., Han, Jiwan., Bian, Chunsong., Li, Guangcun., Liu, Jiangang., & Jin, Liping. (2020). Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 161–172. https://doi.org/10.1016/j.isprsjprs.2020.02.013 Liang, S. (2004). Quantitative Remote Sensing of Land Surfaces. In W. & Sons (Ed.), Wiley Series on Remote Sensing. Wiley & Sons. https://doi.org/10.1002/047172372X Licciardello, F., Lombardo, S., Rizzo, V., Pitino, I., Pandino, G., Strano, M. G., Muratore, G., Restuccia, C., & Mauromicale, G. (2018). Integrated agronomical and technological approach for the quality maintenance of ready-to-fry potato sticks during refrigerated storage. Postharvest Biology and Technology, 136(October 2017), 23–30. https://doi.org/10.1016/j.postharvbio.2017.10.001 Linder, W. (2006). Digital photogrammetry. 3rd. ed. 219 pp. https://doi.org/10.4324/9780203305959 Liu, B., Shen, W., Yue, Y. min, Li, R., Tong, Q., & Zhang, B. (2017). Combining spatial and spectral information to estimate chlorophyll contents of crop leaves with a field imaging spectroscopy system. Precision Agriculture, 18(4), 491–506. https://doi.org/10.1007/s11119-016-9466-5 Liu, N., Townsend, P. A., Naber, M. R., Bethke, P. C., Hills, W. B., & Wang, Y. (2021). Hyperspectral imagery to monitor crop nutrient status within and across growing seasons. Remote Sensing of Environment, 255(January). https://doi.org/10.1016/j.rse.2021.112303 Liu, Y., Feng, H., Yue, J., Jin, X., Fan, Y., Chen, R., Bian, M., Ma, Y., Song, X., & Yang, G. (2023). Improved potato AGB estimates based on UAV RGB and hyperspectral images. Computers and Electronics in Agriculture, 214(February). https://doi.org/10.1016/j.compag.2023.108260 Lizarazo, I., Rodriguez, J. L., Cristancho, O., Olaya, F., Duarte, M., & Prieto, F. (2023). Identification of symptoms related to potato Verticillium wilt from UAV-based multispectral imagery using an ensemble of gradient boosting machines. Smart Agricultural Technology, 3(November 2022). https://doi.org/10.1016/j.atech.2022.100138 Lu, H., Fu, X., Liu, C., Li, L. guo, He, Y. xin, & Li, N. wen. (2017). Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning. Journal of Mountain Science, 14(4), 731–741. https://doi.org/10.1007/s11629-016-3950-2 Lu, Jingshan., Eitel, J. U. H., Engels, M., Zhu, J., Ma, Y., Liao, F., Zheng, H., Wang, X., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2021). Improving Unmanned Aerial Vehicle (UAV) remote sensing of rice plant potassium accumulation by fusing spectral and textural information. International Journal of Applied Earth Observation and Geoinformation, 104. https://doi.org/10.1016/j.jag.2021.102592 M. Prabhakar , Y. G. Prasad, and M. N. R. (2012). Remote Sensing of Biotic Stress in Crop Plants and Its Applications for Pest Management. In B. Venkateswarlu, A. K. Shanker, C. Shanker, & M. Maheswari (Eds.), Crop Stress and its Management: Perspectives and Strategies (Vol. 9789400722, pp. 1–611). https://doi.org/10.1007/978-94-007-2220-0 Mahajan, G. R., Pandey, R. N., Sahoo, R. N., Gupta, V. K., Datta, S. C., & Kumar, D. (2017). Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing. Precision Agriculture, 18(5), 736–761. https://doi.org/10.1007/s11119-016-9485-2 Mahlein, A. K., Oerke, E. C., Steiner, U., & Dehne, H. W. (2012). Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology, 133(1), 197–209. https://doi.org/10.1007/s10658-011-9878-z Mahlein, A., Rumpf, T., Welke, P., Dehne, H., Plümer, L., Steiner, U., & Oerke, E. (2013). Remote Sensing of Environment 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 Mamaghani, B., & Salvaggio, C. (2019). Multispectral sensor calibration and characterization for sUAS remote sensing. Sensors (Switzerland), 19(20). https://doi.org/10.3390/s19204453 Marouani, A., & Harbeoui, Y. (2016). Eficiencia Nitrogeno En Cultivos Papa. Acta Agronómica, 65(2), 164–169. Marschener, P. (2012). Mineral Nutrition of Higher Plants (P. Marschner, Ed.; Third Edit). Martínez, L. J. (2017). Relationship between crop nutritional status , spectral measurements and Sentinel 2 images. Agronomia Colombiana, 35(2), 205–215. https://doi.org/10.15446/agron.colomb.v35n2.62857 Merton, R., & Huntington, J. (1999). Early Simulation Results of the Aries-1 Satellite Sensor for Multi-Temporal Vegetation Research Derived From Aviris. Proceedings of the Eighth Annual JPL Airborne Earth Science Workshop1–10. Minolta, K. (2009). A lightweight handheld meter for leaves without causing damage to plants. 4. Mokrani, K., Hamdi, K., & Tarchoun, N. (2018). Potato (Solanum Tuberosum L.) Response to Nitrogen, Phosphorus and Potassium Fertilization Rates. Communications in Soil Science and Plant Analysis, 49(11), 1314–1330. https://doi.org/10.1080/00103624.2018.1457159 Moran, P. A. P. (1950). Notes on Continuous Stochastic Phenomena. Biometrika, 37(1/2), 17–23. https://doi.org/10.2307/2332142 Morier, T., Cambouris, A. N., & Chokmani, K. (2015). In-season nitrogen status assessment and yield estimation using hyperspectral vegetation indices in a potato crop. Agronomy Journal, 107(4), 1295–1309. https://doi.org/10.2134/agronj14.0402 Naumann, M., Koch, M., Pawelzik, E., Gransee, A., & Thiel, H. (2019). The Importance of Nutrient Management for Potato Production Part II: Plant Nutrition and Tuber Quality. Potato Research, 63, 121–137. https://doi.org/10.1007/s11540-019-09431-2 Nex, F., & Remondino, F. (2014). UAV for 3D mapping applications: A review. Applied Geomatics, 6(1), 1–15. https://doi.org/10.1007/s12518-013-0120-x Nigon, T. J., Mulla, D. J., Rosen, C. J., Cohen, Y., Alchanatis, V., Knight, J., & Rud, R. (2015). Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars. Computers and Electronics in Agriculture, 112, 36–46. https://doi.org/10.1016/j.compag.2014.12.018 Ñústes, C. E. L., Castellanos, M. S., & Abril, M. S. (2009). Acumulación y distribución de materia seca de cuatro variedades de papa (Solanum tuberosum L.) en Zipaquirá, Cundinamarca (Colombia). Rev. Fac. Nal. Agr. Medellín, 62(113), 4823–4834. Nustez L, C. E. (2011). Variedades Colombianas de papa. In Universidad Nacional de Colombia. Padilla, F. M., Farneselli, M., Gianquinto, G., Tei, F., & Thompson, R. B. (2020). Monitoring nitrogen status of vegetable crops and soils for optimal nitrogen management. Agricultural Water Management, 241(June), 106356. https://doi.org/10.1016/j.agwat.2020.106356 Pavlidis, G., Karasali, H., & Tsihrintzis, V. A. (2020). Pesticide and Fertilizer Pollution Reduction in Two Alley Cropping Agroforestry Cultivating Systems. Water, Air, and Soil Pollution, 231(5). https://doi.org/10.1007/s11270-020-04590-2 Pimstein, A., Karnieli, A., Bansal, S. K., & Bonfil, D. J. (2011). Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy. Field Crops Research, 121(1), 125–135. https://doi.org/10.1016/j.fcr.2010.12.001 Pineux, N., Lisein, J., Swerts, G., Bielders, C. L., Lejeune, P., Colinet, G., & Degré, A. (2017). Can DEM time series produced by UAV be used to quantify diffuse erosion in an agricultural watershed? Geomorphology, 280, 122–136. https://doi.org/10.1016/j.geomorph.2016.12.003 Po, E. A., Snapp, S. S., & Kravchenko, A. (2010). Potato yield variability across the landscape. Agronomy Journal, 102(3), 885–894. https://doi.org/10.2134/agronj2009.0424 Quirós Rosado, E. (2014). Introducción a la Fotogrametría y Cartografía aplicadas a la Ingeniería Civil (Universidad de Extremadura, Ed.; First). Ray, S. S., Singh, J. P., & Panigraphy, S. (2010). Use of hyperstectralremote senings data for crop stress detection: ground-based studies. International Archives of Photogrammetry, XXXVIII(8), 562–570. Riccardi, M., Mele, G., Pulvento, C., Lavini, A., D’Andria, R., & Jacobsen, S. E. (2014). Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components. Photosynthesis Research, 120(3), 263–272. https://doi.org/10.1007/s11120-014-9970-2 Rodríguez, A., Peña-Fleitas, M. T., Padilla, F. M., Gallardo, M., & Thompson, R. B. (2021). Petiole sap nitrate concentration to assess crop nitrogen status of greenhouse sweet pepper. Scientia Horticulturae, 285(July 2020). https://doi.org/10.1016/j.scienta.2021.110157 Rodríguez, J., Lizarazo, I., Prieto, F., & Angulo-Morales, V. (2021). Assessment of potato late blight from UAV-based multispectral imagery. Computers and Electronics in Agriculture, 184(July 2020). https://doi.org/10.1016/j.compag.2021.106061 Rodríguez, L. E. (2010). Origen y evolución de la papa cultivada . Una revisión Origins and evolution of cultivated potato . A review. Agronomia Colombiana, 28(1), 9–17. Rodríguez-Pérez, L. (2010). Ecofisiología del cultivo de la papa. Revista Colombiana de Ciencias Hortícolas, 4(1), 97–108. Roosjen, P. P. J., Brede, B., Suomalainen, J. M., Bartholomeus, H. M., Kooistra, L., & Clevers, J. G. P. W. (2018). Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data – potential of unmanned aerial vehicle imagery. International Journal of Applied Earth Observation and Geoinformation, 66(July 2017), 14–26. https://doi.org/10.1016/j.jag.2017.10.012 Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring Vegetation Systems in the Great Plains with Erts. In NASA Special Publication (Vol. 351, p. 309). Severtson, D., Callow, N., Flower, K., Neuhaus, A., Olejnik, M., & Nansen, C. (2016). Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola. Precision Agriculture, 17(6), 659–677. https://doi.org/10.1007/s11119-016-9442-0 Shi, X., Zhang, X., Kang, W., Chen, Y., & Fan, M. (2019). Possibility of Recommending Potassium Application Rates Based on a Rapid Detection of the Potato Petiole K Status with a Portable K ion Meter. American Journal of Potato Research, 96(1), 48–54. https://doi.org/10.1007/s12230-018-9687-4 Sid’ko, A. F., Botvich, I. Y., Pisman, T. I., & Shevyrnogov, A. P. (2017). Estimation of chlorophyll content and yield of wheat crops from reflectance spectra obtained by ground-based remote measurements. Field Crops Research, 207, 24–29. https://doi.org/10.1016/j.fcr.2016.10.023 Som-ard, J., Hossain, M. D., Ninsawat, S., & Veerachitt, V. (2018). Pre-harvest Sugarcane Yield Estimation Using UAV-Based RGB Images and Ground Observation. Sugar Tech, 1–13. https://doi.org/10.1007/s12355-018-0601-7 Souza, C. H. W. de, Lamparelli, R. A. C., Rocha, J. V., & Magalhães, P. S. G. (2017). Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images. Computers and Electronics in Agriculture, 143(March 2016), 49–56. https://doi.org/10.1016/j.compag.2017.10.006 Stellacci, A. M., Castrignanò, A., Troccoli, A., Basso, B., & Buttafuoco, G. (2016). Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches. Environmental Monitoring and Assessment, 188(3), 1–15. https://doi.org/10.1007/s10661-016-5171-0 Sugiura, R., Tsuda, S., Tamiya, S., Itoh, A., Nishiwaki, K., Murakami, N., Shibuya, Y., Hirafuji, M., & Nuske, S. (2016). Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle. Biosystems Engineering, 148, 1–10. https://doi.org/10.1016/j.biosystemseng.2016.04.010 Tabares Patiño, Edison., Villegas Jaramillo, Sonia., González Santamaría, L. Hernán., & Cotes, J. Miguel. (2009). Respuesta de la papa (Solanum tuberosum L.) Variedad diacol capiro a la fertilización en un andisol del oriente antioqueño, Colombia. Revista Facultad Nacional de Agronomía, 62(2), 5099–5110. Taiz, L., & Zeiger, E. (2010). Plant Physiology. In Annals of Botany (Fourth edi). Sinauer Associates. https://doi.org/10.1104/pp.900074 Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71(2), 158–182. https://doi.org/10.1016/S0034-4257(99)00067-X Thompson, R. B., Tremblay, N., Fink, M., Gallardo, M., & Padilla, F. M. (2017). Tools and Strategies for Sustainable Nitrogen Fertilisation of Vegetable Crops. In F. Tei, S. Nicola, & P. Benincasa (Eds.), Advances in Research on Fertilization Management of Vegetable Crops (pp. 11–63). Springer International Publishing. https://doi.org/10.1007/978-3-319-53626-2_2 Turner, D., Lucieer, A., & Watson, C. (2012). An automated technique for generating georectified mosaics from ultra-high resolution Unmanned Aerial Vehicle (UAV) imagery, based on Structure from Motion (SFM) point clouds. Remote Sensing, 4(5), 1392–1410. https://doi.org/10.3390/rs4051392 Unidad de Planificación Rural Agropecuaria. (2016). Cultivo comercial de papa: Identificación de zonas aptas en Colombia, a escala 1:100.000. UPRA. https://doi.org/10.1017/CBO9781107415324.004 Vesali, F., Omid, M., Mobli, H., & Kaleita, A. (2017). Feasibility of using smart phones to estimate chlorophyll content in corn plants. Photosynthetica, 55(4), 603–610. https://doi.org/10.1007/s11099-016-0677-9 Virnodkar, S. S., Pachghare, V. K., Patil, V. C., & Jha, S. K. (2020). Remote sensing and machine learning for crop water stress determination in various crops: a critical review. In Precision Agriculture (Issue 0123456789). Springer US. https://doi.org/10.1007/s11119-020-09711-9 Wagner, P., & Hank, K. (2013). Suitability of aerial and satellite data for calculation of site-specific nitrogen fertilisation compared to ground based sensor data. Precision Agriculture, 14(2), 135–150. https://doi.org/10.1007/s11119-012-9278-1 Wu, J., Wang, D., Rosen, C. J., & Bauer, M. E. (2007). Comparison of petiole nitrate concentrations, SPAD chlorophyll readings, and QuickBird satellite imagery in detecting nitrogen status of potato canopies. Field Crops Research, 101(1), 96–103. https://doi.org/10.1016/j.fcr.2006.09.014 Xu, K., Gong, Y., Fang, S., Wang, K., Lin, Z., & Wang, F. (2019). Radiometric calibration of UAV remote sensing image with spectral angle constraint. Remote Sensing, 11(11). https://doi.org/10.3390/rs11111291 Yan, L., Gou, Z., & Duan, Y. (2009). A UAV Remote Sensing System: Design and Tests. In D. Li, J. Shan, & J. Gong (Eds.), Geospatial Technology for Earth Observation (pp. 27–44). Springer US. https://doi.org/10.1007/978-1-4419-0050-0_2 Yang, H., Li, F., Hu, Y., & Yu, K. (2021). Hyperspectral indices optimization algorithms for estimating canopy nitrogen concentration in potato (Solanum tuberosum L.). International Journal of Applied Earth Observation and Geoinformation, 102. https://doi.org/10.1016/j.jag.2021.102416 Yang, T., Lu, J., Liao, F., Qi, H., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2021). Retrieving potassium levels in wheat blades using normalised spectra. International Journal of Applied Earth Observation and Geoinformation, 102, 102412. https://doi.org/10.1016/j.jag.2021.102412 Yin, C., Lin, J., Ma, L., Zhang, Z., Hou, T., Zhang, L., & Lv, X. (2021). Study on the Quantitative Relationship Among Canopy Hyperspectral Reflectance, Vegetation Index and Cotton Leaf Nitrogen Content. Journal of the Indian Society of Remote Sensing, 0. https://doi.org/10.1007/s12524-021-01355-0 Zahir, S. A. D. M., Jamlos, M. F., Omar, A. F., Jamlos, M. A., Mamat, R., Muncan, J., & Tsenkova, R. (2024). Review – Plant nutritional status analysis employing the visible and near-infrared spectroscopy spectral sensor. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 304(August 2023), 123273. https://doi.org/10.1016/j.saa.2023.123273 Zaman-Allah, M., Vergara, O., Araus, J. L., Tarekegne, A., Magorokosho, C., Zarco-Tejada, P. J., Hornero, A., Albà, A. H., Das, B., Craufurd, P., Olsen, M., Prasanna, B. M., & Cairns, J. (2015). Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods, 11(1), 1–10. https://doi.org/10.1186/s13007-015-0078-2 Zhang, M., Chen, T., Gu, X., Kuai, Y., Wang, C., Chen, D., & Zhao, C. (2023). UAV-borne hyperspectral estimation of nitrogen content in tobacco leaves based on ensemble learning methods. Computers and Electronics in Agriculture, 211. https://doi.org/10.1016/j.compag.2023.108008 Zhang, W., Liu, X., Wang, Q., Zhang, H., Li, M., Song, B., & Zhao, Z. (2018). Effects of potassium fertilization on potato starch physicochemical properties. International Journal of Biological Macromolecules, 117, 467–472. https://doi.org/10.1016/j.ijbiomac.2018.05.131 Zhao, R., An, L., Song, D., Li, M., Qiao, L., Liu, N., & Sun, H. (2021). Detection of chlorophyll fluorescence parameters of potato leaves based on continuous wavelet transform and spectral analysis. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 259, 119768. https://doi.org/10.1016/j.saa.2021.119768 Zhao, Ruomei., An, Lulu., Tang, Weijie., Gao, Dehua., Qiao, Lang., Li, Minzan., Sun, Hong., & Qiao, Jinbo. (2022). Deep learning assisted continuous wavelet transform-based spectrogram for the detection of chlorophyll content in potato leaves. Computers and Electronics in Agriculture, 195(February). https://doi.org/10.1016/j.compag.2022.106802 Zhou, Z., Jabloun, M., Plauborg, F., & Andersen, M. N. (2018). Using ground-based spectral reflectance sensors and photography to estimate shoot N concentration and dry matter of potato. Computers and Electronics in Agriculture, 144, 154–163. https://doi.org/10.1016/j.compag.2017.12.005 Zhu, W., Rezaei, E. E., Nouri, H., Sun, Z., Li, J., Yu, D., & Siebert, S. (2022). UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases. Field Crops Research, 284. https://doi.org/10.1016/j.fcr.2022.108582 |
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 |
xx, 132 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.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/86752/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/86752/2/1022998428.2024.pdf https://repositorio.unal.edu.co/bitstream/unal/86752/3/1022998428.2024.pdf.jpg |
bitstream.checksum.fl_str_mv |
eb34b1cf90b7e1103fc9dfd26be24b4a 3a5f442cf0bd0cc0b1d1abb888c19e5b fb3177c38ce3e6ca322e1300843a2442 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
_version_ |
1814089285695963136 |
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 Joel94d011bd9a7f169197ab0a1837a443b9Darghan Contreras, Aquiles Enrique47b75e73e4fb74030d670c282e8637d0Cristancho Rojas, Omar Yesid841a7d8799d972b6be1de165aefc9b44Cristancho Rojas, Omar Yesid [0000000246097632]2024-08-26T14:05:24Z2024-08-26T14:05:24Z2023https://repositorio.unal.edu.co/handle/unal/86752Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, fotografías, tablasEl uso racional de fertilizantes es una medida que busca la sostenibilidad del sistema productivo de papa en Colombia, ya que con esto se puede reducir el efecto ambiental y los costos asociados a la fertilización del cultivo. El objetivo de este trabajo fue evaluar el uso de información espectral para estimar el estado nutricional de Solanum tuberosum L, variedad Bacatá bajo diferentes niveles de fertilización, para esto se realizó un ensayo en un lote comercial de papa en Soacha, Cundinamarca, Colombia. Se estableció un diseño en medidas repetidas para un arreglo en bloques generalizados y al azar usando los tiempos como factor intra-sujetos, con ocho tratamientos con variación en nitrógeno (N) y potasio (K). Se evaluó la respuesta híperespectral entre los 350 a 2500nm con un sensor FieldSpec Standar-Res ® y se tomaron fotografías con una cámara multiespectral Micasense Red-edge M ®. Las variables nutricionales se midieron con sensores de ion selectivo Laqua ® y clorofilometro SPAD. Las mediciones se llevaron a cabo entre los 65 y 107 DDS. Se encontró que hubo efecto de los tratamientos y la época en la respuesta espectral de las plantas de papa por los cambios en la concentración de pigmentos, ya que hubo efecto de los tratamientos sobre el contenido de nitratos y en los valores SPAD. Sin embargo, no hubo efecto sobre el contenido de K en peciolos. Los índices de vegetación obtenidos con el sensor híperespectral que se basaron en la reflectancia de la región entre los 445 y 850 nm fueron los que más correlación obtuvieron con el contenido de nitratos y unidades SPAD. En las imágenes multiespectrales se registró la reflectancia más alta en las regiones del Red-Edge y NIR con las dosis más altas de fertilizante nitrogenado, además se encontró que los índices PSSRa, PSSRc y DATT-4 fueron los más sensibles a los cambios generados por la época de medición y los tratamientos evaluados, lo que los convierte en parámetros con potencial en la estimación del estado nutricional para la variedad Bacatá (Texto tomado de la fuente).The rational use of fertilizers is a measure that seeks the sustainability of the potato production system in Colombia, since this can reduce the environmental effect and the costs associated with the fertilization of the crop. The objective of this work was to evaluate the use of spectral information to estimate the nutritional status of Solanum tuberosum L, variety Bacatá under different levels of fertilization, for this a trial was carried out in a commercial potato lot in Soacha, Cundinamarca, Colombia. A repeated measures design was established for a generalized and randomized block arrangement using times as an intra-subjects factor, with eight treatments with variation in nitrogen (N) and potassium (K). The hyperspectral response between 350 to 2500nm was evaluated with a FieldSpec Standard-Res ® sensor and photographs were taken with a Micasense Red-edge M ® multispectral camera. Nutritional variables were measured with Laqua ® selective ion sensors and SPAD chlorophyllometer. Measurements were carried out between 65 and 107 DAS. It was found that there was an effect of the treatments and the season on the spectral response of the potato plants due to changes in the concentration of pigments, since there was an effect of the treatments on the nitrate content and on the SPAD values. However, there was no effect on the K content in petioles. The vegetation indices obtained with the hyperspectral sensor that were based on the reflectance of the region between 445 and 850 nm were the ones that obtained the most correlation with the content of nitrates and SPAD units. In the multispectral images, the highest reflectance was recorded in the Red-Edge and NIR regions with the highest doses of nitrogen fertilizer, and it was also found that the PSSRa, PSSRc and DATT-4 indices were the most sensitive to the changes generated by the time of measurement and the treatments evaluated, which makes them parameters with potential in estimating the nutritional status for the Bacatá variety.MaestríaMagíster en GeomáticaEl estudio se llevó a cabo en un lote comercial de papa para industria en el municipio de Soacha (Cundinamarca), con coordenadas 4° 37’ 00” N y 74° 15’ 60” W, a una altura sobre el nivel del mar de 2630 m, la pendiente promedio del lote experimental era del 13%. La zona corresponde a un clima frio semihúmedo con una temperatura media anual de 13.4 grados Celsius y una precipitación anual media de 1850 mm (IDEAM, 2020). El suelo tenía buena profundidad, una textura franco-limosa, un pH de 5.6 y materia orgánica del 22,3%.Geoinformación para el uso sostenible de los recursos naturalesxx, 132 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en GeomáticaFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá630 - Agricultura y tecnologías relacionadas640 - Gestión del hogar y vida familiar::641 - Alimentos y bebidasPAPAS (TUBERCULOS)HORTALIZAS DE RAIZ-CULTIVOPAPAS (TUBERCULOS)-ABONOS Y FERTILIZANTESCONTAMINACION DE SUELOSPRODUCTOS QUIMICOS AGRICOLAS-ASPECTOS AMBIENTALESNITROGENO COMO FERTILIZANTEPOTASIO COMO FERTILIZANTEPotatoesRoot vegetables--CropsPotatoes - fertilizers and manuresSoil pollutionAgricultural chemicals - environmental aspectsNitrogen as fertilizerPotassium as fertilizerÍndices de vegetaciónRegión Red-edgeSensores de ion selectivoMedidas repetidas en el tiempoVegetation indicesRed-edge regionIon selective sensorsRepeated measures in timeEvaluación de la relación entre el estado nutricional y la respuesta espectral del cultivo de papa (Solanum tuberosum L.) para la estimación del contenido de nitrógeno y potasioEvaluation of the relationship between the nutritional status and the spectral response of the potato crop (Solanum tuberosum L.) for the estimation of nitrogen and potassium contentTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAgrosaviaAgrovocAbdel-Rahman, E. M., Mutanga, O., Odindi, J., Adam, E., Odindo, A., & Ismail, R. (2017). Estimating Swiss chard foliar macro- and micronutrient concentrations under different irrigation water sources using ground-based hyperspectral data and four partial least squares (PLS)-based (PLS1, PLS2, SPLS1 and SPLS2) regression algorithms. Computers and Electronics in Agriculture, 132, 21–33. https://doi.org/10.1016/j.compag.2016.11.008Abutaleb, K., & Fatma Sayed, A. (2021a). Modeling Potato Yield Response to Different Nitrogen Application Rates Using Hyperspectral Data and PLS Regression. Journal of Horticultural Science & Ornamental Plants, 13(3), 301–310. https://doi.org/10.5829/idosi.jhsop.2021.301.310Agisoft. (2019). Agisoft Metashape User Manual. 160.Alberto, R. T., Rivera, J. C. E., Biagtan, A. R., & Isip, M. F. (2019). Extraction of onion fields infected by anthracnose-twister disease in selected municipalities of Nueva Ecija using UAV imageries. Spatial Information Research. https://doi.org/10.1007/s41324-019-00302-zAsefpour Vakilian, K., & Massah, J. (2017). A farmer-assistant robot for nitrogen fertilizing management of greenhouse crops. Computers and Electronics in Agriculture, 139, 153–163. https://doi.org/10.1016/j.compag.2017.05.012Basso, M., & Pignaton de Freitas, E. (2019). A UAV Guidance System Using Crop Row Detection and Line Follower Algorithms. Journal of Intelligent and Robotic Systems: Theory and Applications, 605–621. https://doi.org/10.1007/s10846-019-01006-0Bautista, H. F., Ramírez, W. L., & Torres, J. (2012). Nutrient uptake of the diploid potato ( Solanum phureja ) variety Criolla Colombia , as a reference point to determine critical nutritional levels Absorción de nutrientes de la papa diploide ( Solanum phureja ) variedad Criolla Colombia , como punto de pa. Agronomia Colombiana, 30(3), 436–446.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.2015.02.012Blackburn, G. A. (1998). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing, 19(4), 657–675. https://doi.org/10.1080/014311698215919Botha, E. J., Leblon, B., Zebarth, B., & Watmough, J. (2007). Non-destructive estimation of potato leaf chlorophyll from canopy hyperspectral reflectance using the inverted PROSAIL model. 9, 360–374. https://doi.org/10.1016/j.jag.2006.11.003Brunner, E. (2001). Asymptotic and approximate analysis of repeated measures designs under heteroscedasticity. Mathematical Statistics with Applications in Biometry.Cao, Q., Miao, Y., Wang, H., Huang, S., Cheng, S., Khosla, R., & Jiang, R. (2013). Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor. Field Crops Research, 154, 133–144. https://doi.org/10.1016/j.fcr.2013.08.005Carter, G. A. (1994). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 15(3), 697–703. https://doi.org/10.1080/01431169408954109Cen, H., Wan, L., Zhu, J., Li, Y., Li, X., Zhu, Y., Weng, H., Wu, W., Yin, W., Xu, C., Bao, Y., Feng, L., Shou, J., & He, Y. (2019). Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras. Plant Methods, 15(1), 1–16. https://doi.org/10.1186/s13007-019-0418-8Chivasa, W., Mutanga, O., & Biradar, C. (2017). Application of remote sensing in estimating maize grain yield in heterogeneous african agricultural landscapes: A review. International Journal of Remote Sensing, 38(23), 6816–6845. https://doi.org/10.1080/01431161.2017.1365390Corti, M., Cavalli, D., Cabassi, G., Vigoni, A., Degano, L., & Marino Gallina, P. (2019). Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables. Precision Agriculture, 20(4), 675–696. https://doi.org/10.1007/s11119-018-9609-yCucho-Padin, G., Loayza, H., Palacios, S., Balcazar, M., Carbajal, M., & Quiroz, R. (2019). Development of low-cost remote sensing tools and methods for supporting smallholder agriculture. Applied Geomatics. https://doi.org/10.1007/s12518-019-00292-5Datt, B. (1998). Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves. Remote Sensing of Environment, 66(2), 111–121. https://doi.org/10.1016/S0034-4257(98)00046-7Delgado-Vera, C., Aguirre-Munizaga, M., Jiménez-Icaza, M., Manobanda-Herrera, N., & Rodríguez-Méndez, A. (2017). A Photogrammetry Software as a Tool for Precision Agriculture: A Case Study. In R. Valencia-García, K. Lagos-Ortiz, G. Alcaraz-Mármol, J. Del Cioppo, N. Vera-Lucio, & M. Bucaram-Leverone (Eds.), Technologies and Innovation (pp. 282–295). Springer International Publishing.Dutta Gupta, S., Ibaraki, Y., & Pattanayak, A. K. (2013). Development of a digital image analysis method for real-time estimation of chlorophyll content in micropropagated potato plants. Plant Biotechnology Reports, 7(1), 91–97. https://doi.org/10.1007/s11816-012-0240-5FAOSTAT. (2018). Food and Agriculture Organization of United Nations. http://www.fao.org/faostat/es/#data/QCFEDEPAPA. (2018). Informe de gestión 2018. 188FEDEPAPA. (2021). Federación Colombiana De Productores De Papa – Fedepapa Fondo Nacional De Fomento De La Papa – Fnfp. 555.Friedrich, S., Brunner, E., & Pauly, M. (2017). Permuting longitudinal data in spite of the dependencies. Journal of Multivariate Analysis, 153, 255–265. https://doi.org/10.1016/j.jmva.2016.10.004Friedrich, S., & Pauly, M. (2018). MATS: Inference for potentially singular and heteroscedastic MANOVA. Journal of Multivariate Analysis, 165, 166–179. https://doi.org/10.1016/j.jmva.2017.12.008Friedrich, Sarah., Konietschke, Frank., & Pauly, Markus. (2019). Resampling-based analysis of multivariate data and repeated measures designs with the R package MANOVA. RM. R Journal, 11(2), 380–400. https://doi.org/10.32614/rj-2019-051Gamon, J. A., Serrano, L., & Surfus, J. S. (1997). The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia, 112(4), 492–501. https://doi.org/10.1007/s004420050337Gandia, S., Fernández, G., García, J. C., & Moreno, J. F. (2004). RETRIEVAL OF VEGETATION BIOPHYSICAL VARIABLES FROM CHRIS/PROBA DATA IN THE SPARC CAMPAING.Gao, P., Zuo, Z., Zhang, R., Qiu, Y., He, R., Gao, R., & Gu, R. (2016). Optimum Nitrogen Fertilization for. Agronomy Journal, 108(1), 448–458. https://doi.org/10.2134/agronj2015.0324Giletto, C. M., Reussi Calvo, N. I., Sandaña, P., Echeverría, H. E., & Bélanger, G. (2020). Shoot- and tuber-based critical nitrogen dilution curves for the prediction of the N status in potato. European Journal of Agronomy, 119(June), 126114. https://doi.org/10.1016/j.eja.2020.126114Gitelson, A. A., Gritz, Y., & Merzlyak, M. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3), 271–282.Gitelson, A., Merzlyak, M., & Lichtenthaler, H. K. (1996). Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm. Journal of Plant Physiology, 148(3–4), 501–508. https://doi.org/10.1016/S0176-1617(96)80285-9Gitelson, A., & Merzlyak, M. N. (1994). Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation. Journal of Plant Physiology, 143(3), 286–292. https://doi.org/10.1016/S0176-1617(11)81633-0Gitelson, Anatoly., Kaufman, Y. J., & Merzlyak, Mark. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289–298. https://doi.org/10.1016/S0034-4257(96)00072-7Gizaw, S. A., Garland-campbell, K., & Carter, A. H. (2016). Evaluation of agronomic traits and spectral reflectance in Pacific Northwest winter wheat under rain-fed and irrigated conditions. Field Crops Research. https://doi.org/10.1016/j.fcr.2016.06.018Gómez, M. I., Magnitskiy, S., & Rodríguez, L. E. (2017). Diagnóstico de K + y NO 3- en savia para determinar el estado nutricional en papa ( Solanum tuberosum L . Diagnostics of K + and NO 3- in sap to determine nutritional status in potato ( Solanum tuberosum L . subsp . andigena ). Revista Colombiana de Ciencias Hortícolas, 11(3), 133–142.Gómez, M. I., Magnitskiy, S., & Rodríguez, L. E. (2019). Nitrogen, phosphorus and potassium accumulation and partitioning by the potato group Andigenum in Colombia. Nutrient Cycling in Agroecosystems, 113(3), 349–363. https://doi.org/10.1007/s10705-019-09986-zGu, D.-D., Wang, W.-Z., Hu, J.-D., Zhang, X.-M., Wang, J.-B., & Wang, B.-S. (2016). Nondestructive Determination of Total Chlorophyll Content in Maize Using Three-Wavelength Diffuse Reflectance. Journal of Applied Spectroscopy, 83(4), 541–547. https://doi.org/10.1007/s10812-016-0325-yGuo, J., Zhang, J., Xiong, S., Zhang, Z., Wei, Q., Zhang, W., Feng, W., & Ma, X. (2021). Hyperspectral assessment of leaf nitrogen accumulation for winter wheat using different regression modeling. Precision Agriculture, 22(5), 1634–1658. https://doi.org/10.1007/s11119-021-09804-zHaboudane, 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)00018-4He, L., Song, X., Feng, W., Guo, B.-B., Zhang, Y.-S., Wang, Y.-H., Wang, C.-Y., & Guo, T.-C. (2016). Improved remote sensing of leaf nitrogen concentration in winter wheat using multi-angular hyperspectral data. Remote Sensing of Environment, 174, 122–133. https://doi.org/10.1016/j.rse.2015.12.007Huete, A. R. (2004). R Emote S Ensing for. In M. L. Brusseau. anick F. Artiola, Ian L. Pepper (Ed.), Environmental Monitoring and Characterization (Firts, pp. 183–206). Academic Press. https://doi.org/10.1016/B978-0-12-064477-3.50013-8Hunt, E. R., Donald, J., Spinelli, C. B., Turner, R. W., Bruce, A. E., Gadler, D. J., Brungardt, J. J., & Hamm, P. B. (2018). Monitoring nitrogen status of potatoes using small unmanned aerial vehicles. Precision Agriculture, 19(2), 314–333. https://doi.org/10.1007/s11119-017-9518-5Hussain, A., Sahoo, R. N., Kumar, D., & Pradhan, S. (2017). Relationship of Hyperspectral Reflectance Indices with Leaf N and P Concentration, Dry Matter Accumulation and Grain Yield of Wheat. Journal of the Indian Society of Remote Sensing, 45(5), 773–784. https://doi.org/10.1007/s12524-016-0633-yIerna, A., & Mauromicale, G. (2018). Potato growth, yield and water productivity response to different irrigation and fertilization regimes. Agricultural Water Management, 201(January), 21–26. https://doi.org/10.1016/j.agwat.2018.01.008Ierna, A., & Mauromicale, G. (2019). Sustainable and profitable nitrogen fertilization management of potato. Agronomy, 9(10). https://doi.org/10.3390/agronomy9100582Iglhaut, J., Cabo, C., Puliti, S., Piermattei, L., O’Connor, J., & Rosette, J. (2019). Structure from Motion Photogrammetry in Forestry: a Review. Current Forestry Reports, 5(3), 155–168. https://doi.org/10.1007/s40725-019-00094-3Inostroza, L., Acuña, H., Munoz, P., Vásquez, C., Ibáñez, J., Tapia, G., Pino, M. T., & Aguilera, H. (2016). Using aerial images and canopy spectral reflectance for high-throughput phenotyping of white clover. Crop Science, 56(5), 2629–2637. https://doi.org/10.2135/cropsci2016.03.0156Jay, S., Maupas, F., Bendoula, R., & Gorretta, N. (2017). Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping. Field Crops Research, 210(June), 33–46. https://doi.org/10.1016/j.fcr.2017.05.005Jones, C. A., & Church, E. (2020). Photogrammetry is for everyone: Structure-from-motion software user experiences in archaeology. Journal of Archaeological Science: Reports, 30(March), 102261. https://doi.org/10.1016/j.jasrep.2020.102261Kang, W., Fan, M., Ma, Z., Shi, X., & Zheng, H. (2014). Luxury absorption of potassium by potato plants. American Journal of Potato Research, 91(5), 573–578. https://doi.org/10.1007/s12230-014-9386-8Kaur, R., Singh, B., Singh, M., & Thind, S. K. (2015). Hyperspectral Indices, Correlation and Regression Models for Estimating Growth Parameters of Wheat Genotypes. Journal of the Indian Society of Remote Sensing, 43(3), 551–558. https://doi.org/10.1007/s12524-014-0425-1Koch, M., Naumann, M., Pawelzik, E., Gransee, A., & Thiel, H. (2019). The Importance of Nutrient Management for Potato Production Part I: Plant Nutrition and Yield. Potato Research, 97–119. https://doi.org/10.1007/s11540-019-09431-2Konietschke, F., Bathke, A. C., Harrar, S. W., & Pauly, M. (2015). Parametric and nonparametric bootstrap methods for general MANOVA. Journal of Multivariate Analysis, 140, 291–301. https://doi.org/10.1016/j.jmva.2015.05.001Laban, N., Abdellatif, B., Ebeid, H. M., Shedeed, H. A., & Tolba, M. F. (2019). Machine Learning for Enhancement Land Cover and Crop Types Classification. In A. E. Hassanien (Ed.), Machine Learning Paradigms: Theory and Application (pp. 71–87). Springer International Publishing. https://doi.org/10.1007/978-3-030-02357-7_4Lee, J., & Sung, S. (2016). Evaluating spatial resolution for quality assurance of UAV images. Spatial Information Research, 24(2), 141–154. https://doi.org/10.1007/s41324-016-0015-0Lehnert, L. W., Meyer, H., Obermeier, W. A., Silva, B., Regeling, B., Thies, B., & Bendix, J. (2019). Hyperspectral data analysis in R: The hsdar package. Journal of Statistical Software, 89(Ii). https://doi.org/10.18637/jss.v089.i12Li, B., Xu, X., Han, J., Zhang, L., Bian, C., Jin, L., & Liu, J. (2019). The estimation of crop emergence in potatoes by UAV RGB imagery. Plant Methods, 15(1), 1–13. https://doi.org/10.1186/s13007-019-0399-7Li, Bo., Xu, Xiangming., Zhang, Li., Han, Jiwan., Bian, Chunsong., Li, Guangcun., Liu, Jiangang., & Jin, Liping. (2020). Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 161–172. https://doi.org/10.1016/j.isprsjprs.2020.02.013Liang, S. (2004). Quantitative Remote Sensing of Land Surfaces. In W. & Sons (Ed.), Wiley Series on Remote Sensing. Wiley & Sons. https://doi.org/10.1002/047172372XLicciardello, F., Lombardo, S., Rizzo, V., Pitino, I., Pandino, G., Strano, M. G., Muratore, G., Restuccia, C., & Mauromicale, G. (2018). Integrated agronomical and technological approach for the quality maintenance of ready-to-fry potato sticks during refrigerated storage. Postharvest Biology and Technology, 136(October 2017), 23–30. https://doi.org/10.1016/j.postharvbio.2017.10.001Linder, W. (2006). Digital photogrammetry. 3rd. ed. 219 pp. https://doi.org/10.4324/9780203305959Liu, B., Shen, W., Yue, Y. min, Li, R., Tong, Q., & Zhang, B. (2017). Combining spatial and spectral information to estimate chlorophyll contents of crop leaves with a field imaging spectroscopy system. Precision Agriculture, 18(4), 491–506. https://doi.org/10.1007/s11119-016-9466-5Liu, N., Townsend, P. A., Naber, M. R., Bethke, P. C., Hills, W. B., & Wang, Y. (2021). Hyperspectral imagery to monitor crop nutrient status within and across growing seasons. Remote Sensing of Environment, 255(January). https://doi.org/10.1016/j.rse.2021.112303Liu, Y., Feng, H., Yue, J., Jin, X., Fan, Y., Chen, R., Bian, M., Ma, Y., Song, X., & Yang, G. (2023). Improved potato AGB estimates based on UAV RGB and hyperspectral images. Computers and Electronics in Agriculture, 214(February). https://doi.org/10.1016/j.compag.2023.108260Lizarazo, I., Rodriguez, J. L., Cristancho, O., Olaya, F., Duarte, M., & Prieto, F. (2023). Identification of symptoms related to potato Verticillium wilt from UAV-based multispectral imagery using an ensemble of gradient boosting machines. Smart Agricultural Technology, 3(November 2022). https://doi.org/10.1016/j.atech.2022.100138Lu, H., Fu, X., Liu, C., Li, L. guo, He, Y. xin, & Li, N. wen. (2017). Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning. Journal of Mountain Science, 14(4), 731–741. https://doi.org/10.1007/s11629-016-3950-2Lu, Jingshan., Eitel, J. U. H., Engels, M., Zhu, J., Ma, Y., Liao, F., Zheng, H., Wang, X., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2021). Improving Unmanned Aerial Vehicle (UAV) remote sensing of rice plant potassium accumulation by fusing spectral and textural information. International Journal of Applied Earth Observation and Geoinformation, 104. https://doi.org/10.1016/j.jag.2021.102592M. Prabhakar , Y. G. Prasad, and M. N. R. (2012). Remote Sensing of Biotic Stress in Crop Plants and Its Applications for Pest Management. In B. Venkateswarlu, A. K. Shanker, C. Shanker, & M. Maheswari (Eds.), Crop Stress and its Management: Perspectives and Strategies (Vol. 9789400722, pp. 1–611). https://doi.org/10.1007/978-94-007-2220-0Mahajan, G. R., Pandey, R. N., Sahoo, R. N., Gupta, V. K., Datta, S. C., & Kumar, D. (2017). Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing. Precision Agriculture, 18(5), 736–761. https://doi.org/10.1007/s11119-016-9485-2Mahlein, A. K., Oerke, E. C., Steiner, U., & Dehne, H. W. (2012). Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology, 133(1), 197–209. https://doi.org/10.1007/s10658-011-9878-zMahlein, A., Rumpf, T., Welke, P., Dehne, H., Plümer, L., Steiner, U., & Oerke, E. (2013). Remote Sensing of Environment Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128, 21–30. https://doi.org/10.1016/j.rse.2012.09.019Mamaghani, B., & Salvaggio, C. (2019). Multispectral sensor calibration and characterization for sUAS remote sensing. Sensors (Switzerland), 19(20). https://doi.org/10.3390/s19204453Marouani, A., & Harbeoui, Y. (2016). Eficiencia Nitrogeno En Cultivos Papa. Acta Agronómica, 65(2), 164–169.Marschener, P. (2012). Mineral Nutrition of Higher Plants (P. Marschner, Ed.; Third Edit).Martínez, L. J. (2017). Relationship between crop nutritional status , spectral measurements and Sentinel 2 images. Agronomia Colombiana, 35(2), 205–215. https://doi.org/10.15446/agron.colomb.v35n2.62857Merton, R., & Huntington, J. (1999). Early Simulation Results of the Aries-1 Satellite Sensor for Multi-Temporal Vegetation Research Derived From Aviris. Proceedings of the Eighth Annual JPL Airborne Earth Science Workshop1–10.Minolta, K. (2009). A lightweight handheld meter for leaves without causing damage to plants. 4.Mokrani, K., Hamdi, K., & Tarchoun, N. (2018). Potato (Solanum Tuberosum L.) Response to Nitrogen, Phosphorus and Potassium Fertilization Rates. Communications in Soil Science and Plant Analysis, 49(11), 1314–1330. https://doi.org/10.1080/00103624.2018.1457159Moran, P. A. P. (1950). Notes on Continuous Stochastic Phenomena. Biometrika, 37(1/2), 17–23. https://doi.org/10.2307/2332142Morier, T., Cambouris, A. N., & Chokmani, K. (2015). In-season nitrogen status assessment and yield estimation using hyperspectral vegetation indices in a potato crop. Agronomy Journal, 107(4), 1295–1309. https://doi.org/10.2134/agronj14.0402Naumann, M., Koch, M., Pawelzik, E., Gransee, A., & Thiel, H. (2019). The Importance of Nutrient Management for Potato Production Part II: Plant Nutrition and Tuber Quality. Potato Research, 63, 121–137. https://doi.org/10.1007/s11540-019-09431-2Nex, F., & Remondino, F. (2014). UAV for 3D mapping applications: A review. Applied Geomatics, 6(1), 1–15. https://doi.org/10.1007/s12518-013-0120-xNigon, T. J., Mulla, D. J., Rosen, C. J., Cohen, Y., Alchanatis, V., Knight, J., & Rud, R. (2015). Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars. Computers and Electronics in Agriculture, 112, 36–46. https://doi.org/10.1016/j.compag.2014.12.018Ñústes, C. E. L., Castellanos, M. S., & Abril, M. S. (2009). Acumulación y distribución de materia seca de cuatro variedades de papa (Solanum tuberosum L.) en Zipaquirá, Cundinamarca (Colombia). Rev. Fac. Nal. Agr. Medellín, 62(113), 4823–4834.Nustez L, C. E. (2011). Variedades Colombianas de papa. In Universidad Nacional de Colombia.Padilla, F. M., Farneselli, M., Gianquinto, G., Tei, F., & Thompson, R. B. (2020). Monitoring nitrogen status of vegetable crops and soils for optimal nitrogen management. Agricultural Water Management, 241(June), 106356. https://doi.org/10.1016/j.agwat.2020.106356Pavlidis, G., Karasali, H., & Tsihrintzis, V. A. (2020). Pesticide and Fertilizer Pollution Reduction in Two Alley Cropping Agroforestry Cultivating Systems. Water, Air, and Soil Pollution, 231(5). https://doi.org/10.1007/s11270-020-04590-2Pimstein, A., Karnieli, A., Bansal, S. K., & Bonfil, D. J. (2011). Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy. Field Crops Research, 121(1), 125–135. https://doi.org/10.1016/j.fcr.2010.12.001Pineux, N., Lisein, J., Swerts, G., Bielders, C. L., Lejeune, P., Colinet, G., & Degré, A. (2017). Can DEM time series produced by UAV be used to quantify diffuse erosion in an agricultural watershed? Geomorphology, 280, 122–136. https://doi.org/10.1016/j.geomorph.2016.12.003Po, E. A., Snapp, S. S., & Kravchenko, A. (2010). Potato yield variability across the landscape. Agronomy Journal, 102(3), 885–894. https://doi.org/10.2134/agronj2009.0424Quirós Rosado, E. (2014). Introducción a la Fotogrametría y Cartografía aplicadas a la Ingeniería Civil (Universidad de Extremadura, Ed.; First).Ray, S. S., Singh, J. P., & Panigraphy, S. (2010). Use of hyperstectralremote senings data for crop stress detection: ground-based studies. International Archives of Photogrammetry, XXXVIII(8), 562–570.Riccardi, M., Mele, G., Pulvento, C., Lavini, A., D’Andria, R., & Jacobsen, S. E. (2014). Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components. Photosynthesis Research, 120(3), 263–272. https://doi.org/10.1007/s11120-014-9970-2Rodríguez, A., Peña-Fleitas, M. T., Padilla, F. M., Gallardo, M., & Thompson, R. B. (2021). Petiole sap nitrate concentration to assess crop nitrogen status of greenhouse sweet pepper. Scientia Horticulturae, 285(July 2020). https://doi.org/10.1016/j.scienta.2021.110157Rodríguez, J., Lizarazo, I., Prieto, F., & Angulo-Morales, V. (2021). Assessment of potato late blight from UAV-based multispectral imagery. Computers and Electronics in Agriculture, 184(July 2020). https://doi.org/10.1016/j.compag.2021.106061Rodríguez, L. E. (2010). Origen y evolución de la papa cultivada . Una revisión Origins and evolution of cultivated potato . A review. Agronomia Colombiana, 28(1), 9–17.Rodríguez-Pérez, L. (2010). Ecofisiología del cultivo de la papa. Revista Colombiana de Ciencias Hortícolas, 4(1), 97–108.Roosjen, P. P. J., Brede, B., Suomalainen, J. M., Bartholomeus, H. M., Kooistra, L., & Clevers, J. G. P. W. (2018). Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data – potential of unmanned aerial vehicle imagery. International Journal of Applied Earth Observation and Geoinformation, 66(July 2017), 14–26. https://doi.org/10.1016/j.jag.2017.10.012Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring Vegetation Systems in the Great Plains with Erts. In NASA Special Publication (Vol. 351, p. 309).Severtson, D., Callow, N., Flower, K., Neuhaus, A., Olejnik, M., & Nansen, C. (2016). Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola. Precision Agriculture, 17(6), 659–677. https://doi.org/10.1007/s11119-016-9442-0Shi, X., Zhang, X., Kang, W., Chen, Y., & Fan, M. (2019). Possibility of Recommending Potassium Application Rates Based on a Rapid Detection of the Potato Petiole K Status with a Portable K ion Meter. American Journal of Potato Research, 96(1), 48–54. https://doi.org/10.1007/s12230-018-9687-4Sid’ko, A. F., Botvich, I. Y., Pisman, T. I., & Shevyrnogov, A. P. (2017). Estimation of chlorophyll content and yield of wheat crops from reflectance spectra obtained by ground-based remote measurements. Field Crops Research, 207, 24–29. https://doi.org/10.1016/j.fcr.2016.10.023Som-ard, J., Hossain, M. D., Ninsawat, S., & Veerachitt, V. (2018). Pre-harvest Sugarcane Yield Estimation Using UAV-Based RGB Images and Ground Observation. Sugar Tech, 1–13. https://doi.org/10.1007/s12355-018-0601-7Souza, C. H. W. de, Lamparelli, R. A. C., Rocha, J. V., & Magalhães, P. S. G. (2017). Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images. Computers and Electronics in Agriculture, 143(March 2016), 49–56. https://doi.org/10.1016/j.compag.2017.10.006Stellacci, A. M., Castrignanò, A., Troccoli, A., Basso, B., & Buttafuoco, G. (2016). Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches. Environmental Monitoring and Assessment, 188(3), 1–15. https://doi.org/10.1007/s10661-016-5171-0Sugiura, R., Tsuda, S., Tamiya, S., Itoh, A., Nishiwaki, K., Murakami, N., Shibuya, Y., Hirafuji, M., & Nuske, S. (2016). Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle. Biosystems Engineering, 148, 1–10. https://doi.org/10.1016/j.biosystemseng.2016.04.010Tabares Patiño, Edison., Villegas Jaramillo, Sonia., González Santamaría, L. Hernán., & Cotes, J. Miguel. (2009). Respuesta de la papa (Solanum tuberosum L.) Variedad diacol capiro a la fertilización en un andisol del oriente antioqueño, Colombia. Revista Facultad Nacional de Agronomía, 62(2), 5099–5110.Taiz, L., & Zeiger, E. (2010). Plant Physiology. In Annals of Botany (Fourth edi). Sinauer Associates. https://doi.org/10.1104/pp.900074Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71(2), 158–182. https://doi.org/10.1016/S0034-4257(99)00067-XThompson, R. B., Tremblay, N., Fink, M., Gallardo, M., & Padilla, F. M. (2017). Tools and Strategies for Sustainable Nitrogen Fertilisation of Vegetable Crops. In F. Tei, S. Nicola, & P. Benincasa (Eds.), Advances in Research on Fertilization Management of Vegetable Crops (pp. 11–63). Springer International Publishing. https://doi.org/10.1007/978-3-319-53626-2_2Turner, D., Lucieer, A., & Watson, C. (2012). An automated technique for generating georectified mosaics from ultra-high resolution Unmanned Aerial Vehicle (UAV) imagery, based on Structure from Motion (SFM) point clouds. Remote Sensing, 4(5), 1392–1410. https://doi.org/10.3390/rs4051392Unidad de Planificación Rural Agropecuaria. (2016). Cultivo comercial de papa: Identificación de zonas aptas en Colombia, a escala 1:100.000. UPRA. https://doi.org/10.1017/CBO9781107415324.004Vesali, F., Omid, M., Mobli, H., & Kaleita, A. (2017). Feasibility of using smart phones to estimate chlorophyll content in corn plants. Photosynthetica, 55(4), 603–610. https://doi.org/10.1007/s11099-016-0677-9Virnodkar, S. S., Pachghare, V. K., Patil, V. C., & Jha, S. K. (2020). Remote sensing and machine learning for crop water stress determination in various crops: a critical review. In Precision Agriculture (Issue 0123456789). Springer US. https://doi.org/10.1007/s11119-020-09711-9Wagner, P., & Hank, K. (2013). Suitability of aerial and satellite data for calculation of site-specific nitrogen fertilisation compared to ground based sensor data. Precision Agriculture, 14(2), 135–150. https://doi.org/10.1007/s11119-012-9278-1Wu, J., Wang, D., Rosen, C. J., & Bauer, M. E. (2007). Comparison of petiole nitrate concentrations, SPAD chlorophyll readings, and QuickBird satellite imagery in detecting nitrogen status of potato canopies. Field Crops Research, 101(1), 96–103. https://doi.org/10.1016/j.fcr.2006.09.014Xu, K., Gong, Y., Fang, S., Wang, K., Lin, Z., & Wang, F. (2019). Radiometric calibration of UAV remote sensing image with spectral angle constraint. Remote Sensing, 11(11). https://doi.org/10.3390/rs11111291Yan, L., Gou, Z., & Duan, Y. (2009). A UAV Remote Sensing System: Design and Tests. In D. Li, J. Shan, & J. Gong (Eds.), Geospatial Technology for Earth Observation (pp. 27–44). Springer US. https://doi.org/10.1007/978-1-4419-0050-0_2Yang, H., Li, F., Hu, Y., & Yu, K. (2021). Hyperspectral indices optimization algorithms for estimating canopy nitrogen concentration in potato (Solanum tuberosum L.). International Journal of Applied Earth Observation and Geoinformation, 102. https://doi.org/10.1016/j.jag.2021.102416Yang, T., Lu, J., Liao, F., Qi, H., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2021). Retrieving potassium levels in wheat blades using normalised spectra. International Journal of Applied Earth Observation and Geoinformation, 102, 102412. https://doi.org/10.1016/j.jag.2021.102412Yin, C., Lin, J., Ma, L., Zhang, Z., Hou, T., Zhang, L., & Lv, X. (2021). Study on the Quantitative Relationship Among Canopy Hyperspectral Reflectance, Vegetation Index and Cotton Leaf Nitrogen Content. Journal of the Indian Society of Remote Sensing, 0. https://doi.org/10.1007/s12524-021-01355-0Zahir, S. A. D. M., Jamlos, M. F., Omar, A. F., Jamlos, M. A., Mamat, R., Muncan, J., & Tsenkova, R. (2024). Review – Plant nutritional status analysis employing the visible and near-infrared spectroscopy spectral sensor. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 304(August 2023), 123273. https://doi.org/10.1016/j.saa.2023.123273Zaman-Allah, M., Vergara, O., Araus, J. L., Tarekegne, A., Magorokosho, C., Zarco-Tejada, P. J., Hornero, A., Albà, A. H., Das, B., Craufurd, P., Olsen, M., Prasanna, B. M., & Cairns, J. (2015). Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods, 11(1), 1–10. https://doi.org/10.1186/s13007-015-0078-2Zhang, M., Chen, T., Gu, X., Kuai, Y., Wang, C., Chen, D., & Zhao, C. (2023). UAV-borne hyperspectral estimation of nitrogen content in tobacco leaves based on ensemble learning methods. Computers and Electronics in Agriculture, 211. https://doi.org/10.1016/j.compag.2023.108008Zhang, W., Liu, X., Wang, Q., Zhang, H., Li, M., Song, B., & Zhao, Z. (2018). Effects of potassium fertilization on potato starch physicochemical properties. International Journal of Biological Macromolecules, 117, 467–472. https://doi.org/10.1016/j.ijbiomac.2018.05.131Zhao, R., An, L., Song, D., Li, M., Qiao, L., Liu, N., & Sun, H. (2021). Detection of chlorophyll fluorescence parameters of potato leaves based on continuous wavelet transform and spectral analysis. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 259, 119768. https://doi.org/10.1016/j.saa.2021.119768Zhao, Ruomei., An, Lulu., Tang, Weijie., Gao, Dehua., Qiao, Lang., Li, Minzan., Sun, Hong., & Qiao, Jinbo. (2022). Deep learning assisted continuous wavelet transform-based spectrogram for the detection of chlorophyll content in potato leaves. Computers and Electronics in Agriculture, 195(February). https://doi.org/10.1016/j.compag.2022.106802Zhou, Z., Jabloun, M., Plauborg, F., & Andersen, M. N. (2018). Using ground-based spectral reflectance sensors and photography to estimate shoot N concentration and dry matter of potato. Computers and Electronics in Agriculture, 144, 154–163. https://doi.org/10.1016/j.compag.2017.12.005Zhu, W., Rezaei, E. E., Nouri, H., Sun, Z., Li, J., Yu, D., & Siebert, S. (2022). UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases. Field Crops Research, 284. https://doi.org/10.1016/j.fcr.2022.108582EstudiantesInvestigadoresPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86752/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1022998428.2024.pdf1022998428.2024.pdfTesis de Maestría en Geomáticaapplication/pdf3152952https://repositorio.unal.edu.co/bitstream/unal/86752/2/1022998428.2024.pdf3a5f442cf0bd0cc0b1d1abb888c19e5bMD52THUMBNAIL1022998428.2024.pdf.jpg1022998428.2024.pdf.jpgGenerated Thumbnailimage/jpeg5784https://repositorio.unal.edu.co/bitstream/unal/86752/3/1022998428.2024.pdf.jpgfb3177c38ce3e6ca322e1300843a2442MD53unal/86752oai:repositorio.unal.edu.co:unal/867522024-08-26 23:04:20.135Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |