Predicción de altura de planta y rendimiento de sorgo mediante datos multiespectrales y VANT
DOI:
https://doi.org/10.15517/d981kg61Palabras clave:
sensores remotos, mejoramiento, bosques aleatorios, fenotipadoResumen
Introducción. El crecimiento proyectado de la población mundial representa un desafío significativo para garantizar una producción alimentaria suficiente. El mejoramiento genético de cultivos, esencial para satisfacer esta demanda, depende de tecnologías avanzadas para acelerar los procesos de fenotipado en campo. Objetivo. Predecir la altura de planta y el rendimiento de biomasa en sorgo con el uso de fotogrametría y datos multiespectrales adquiridos mediante vuelos con pequeños vehículos aéreos no tripulados (VANT). Materiales y métodos. Se evaluaron seis genotipos de sorgo en Cañas, Guanacaste, Costa Rica, en un diseño completamente aleatorizado con ocho réplicas por genotipo. Se realizaron vuelos con un sensor multiespectral en etapas fenológicas seleccionadas para generar índices de vegetación, MDT (modelos digitales de terreno) y MDS (modelos digitales de superficie). Las mediciones manuales de la altura de la planta se utilizaron para análisis de correlación y regresión lineal simple, mientras que la biomasa se predijo mediante regresión con random forest. Resultados. Los MDT y los MDS proporcionaron una estimación confiable de la altura de la planta durante la etapa de crecimiento inicial (R² = 0,53) y alcanzaron una mayor precisión en etapas posteriores (R² = 0,76; RMSE = 0,13 m). La predicción de biomasa fue más precisa durante el estado de bota (r = 0,72; RMSE = 1,40 t·ha-¹), y se identificaron el NDRE (Índice de Diferencia Normalizada del Borde Rojo) e IKAW (Índice de Kawashima) como los índices espectrales más relevantes. Conclusiones. Los MDT y MDS derivados de imágenes multiespectrales predijeron con precisión la altura de planta en etapas posteriores de crecimiento, pero fueron menos precisos en etapas tempranas. La incorporación de la altura de la planta junto con los índices espectrales en los modelos mejoró la predicción de la biomasa. Los hallazgos demostraron que los sensores montados en VANT y los índices multiespectrales son herramientas potenciales para el fenotipado en programas de mejoramiento de sorgo en Costa Rica.
Descargas
Referencias
Alemán Montes, B., Henríquez Henríquez, C., Ramírez Rodríguez, T., & Largaespada Zelaya, K. (2021). Estimación de rendimiento en el cultivo de caña de azúcar (Saccharum officinarum) a partir de fotogrametría con vehículos aéreos no tripulados (VANT). Agronomía Costarricense, 45(1), 67-80. https://doi.org/10.15517/rac.v45i1.45695
Araus, J. L., & Kefauver, S. C. (2018). Breeding to adapt agriculture to climate change: affordable phenotyping solutions. Current Opinion in Plant Biology, 45, 237-247. https://doi.org/10.1016/j.pbi.2018.05.003
Aswini M. S., Kiran, Lenka , B., Shaniware , Y. A., Pandey , P., Dash , A. P., & Haokip , S. W. (2023). The role of genetics and plant breeding for crop improvement: Current progress and future prospects. International Journal of Plant & Soil Science, 35(20), 190-202. https://doi.org/10.9734/ijpss/2023/v35i203798
Ballester, C., Brinkhoff, J., Quayle, W. C., & Hornbuckle, J. (2019). Monitoring the effects of water stress in cotton using the green red vegetation index and red edge ratio. Remote Sensing, 11(7), Article 873. https://doi.org/10.3390/RS11070873
Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., & Bareth, G. (2014). Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, 6(11), 10395-10412. https://doi.org/10.3390/rs61110395
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
Cheng, T., Song, R., Li, D., Zhou, K., Zheng, H., Yao, X., Tian, Y., Cao, W., & Zhu, Y. (2017). Spectroscopic estimation of biomass in canopy components of paddy rice using dry matter and chlorophyll indices. Remote Sensing, 9(4), Article 319. https://doi.org/10.3390/rs9040319
Chivasa, W., Mutanga, O., & Biradar, C. (2020). UAV-Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions. Remote Sensing, 12(15), Article 15. https://doi.org/10.3390/rs12152445
De Swaef, T., Maes, W. H., Aper, J., Baert, J., Cougnon, M., Reheul, D., Steppe, K., Roldán-Ruiz, I., & Lootens, P. (2021). Applying rgb- and thermal-based vegetation indices from UAVs for high-throughput field phenotyping of drought tolerance in forage grasses. Remote Sensing, 13(1), Article 147. https://doi.org/10.3390/rs13010147
Fitzgerald, G. J., Rodriguez, D., Christensen, L. K., Belford, R., Sadras, V. O., & Clarke, T. R. (2006). Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments. Precision Agriculture, 7(4), 233-248. https://doi.org/10.1007/s11119-006-9011-z
Gano, B., Dembele, J. S. B., Ndour, A., Luquet, D., Beurier, G., Diouf, D., & Audebert, A. (2021). Using uav borne, multi-spectral imaging for the field phenotyping of shoot biomass, leaf area index and height of west african sorghum varieties under two contrasted water conditions. Agronomy, 11(5), Article 850. https://doi.org/10.3390/agronomy11050850
Gerik, T., Bean, B., & Vanderlip, R. (2003). Sorghum growth and development. The Texas A&M University System. http://glasscock.agrilife.org/files/2015/05/Sorghum-Growth-and-Development.pdf
Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M .N. (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
Griffiths, S., Simmonds, J., Leverington, M., Wang, Y., Fish, L., Sayers, L., Alibert, L., Orford, S., Wingen, L., & Snape, J. (2012). Meta-QTL analysis of the genetic control of crop height in elite European winter wheat germplasm. Molecular Breeding, 29, 159-171. https://doi.org/10.1007/s11032-010-9534-x
Hagen, I., Huggel, C., Ramajo, L., Chacón, N., Ometto, J. P., Postigo, J. C., & Castellanos, E. J. (2022). Climate change-related risks and adaptation potential in Central and South America during the 21st century. Environmental Research Letters, 17(3), Article 033002. https://doi.org/10.1088/1748-9326/ac5271
Hall, A. J., & Richards, R. A. (2013). Prognosis for genetic improvement of yield potential and water-limited yield of major grain crops. Field Crops Research, 143, 18-33. https://doi.org/10.1016/j.fcr.2012.05.014
Hassan, M. A., Yang, M., Fu, L., Rasheed, A., Zheng, B., Xia, X., Xiao, Y., & He, Z. (2019). Accuracy assessment of plant height using an unmanned aerial vehicle for quantitative genomic analysis in bread wheat. Plant Methods, 15, Article 37. https://doi.org/10.1186/s13007-019-0419-7
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309. https://doi.org/10.1016/0034-4257(88)90106-X
Instituto Meteorológico Nacional. (2023). El clima y las regiones climáticas de Costa Rica. https://www.imn.ac.cr/documents/10179/31165/clima-regiones-climat.pdf/cb3b55c3-f358-495a-b66c-90e677e35f57
Jiang, Q., Fang, S., Peng, Y., Gong, Y., Zhu, R., Wu, X., Ma, Y., Duan, B., & Liu, J. (2019). UAV-based biomass estimation for rice-combining spectral, TIN-based structural and meteorological features. Remote Sensing, 11(7), Article 890. https://doi.org/10.3390/RS11070890
Jiang, Z., Tu, H., Bai, B., Yang, C., Zhao, B., Guo, Z., Liu, Q., Zhao, H., Yang, W., Xiong, L., & Zhang, J. (2021). Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress. New Phytologist, 232(1), 440-455. https://doi.org/10.1111/nph.17580
Jordan, C. F. (1969). Derivation of leaf-area index from quality of light on the forest floor. Ecology, 50(4), 663–666. https://doi.org/10.2307/1936256
Kanke, Y., Tubaña, B., Dalen, M., & Harrell, D. (2016). Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields. Precision Agriculture, 17(5), 507-530. https://doi.org/10.1007/s11119-016-9433-1
Kawashima, S., & Nakatani, M. (1998). An algorithm for estimating chlorophyll content in leaves using a video camera. Annals of Botany, 81(1), 49-54. https://doi.org/10.1006/anbo.1997.0544
Li, J., Shi, Y., Veeranampalayam-Sivakumar, A. N., & Schachtman, D. P. (2018). Elucidating sorghum biomass, nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system. Frontiers in Plant Science, 9, Article 1406. https://doi.org/10.3389/fpls.2018.01406
Louhaichi, M., Borman, M. M., & Johnson, D. E. (2001). Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16(1), 65-70. https://doi.org/10.1080/10106040108542184
Malambo, L., Popescu, S. C., Murray, S. C., Putman, E., Pugh, N. A., Horne, D. W., Richardson, G., Sheridan, R., Rooney, W. L., Avant, R., Vidrine, M., McCutchen, B., Baltensperger, D., & Bishop, M. (2018). Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery. International Journal of Applied Earth Observation and Geoinformation, 64, 31-42. https://doi.org/10.1016/j.jag.2017.08.014
Mandal, S., Bhattacharya, S., & Paul, S. (2022). Assessing the impact of coal-fired thermal power plant emissions on surrounding vegetation health using geoinformatics: a case study. Safety in Extreme Environments, 4, 81-100. https://doi.org/10.1007/s42797-022-00054-4
Marques Ramos, A. P., Prado Osco, L., Garcia Furuya, D. E., Nunes Gonçalves, W., Cordeiro Santana, D., Pereira Ribeiro, L., Da Silva Junior, C. A., Capristo-Silva, G. F., Li, J., Rojo Baio, F. H., Marcato Junior, J., Teodoro, P. E., & Pistori, H. (2020). A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices. Computers and Electronics in Agriculture, 178, Article 105791. https://doi.org/10.1016/j.compag.2020.105791
Matias, F. I., Caraza-Harter, M. V & Endelman, J. B. (2020). FIELDimageR: An R package to analyze orthomosaic images from agricultural field trials. The Plant Phenome Journal, 3, Article e20005. https://doi.org/10.1002/ppj2.20005
Mesas-Carrascosa, F. J., Torres-Sánchez, J., Clavero-Rumbao, I., García-Ferrer, A., Peña, J.-M., Borra-Serrano, I., & López-Granados, F. (2015). Assessing optimal flight parameters for generating accurate multispectral orthomosaicks by uav to support site-specific crop management. Remote Sensing, 7(10), 12793-12814. https://doi.org/10.3390/rs71012793
Navure Team. (n.d.). Navure (2.7.1): A data-science-statistic oriented application for making evidence-based decisions. Retrieved December 4, from https://www.navure.com/download/
Orozco Barrantes, E., & Sánchez Ledezma, W. (2018). Evaluación de variedades e híbridos de sorgo forrajero en condiciones de Bosque Húmedo Tropical. Alcances Tecnológicos, 8(1), 45-54. https://doi.org/10.35486/at.v8i1.78
Peroni Venancio, L., Chartuni Mantovani, E., Do Amaral, C. H., Usher Neale, C. M., Zution Gonçalves, I., Filgueiras, R., & Coelho Eugenio, F. (2020). Potential of using spectral vegetation indices for corn green biomass estimation based on their relationship with the photosynthetic vegetation sub-pixel fraction. Agricultural Water Management, 236, Article 106155. https://doi.org/10.1016/j.agwat.2020.106155
Pix4D. (2017). Pix4Dmapper4.1 User manual. Retrieved August 20, 2023, from https://support.pix4d.com/hc/en-us/articles/204272989
Ranđelović, P., Đorđević, V., Miladinović, J., Prodanović, S., Ćeran, M., & Vollmann, J. (2023). High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data. Plant Methods, 19, Article 89. https://doi.org/10.1186/s13007-023-01054-6
R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org
Rodriguez, O. (2023). regressoR: Regression Data Analysis System. R package version 3.0.2. https://CRAN.R-project.org/package=regressoR
Roujean, J. L., & Breon, F. M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3), 375-384. https://doi.org/10.1016/0034-4257(94)00114-3
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D.W. (1974, December 10-14). Monitoring vegetation systems in the Great Plains with ERTS. In National Aeronautics and Space Administration (Ed.), Proceedings of the Third Earth Resources Technology Satellite (ERTS) Symposium (NASA SP-351, Vol 1, pp. 309-317). National Aeronautics and Space Administration.
Shi, Y., Thomasson, J. A., Murray, S. C., Pugh, N. A., Rooney, W. L., Shafian, S., Rajan, N., Rouze, G., Morgan, C. L. S., Neely, H. L., Rana, A., Bagavathiannan, M. V., Henrickson, J., Bowden, E., Valasek, J., Olsenholler, J., Bishop, M. P., Sheridan, R., Putman, E. B., … Yang, C. (2016). Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLOS One, 11(7), Article e0159781. https://doi.org/10.1371/journal.pone.0159781
Shu, M., Li, Q., Ghafoor, A., Zhu, J., Li, B., & Ma, Y. (2023). Using the plant height and canopy coverage to estimation maize aboveground biomass with UAV digital images. European Journal of Agronomy, 151, Article 126957. https://doi.org/10.1016/j.eja.2023.126957
Stavrakoudis, D., Katsantonis, D., Kadoglidou, K., Kalaitzidis, A., & Gitas, I. Z. (2019). Estimating rice agronomic traits using drone-collected multispectral imagery. Remote Sensing, 11(5), Article 545. https://doi.org/10.3390/rs11050545
Tayade, R., Yoon, J., Lay, L., Khan, A. L., Yoon, Y., & Kim, Y. (2022). Utilization of spectral indices for high-throughput phenotyping. Plants, 11(13), Article 1712. https://doi.org/10.3390/plants11131712
United Nations. (2017, June 21). World population projected to reach 9.8 billion in 2050, and 11.2 billion in 2100 – says UN. https://www.un.org/en/desa/world-population-projected-reach-98-billion-2050-and-112-billion-2100
Varela, S., Pederson, T., Bernacchi, C. J., & Leakey, A. D. B. (2021). Understanding growth dynamics and yield prediction of sorghum using high temporal resolution UAV imagery time series and machine learning. Remote Sensing, 13(9), Article 1763. https://doi.org/10.3390/rs13091763
Wan, L., Cen, H., Zhu, J., Zhang, J., Zhu, Y., Sun, D., Du, X., Zhai, L., Weng, H., Li, Y., Li, X., Bao, Y., Shou, J., & He, Y. (2020). Grain yield prediction of rice using multi-temporal UAV-based RGB and multispectral images and model transfer – a case study of small farmlands in the South of China. Agricultural and Forest Meteorology, 291, Article 108096. https://doi.org/10.1016/j.agrformet.2020.108096
Watanabe, K., Guo, W., Arai, K., Takanashi, H., Kajiya-Kanegae, H., Kobayashi, M., Yano, K., Tokunaga, T., Fujiwara, T., Tsutsumi, N., & Iwata, H. (2017). High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Frontiers in Plant Science, 8, Article 421. https://doi.org/10.3389/fpls.2017.00421
Watt, M., Fiorani, F., Usadel, B., Rascher, U., Muller, O., & Schurr, U. (2020). Phenotyping: new windows into the plant for breeders. Annual Review of Plant Biology, 71, 689-712. https://doi.org/10.1146/annurev-arplant-042916-041124
Xie, Q., Dash, J., Huang, W., Peng, D., Qin, Q., Mortimer, H., Casa, R., Pignatti, S., Laneve, G., Pascucci, S., Dong, Y., & Ye, H. (2018). Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5), 1482-1493. https://doi.org/10.1109/JSTARS.2018.2813281
Yang, G., Liu, J., Zhao, C., Li, Z., Huang, Y., Yu, H., Xu, B., Yang, X., Zhu, D., Zhang, X., Zhang, R., Feng, H., Zhao, X., Li, Z., Li, H., & Yang, H. (2017). Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. Frontiers in Plant Science, 8, Article 1111. https://doi.org/10.3389/fpls.2017.01111
Yue, J., Yang, G., Li, C., Li, Z., Wang, Y., Feng, H., & Xu, B. (2017). Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sensing, 9(7), Article 708. https://doi.org/10.3390/rs9070708
Zheng, H., Cheng, T., Zhou, M., Li, D., Yao, X., Tian, Y., Cao, W., & Zhu, Y. (2019). Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery. Precision Agriculture, 20, 611-629. https://doi.org/10.1007/s11119-018-9600-7
Descargas
Publicado
Declaración de disponibilidad de datos
Data are available from the corresponding author upon reasonable request by email
Licencia
Derechos de autor 2026 Kevin Carrillo Montoya, Jorge Claudio Vargas Rojas, Melvin Lizano Araya, Nevio Bonilla Morales, Josselyne Aguilar López, José Roberto Camacho Montero (Autor/a)
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
1. Política propuesta para revistas de acceso abierto
Los autores/as que publiquen en esta revista aceptan las siguientes condiciones:
- Los autores/as conservan los derechos morales de autor y ceden a la revista el derecho de la primera publicación, con el trabajo registrado con la licencia de atribución, no comercial y sin obra derivada de Creative Commons, que permite a terceros utilizar lo publicado siempre que mencionen la autoría del trabajo y a la primera publicación en esta revista, no se puede hacer uso de la obra con propósitos comerciales y no se puede utilizar las publicaciones para remezclar, transformar o crear otra obra.
- Los autores/as pueden realizar otros acuerdos contractuales independientes y adicionales para la distribución no exclusiva de la versión del artículo publicado en esta revista (p. ej., incluirlo en un repositorio institucional o publicarlo en un libro) siempre que indiquen claramente que el trabajo se publicó por primera vez en esta revista.
- Se permite y recomienda a los autores/as a publicar su trabajo en Internet (por ejemplo en páginas institucionales o personales) antes y durante el proceso de revisión y publicación, ya que puede conducir a intercambios productivos y a una mayor y más rápida difusión del trabajo publicado (vea The Effect of Open Access).
Cómo citar
