Fotogrametría con vehículos aéreos no tripulados para la caracterización estructural del cultivo de maíz (Zea mays L.) en el trópico húmedo

Autores/as

  • Sergio Salgado-Velázquez Colegio de Postgraduados
  • Fabiola Olvera-Rincón Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias
  • Sabel Barrón-Freyre Colegio de Postgraduados
  • Dante Sumano-López Colegio de Postgraduados
  • Pablo Ulises Hernández-Lara Colegio de Postgraduados
  • David Julián Palma-Cancino Colegio de Postgraduados
  • Samuel Córdova-Sánchez Universidad Popular de la Chontalpa

Palabras clave:

Fotogrametría, agricultura de precisión, maíz, altura de planta, modelos digitales

Resumen

El uso de vehículos aéreos no tripulados se ha consolidado como una herramienta clave para el monitoreo agrícola de alta precisión. En este estudio, se aplicaron técnicas de fotogrametría digital mediante dron para estimar la altura de plantas de maíz (Zea mays L.) en condiciones tropicales de Tabasco, México. Las misiones se realizaron a 100 m de altura, con velocidad de 3.5 m s-1 e intervalo de disparo de 2 s, generando un traslape frontal del 95 %. Las imágenes se procesaron con algoritmos Structure from Motion y correlación multivista para generar la nube densa, el modelo digital de superficie, el modelo digital del terreno y el ortomosaico georreferenciado. La altura se obtuvo por diferencia altimétrica, con valores medios de 2.68 m, consistentes con el rango fisiológico del cultivo en el trópico húmedo. Las variaciones espaciales reflejaron heterogeneidad edáfica y manejo agronómico. Valores mayores observados (> 3.5 m) se atribuyeron a artefactos. La validación mostró un R2 de 0.96 y un RMSE de 0.205 m, confirmando su precisión.

Referencias

Agisoft LLC. (2023). Agisoft Metashape User Manual (Version 2.0). Agisoft LLC.

Agüera-Vega, F., Carvajal-Ramírez, F., & Martínez-Carricondo, P. (2017). Accuracy of digital surface models and orthophotos derived from unmanned aerial vehicle photogrammetry. Journal of Surveying Engineering, 143(2), 04016025. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000206

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.

American Society for Photogrammetry and Remote Sensing. (2024). asprs Positional Accuracy Standards for Digital Geospatial Data (Edition 2, Version 2.0). ASPRS. https://publicdocuments.asprs.org/PositionalAccuracyStd-Ed2-V2

Ballesteros, R., Ortega, J. F., Hernández, D., & Moreno, M. A. (2014). Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing. Precision Agriculture, 15, 579-592. https://doi.org/10.1007/s11119-014-9355-8

Barrón-Freyre, S., Barrón, A. M., & Palafox, C. A.. (2014). Potencial productivo de nueve híbridos de maíz para forraje en condiciones de temporal en la Chontalpa Tabasco. En xxvi Reunión Científica-Tecnológica Forestal y Agropecuaria Tabasco, iii Simposio Internacional en Producción Agroalimentaria Tropical. Villahermosa, México.

Belton, D., Helmholz, P., Long, J., & Zerihun, A. (2019). Crop height monitoring using a consumer-grade camera and UAV technology. pfg-Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 87, 249-262. https://doi.org/10.1007/s41064-019-00087-8

Bongomin, O., Lamo, J., Guina, J. M., Okello, C., Ocen, G. G., Obura, M., Alibu, S., Owino, C. A., Akwero, A., & Ojok, S. (2024). UAV image acquisition and processing for high‐throughput phenotyping in agricultural research and breeding programs. The Plant Phenome Journal, 7(1), e20096. https://doi.org/10.1002/ppj2.20096

Brandt, L. E., & Freeman, W. T. (2021). Toward Automatic Interpretation of 3D Plots. En J. Lladós, D. Lopresti & S. Uchida (Eds.), Document Analysis and Recognition – icdar 2021. Lecture Notes in Computer Science. Springer. https://doi.org/10.1007/978-3-030-86331-9_3

Cano, E., Horton, R., Liljegren, C., & Bulanon, D. M. (2017). Comparison of small unmanned aerial vehicles performance using image processing. Journal of Imaging, 3(1), 4. https://doi.org/10.3390/jimaging3010004

Córdova-Sánchez, S., Góngora-Cruz, K. G., Hernández-Villegas, M. M., Salgado-Velázquez, S., López-Castañeda, A., & Castañeda-Ceja, R. (2023). Response of different sowing densities on agronomic parameters in the cultivation of mejen corn in Tabasco, Mexico. Agro Productividad, 16(10), 95-102. https://doi.org/10.32854/agrop.v16i9.2623

Corti, M., Cavalli, D., Cabassi, G., Bechini, L., Pricca, N., Paolo, D., Mariononi, L., Vigoni, A., Degano, L., & Marino Gallina, P. (2023). Improved estimation of herbaceous crop aboveground biomass using UAV-derived crop height combined with vegetation indices. Precision Agriculture, 24(2), 587-606. https://doi.org/10.1007/s11119-022-09960-w

Dandois, J. P., Olano, M., & Ellis, E. C. (2015). Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote Sensing, 7(10), 13895-13920. https://doi.org/10.3390/rs71013895

DJI. (2023). dji Mini 4 Pro. Especificaciones. https://www.dji.com/mx/mini-4-pro/specs

Dong, H., Dong, J., Sun, S., Bai, T., Zhao, D., Yin, Y., Shen, X., Wang, Y., Zhang, Z., & Wang, Y. (2024). Crop water stress detection based on UAV remote sensing systems. Agricultural Water Management, 303, 109059. https://doi.org/10.1016/j.agwat.2024.109059

Eskandari, R., Mahdianpari, M., Mohammadimanesh, F., Salehi, B., Brisco, B., & Homayouni, S. (2020). Metaanalysis of unmanned aerial vehicle (UAV) imagery for agro-environmental monitoring using machine learning and statistical models. Remote Sensing, 12(21), 3511. https://doi.org/10.3390/rs12213511

Ferraz, M. A. J., Barboza, T. O. C., Arantes, P. de S., Von Pinho, R. G., & Dos Santos, A. F. (2024). Integrating satellite and UAV technologies for maize plant height estimation using advanced machine learning. AgriEngineering, 6(1), 20-33. https://doi.org/10.3390/agriengineering6010002

Food and Agriculture Organization of the United Nations. (2023). faostat Statistical Database. FAO. https://www.fao.org/faostat/

Fujiwara, R., Kikawada, T., Sato, H., & Akiyama, Y. (2022). Comparison of remote sensing methods for plant heights in agricultural fields using unmanned aerial vehicle-based structure from motion. Frontiers in Plant Science, 13, 886804. https://doi.org/10.3389/fpls.2022.886804

Gao, M., Yang, F., Wei, H., & Liu, X. (2022). Individual maize location and height estimation in field from UAV-Borne LiDAR and RGB images. Remote Sensing, 14(10), 2292. https://doi.org/10.3390/rs14102292

Kim, T., Park, J., Lee, C., Yun, Y., Jung, J., & Han, Y. (2022). Multi-temporal orthophoto and digital surface model registration produced from UAV imagery over an agricultural field. Geocarto International, 37(27), 18767-18790. https://doi.org/10.1080/10106049.2022.2143913

Lambertini, A., Mandanici, E., Tini, M. A., & Vittuari, L. (2022). Technical challenges for multi-temporal and multi-sensor image processing surveyed by UAV for mapping and monitoring in precision agriculture. Remote Sensing, 14(19), 4954. https://doi.org/10.3390/rs14194954

Li, Y., Li, C., Cheng, Q., Duan, F., Zhai, W., Li, Z., Mao, B., Ding, F., Kuang, X., & Chen, Z. (2024). Estimating maize crop height and aboveground biomass using multisource unmanned aerial vehicle remote sensing and optuna-optimized ensemble learning algorithms. Remote Sensing, 16(17), 3176. https://doi.org/10.3390/rs16173176

Malachy, N., Zadak, I., & Rozenstein, O. (2022). Comparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion. Remote Sensing, 14(4), 810. https://doi.org/10.3390/rs14040810

Mesas-Carrascosa, F.-J., Notario García, M. D., Meroño de Larriva, J. E., & García-Ferrer, A. (2016). An analysis of the influence of flight parameters in the generation of unmanned aerial vehicle (UAV) orthomosaicks to survey archaeological areas. Sensors, 16(11), 1838. https://doi.org/10.3390/s16111838

Oehme, L. H., Reineke, A.-J., Weiß, T. M., Würschum, T., He, X., & Müller, J. (2022). Remote sensing of maize plant height at different growth stages using UAV-based digital surface models (DSM). Agronomy, 12(4), 958. https://doi.org/10.3390/agronomy12040958

Olson, D., & Anderson, J. (2021). Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture. Agronomy Journal, 113(2), 971-992. https://doi.org/10.1002/agj2.20595

Olvera-Rincón, F., Salgado-Velázquez, S., Córdova-Sánchez, S., Palma-López, D. J., López-Castañeda, A., & Castañeda-Ceja, R. (2024). Defoliación del cultivo de caña de azúcar (Saccharum officinarum) en la Chontalpa, Tabasco, México. Agronomía Mesoamericana, 35, 53608. http://doi.org/10.15517/am.2024.53608

Palma-López, D. J., Salgado-García, S., Martinez Sebastian, G., Zavala-Cruz, J., & Lagunes-Espinoza, L. Del C. (2015). Cambios en las propiedades del suelo en plantaciones de eucalipto de Tabasco, México. Ecosistemas y Recursos Agropecuarios, 2(5), 163-172. https://doi.org/10.19136/era.a2n5.767

Palma-López, D. J., Jiménez Ramírez, R., Zavala-Cruz, J., Bautista-Zúñiga, F., Gavi Reyes, F., & Palma-Cancino, D. Y. (2017). Actualización de la clasificación de suelos de Tabasco, México. Agro Productividad, 10(12), 29-35.

Qiao, L., Tang, W., Gao, D., Zhao, R., An, L., Li, M., Sun, H., & Song, D. (2022). UAV-based chlorophyll content estimation by evaluating vegetation index responses under different crop coverages. Computers and Electronics in Agriculture, 196, 106775. https://doi.org/10.1016/j.compag.2022.106775

Ruwanpathirana, P. P., Sakai, K., Jayasinghe, G. Y., Nakandakari, T., Yuge, K., Wijekoon, W. M. C. J., Priyankara, A. C. P., Samaraweera, M. D. S., & Madushanka, P. L. A. (2024). Evaluation of sugarcane crop growth monitoring using vegetation indices derived from RGB-based UAV images and machine learning models. Agronomy, 14(9), 2059. https://doi.org/10.3390/agronomy14092059

Salgado-Velázquez, S., Salgado-García, S., Rincón-Ramírez, J. A., Rodrigues Jr, F. A., Palma-López, D. J., Córdova-Sánchez, S., & López-Castañeda, A. (2020). Spatial variability of soil physicochemical properties in agricultural fields cultivated with sugarcane (Saccharum officinarum L.) in Southeastern Mexico. Sugar Tech, 22, 65-75. https://doi.org/10.1007/s12355-019-00742-9

Salgado-Velázquez, S., Becerril-Hernández, H., Rincón-Ramírez, J. A., Aceves-Navarro, L., & Córdova-Sánchez, S. 2025. Remote sensing and machine learning techniques used to predict sugarcane (Saccharum spp.) yield. Agro Productividad, 18(9), 229-237. https://doi.org/10.32854/dp4qtd40

Servicio de Información Agroalimentaria y Pesquera. (2024). Anuario estadístico de la producción agrícola. SIAP. https://nube.agricultura.gob.mx/cierre_agricola/

Sumano-López, D., Barrón-Freyre, S., Ramírez-Guillermo, M. A., Palma-Cancino, D. J., Salgado-Velázquez, S., & Rodríguez-Cuevas, M. (2025). Performance of corn (Zea mays L.) with application of poultry manure and leachates as organic fertilizer. Agro Productividad, 18(2), 135-148. https://doi.org/10.32854/agrop.v18i2.3251

Torres-Sánchez, J., López-Granados, F., Serrano, N., Arquero, O., & Peña, J. M. (2015). High-throughput 3-D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology. PloS ONE, 10(6), e0130479. https://doi.org/10.1371/journal.pone.0130479

Tsouros, D. C., Triantafyllou, A., Bibi, S., & Sarigannidis, P. G. (2019). Data acquisition and analysis methods in UAV-based applications for precision agriculture. En 2019 15th International Conference on Distributed Computing in Sensor Systems. Santorini, Grecia. https://doi.org/10.1109/DCOSS.2019.00080

Velusamy, P., Rajendran, S., Mahendran, R. K., Naseer, S., Shafiq, M., & Choi, J.-G. (2022). Unmanned aerial vehicles (UAV) in precision agriculture: Applications and challenges. Energies, 15(1), 217. https://doi.org/10.3390/en15010217

Xie, T., Li, J., Yang, C., Jiang, Z., Chen, Y., Guo, L., & Zhang, J. (2021). Crop height estimation based on UAV images: Methods, errors, and strategies. Computers and Electronics in Agriculture, 185, 106155. https://doi.org/10.1016/j.compag.2021.106155

Yang, Z., Cao, Y., Shi, Y., Qin, F., Jiang, C., & Yang, S. (2023). Genetic and molecular exploration of maize environmental stress resilience: Toward sustainable agriculture. Molecular Plant, 16(10), 1496-1517. https://doi.org/10.1016/j.molp.2023.07.005

Yu, X., Yin, D., Nie, C., Ming, B., Xu, H., Liu, Y., Bai, Y., Shao, M., Cheng, M., Liu, Y., Liu, S., Wang, Z., Wang, S., Shi, L., & Jin, X. (2022). Maize tassel area dynamic monitoring based on near-ground and UAV RGB images by U-Net model. Computers and Electronics in Agriculture, 203, 107477. https://doi.org/10.1016/j.compag.2022.107477

Zhang, Y., Xia, C., Zhang, X., Cheng, X., Feng, G., Wang, Y., & Gao, Q. (2021). Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images. Ecological Indicators, 129, 107985. https://doi.org/10.1016/j.ecolind.2021.107985

Ziliani, M. G., Parkes, S. D., Hoteit, I., & McCabe, M. F. (2018). Intra-season crop height variability at commercial farm scales using a fixed-wing UAV. Remote Sensing, 10(12), 2007. https://doi.org/10.3390/rs10122007

Wolf, P. R., Dewitt, B. A., & Wilkinson, B. E. (2014). Elements of Photogrammetry with Applications in gis. McGraw-Hill.

Descargas

Publicado

2026-05-05