Evolution and recovery of pastures to optimize livestock nutritional yields by interpreting normalized vegetation indices using multispectral surveys

Authors

DOI:

https://doi.org/10.24054/rcta.v2i42.2701

Keywords:

NDVI, multispectral images, UAV, soil characterization

Abstract

Precision agriculture has experienced significant advances by taking advantage of technologies such as the use of drones and the capture of spectral images. The application of NDVI (Normalized Difference Vegetation Index) has become a key tool for the identification of vegetation cover, allowing for the accurate analysis of crop health and the estimation of the area occupied according to biomass density.  The combination of these technologies facilitates the generation of daily vegetation growth rates, which is essential for projecting pasture recovery. In this work, NDVI was used to assess the health and anticipate the need for adjustments in pasture management and kikuyo grass requirements. Projections based on these data provide a valuable tool for decision making, ensuring that pasture recovery strategies are appropriate and effective as yields are optimized for livestock nutrition. This article focuses on monitoring the evolution of kikuyo pasture, specifically in a farm in the municipality of Pamplona, Norte de Santander, the time window for the development of the monitoring was determined in four months, in which a multispectral survey was carried out per month, and with the information obtained, daily growth rates and the projection of the days for the reestablishment of the grass were determined.

References

Garbero, A., & Jäckering, L. (2021). The potential of agricultural programs for improving food security: A multi-country perspective. Global Food Security, 29, 100529.

Cordero, E., Longchamps, L., Khosla, R., & Sacco, D. (2020). Joint measurements of NDVI and crop production data-set related to combination of management zones delineation and nitrogen fertilisation levels. Data in Brief, 28, 104968.

Talaviya, T., Shah, D., Patel, N., Yagnik, H., & Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58–73.

Bwambale, E., Abagale, F. K., & Anornu, G. K. (2022). Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review. Agricultural Water Management, 260, 107324.

Pelaez, D. A., Gualdron, O. E., & Torres, I. (2020). Soil characterization through remote acquisition of electromagnetic radiation. Journal of Physics: Conference Series, 1587(1), 12033.

Deng, L., Mao, Z., Li, X., Hu, Z., Duan, F., & Yan, Y. (2018). UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 124–136.

Eddy, I. M. S., et al. (2017). Integrating remote sensing and local ecological knowledge to monitor rangeland dynamics. Ecological Indicators, 82, 106–116.

Vecchio, Y., De Rosa, M., Adinolfi, F., Bartoli, L., & Masi, M. (2020). Adoption of precision farming tools: A context-related analysis. Land Use Policy, 94, 104481.

Griesche, C., & Baeumner, A. J. (2020). Biosensors to support sustainable agriculture and food safety. TrAC Trends in Analytical Chemistry, 128, 115906.

Singh, P. J., & De Silva, R. (2018). Design and implementation of an experimental UAV network. In 2018 International Conference on Information and Communications Technology (ICOIACT) (pp. 168–173).

Xie, C., & Yang, C. (2020). A review on plant high-throughput phenotyping traits using UAV-based sensors. Computers and Electronics in Agriculture, 178, 105731.

RadhaKrishna, M. V. V., Govindh, M. V., & Veni, P. K. (2021). A review on image processing sensor. Journal of Physics: Conference Series, 1714(1), 12055.

Chuchico-Arcos, C., & Rivas-Lalaleo, D. (2021). Sensor nodes and communication protocols of the internet of things applied to intelligent agriculture. In Applied Technologies: Second International Conference, ICAT 2020, Quito, Ecuador, December 2–4, 2020, Proceedings 2 (pp. 686–703).

Whitcraft, A. K., Becker, Reshef, I., Justice, C. O., & Jarvis, I. (2022). GEO Global Agricultural Monitoring and Global Policy Frameworks. Earth Observation and Applications for Global Agricultural Monitoring, Global Policy Frameworks, 159–175.

Rivera, L. B., Bonilla, B. M., & Obando-Vidal, F. (2021). Procesamiento de imágenes multiespectrales captadas con drones para evaluar el índice de vegetación de diferencia normalizada en plantaciones de café variedad Castillo. Ciencia y Tecnología Agropecuaria, 22(1).

Modica, G., Messina, G., De Luca, G., Fiozzo, V., & Praticò, S. (2020). Monitoring the vegetation vigor in heterogeneous citrus and olive orchards. A multiscale object-based approach to extract trees’ crowns from UAV multispectral imagery. Computers and Electronics in Agriculture, 175, 105500.

Fern, R. R., Foxley, E. A., Bruno, A., & Morrison, M. L. (2018). Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland. Ecological Indicators, 94, 16–21.

Tang, J., Petrie, P., & Whitty, M. (2019). Low-Cost Filter Selection from Spectrometer Data for Multispectral Imaging Applications. IFAC-PapersOnLine, 52(30), 277–282.

Published

2023-12-11 — Updated on 2023-12-20

Versions

How to Cite

Gualdrón Guerrero, L. D., Gualdrón Guerrero, O. E., & Maestre Delgado, M. (2023). Evolution and recovery of pastures to optimize livestock nutritional yields by interpreting normalized vegetation indices using multispectral surveys. COLOMBIAN JOURNAL OF ADVANCED TECHNOLOGIES, 2(42), 105–114. https://doi.org/10.24054/rcta.v2i42.2701 (Original work published December 11, 2023)

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