Remote sensing of crop yield sugarcane in Cacocum, Cuba.

Sensoramiento remoto del rendimiento agrícola en caña de azúcar en Cacocum, Cuba

The study was carried out in the 2021 year with the
objective of estimating yield of sugarcane through the Normalized Difference Index of Vegetation in the "Cristino Naranjo" Sugar Company, located in the Cacocum municipality, Holguín province, Cuba. 32 randomized sample points were drawn in different months in which the images belonging to the Landsat 8 OLI/TIRS satellite projected in the WGS 84 UTM Zone 18 North system in grid 011/046 were chosen. The radiometric correction of each satellite image and the sampling scheme were carried out with the QGIS "A Coruña" software version 3.10. The value of each point was extracted with ArcGIS 10.5 after the calculation of the NDVI. For the statistical processing, Stargraphics Plus 5.0 software was used; in which, the linear regression analysis was carried out between the values obtained from the NDVI at each sampling point and the real yield values offered by specialists from the central analysis area that corresponds to the image data used. The determination of the NDVI vegetative index showed values from 0 to 0.5,
equivalent to a low vegetation in the study area. The coefficient of determination indicates that the model explains 97.8598 of the yield variability in the sugarcane crop, which can be used as a mathematical model for estimating crop yield under the edaphoclimatic conditions of the study site.

1. Aguilar, N., Galindo, G., Fortanelli, J. and Contreras, C. 2010. Índice normalizado de vegetación en caña de azúcar en la Huasteca Potosina Avances en Investigación Agropecuaria 14(2):49-65
2. Awad, M. 2019. Toward precision in crop yield estimation using remote sensing and optimization techniques. Agriculture 9(54):1-13.
3. Bocco, M., Sayago, S. and Willington, E. 2014. Neural network and crop residue index multiband models for estimating crop residue cover from Landsat TM and ETM+ images. International Journal of Remote Sensing 35(10): 3651-3663.
4. Dancé, J. y Sáenz, D. 2016. La cosecha de caña de azúcar: impacto económico, social y ambiental. Dirección de Investigación FCCEF-USMP, Perú, p1-18.
5. Food and Agriculture Organization of the United Nations. 2017. Food and Agriculture Data (FAOSTAT). Food and Agriculture Organization of the United Nations, Rome, Italy, p48.
6. Fortes, C. 2006. Discrimination of sugarcane varieties using Landsat 7 ETM+ spectral data. International Journal of Remote Sensing 27(7): 395-412.
7. García, R., Villazón, J., Morales, A. y Velázquez, E. 2019. Efecto de la cosecha mecanizada sobre la variabilidad espacial de la resistencia a la penetración. Revista Ingeniería Agrícola, 9(2): 45-50.
8. Gilabert, M.A., González, P.J. y García, H.J. 1997. Acerca de los índices de vegetación. Revista de Teledetección 8(1): 1-10.
9. Hernández, A., Pérez, J. y Rivero, L. D. 2015. Nueva versión de clasificación genética de suelos de Cuba. AGRINFOR, La Habana, Cuba, p91.
10. Krishna, R. P. 2002. Remote sensing: a technology for assessment of sugarcane crop acreage and yield. Sugar Tech 4(3):97-101.
11. Li, A., Liang, S., Wang, A. and Qin, J. 2007. Estimating crop cield from Multi-temporal satellite data using multivariate regression and neural network techniques. Photogrammetric Engineering and Remote Sensing 73(10): 1149-1157.
12. Meera, G.G., Parthiban, S., Thummalu, N. and Christy, A. 2015. Ndvi: Vegetation change detection using remote sensing and gis–A case study of Vellore District. Procedia Computer Science 57: 1199-1210.
13. Molijn, R., Iannini, L., Vieira, J. and Hanssen, R. 2019. Sugarcane productivity mapping through C-Band and L-Band SAR and Optical Satellite Imagery, Remote Sensing, 11(1109): 1-27.
14. Pandey, S., Patel, N. R., Danodia, A. and Singh, R. 2019. Discrimination of sugarcane crop and cane yield estimation using landsat and irs resourcesat satellite data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 62-3/W6: 229-233.
15. Rawashdeh, S. 2012. Assessment of change detection method based on normalized vegetation index in environmental studies. Internatinal Journal of Applied Science and Engineering, 10(2): 89-97.
16. Rouse, J., Haas, R., Schell, J. and Deering, D. 1974. Monitoring Vegetation Systems in the Great Plains with ERTS Proceeding. En: Third Earth Reserves Technology Satellite Symposium, Greenbelt: NASA SP-351, USA.
17. Singh, K., Sunila, G. and Kumar, S. 2020. Crop Yield Prediction Techniques using Remote Sensing Data. International Journal of Engineering and Advanced Technology 9(3): 3683- 3689.
18. Stas, M., Orshovn, J.V., Dong, Q., Heremans, S. and Zhang, B. 2016. A Comparison of machine learning algorithm for regional wheat yield prediction using NDVI time series of SOPT-VGT”, IEEE International Conference Agro-Geoinformatics,pp1 -5.
19. United State of Geological Survey. 2021. Landsat Earth observation satellites: U.S. Geological Survey Fact Sheet 2015-3081.
20. Virnodkar, S., Pachghare, V., Patil, V. and Kumar, S. 2020. Remote sensing and machine learning for crop water stress determination in various crops: a critical review. Precision Agriculture. https://doi.org/10.1007/s11119-020-09711-9
21. Zenteno, G., Palacios, E., Tijerina, L. y Flores, H. 2017. Aplicación de tecnologías de percepción remota para la estimación del rendimiento en caña de azúcar. Revista Mexicana de Ciencias Agrícolas, 8(7): 1575-1586.