Using NDVI and guided sampling to develop yield prediction maps of processing tomato crop

  • Rafael Fortes CICYTEX. Ctra. A-V, km 372, 06187 Guadajira (Badajoz)
  • María del Henar Prieto CICYTEX. Ctra. A-V, km 372, 06187 Guadajira (Badajoz)
  • Abelardo García-Martín Universidad de Extremadura, Escuela de Ingenierías Agrarias, Dept. Ingeniería del Medio Agrícola y Forestal. Avda Adolfo Suarez s/n, 06007 Badajoz
  • Antón Córdoba Lola Fruits S.L. Plaza de España 5, 06002 Badajoz
  • Laura Martínez Sociedad Gestora de Activos Productivos e Inmobiliarios (Roma SL). Ctra Villafranco Balboa Km 1,5. Badajoz
  • Carlos Campillo CICYTEX. Ctra. A-V, km 372, 06187 Guadajira (Badajoz)
Keywords: Solanum lycopersicum, ordinary kriging, regression kriging, vegetation index, precision agriculture

Abstract

The use of yield prediction maps is an important tool for the delineation of within-field management zones. Vegetation indices based on crop reflectance are of potential use in the attainment of this objective. There are different types of vegetation indices based on crop reflectance, the most commonly used of which is the NDVI (normalized difference vegetation index). NDVI values are reported to have good correlation with several vegetation parameters including the ability to predict yield. The field research was conducted in two commercial farms of processing tomato crop, Cantillana and Enviciados. An NDVI prediction map developed through ordinary kriging technique was used for guided sampling of processing tomato yield. Yield was studied and related with NDVI, and finally a prediction map of crop yield for the entire plot was generated using two geostatistical methodologies (ordinary and regression kriging). Finally, a comparison was made between the yield obtained at validation points and the yield values according to the prediction maps. The most precise yield maps were obtained with the regression kriging methodology with RRMSE values of 14% and 17% in Cantillana and Enviciados, respectively, using the NDVI as predictor. The coefficient of correlation between NDVI and yield was correlated in the point samples taken in the two locations, with values of 0.71 and 0.67 in Cantillana and Enviciados, respectively. The results suggest that the use of a massive sampling parameter such as NDVI is a good indicator of the distribution of within-field yield variation.

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Published
2015-02-10
How to Cite
Fortes, R., Prieto, M. del H., García-Martín, A., Córdoba, A., Martínez, L., & Campillo, C. (2015). Using NDVI and guided sampling to develop yield prediction maps of processing tomato crop. Spanish Journal of Agricultural Research, 13(1), e0204. https://doi.org/10.5424/sjar/2015131-6532
Section
Agricultural engineering