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


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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Allen RG, Pereira LS, Raes D, Smith M, 1998. Crop evapotranspiration - Guidelines for computing crop water requirements. FAO Irrig Drain Paper 56.

Balzarini M, Teich I, Bruno C, Peña A, 2011. Making genetic biodiversity measurable: a review of statistical multivariate methods to study variability at gene level. FCA UNCUYO 43(1): 261-275.

Baselga JJ, 1996. A Penman-Monteith for semi-arid climate in South-Western Spain. Proc 1st Int Conf: Evapotranspiration and Irrigation Scheduling. Ed. ASAE-IA. pp: 999-1007.

Cambardella CA, Moorman TB, Novak JM, Parkin TB, Karlen DL, Turco RF, Konopka, AE, 1994. Field-scale variability of soil properties in Central Iowa soils. Soil Sci Soc Am J 58: 1501-1511.

Fortes R, Prieto H, Millán S, Terrón JM, Blanco J, Campillo C, 2014. Using apparent electric conductivity and NDVI measurements for yield estimation of processing tomato crop. T ASABE 57(3): 827-835.

Gianquinto G, Orsini F, Fecondini M, Mezzetti M, Sambo P, Bona S, 2011. A methodological approach for defining spectral indices for assessing tomato nitrogen status and yield. Eur J Agron 35: 135-143.

Goovaerts P, 1997. Geostatistics for natural resources evaluation. Oxford Univ Press, NY. 483 pp.

Heisel T, Andersen C, Ersboll AK, 1996. Annual weed distributions can be mapped with kriging. Weed Res 36(4): 325-337.

Isaaks EH, Srivastava RM, 1989. An introduction to applied geostatistics. Oxford Univ Press, NY. 561 pp.

Jamieson PD, Porter JR, Wilson DR, 1991. A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Fields Crop Res 27: 337-350.

Jongschaap REE, 2006. Run-time calibration of simulation models by integrating remote sensing estimates of leaf area index and canopy nitrogen. Eur J Agron 24(4): 316-324.

Kitchen NR, Sudduth KA, Drummond ST, 1999. Soil electrical conductivity as a crop productivity measure for claypan soils. J Prod Agric 12: 607-617.

Kitchen NR, Drummond ST, Lund ED, Sudduth KA, Buchleiter GW, 2003. Electrical conductivity and topography related to yield for three contrasting soil-crop systems. Agron J 95: 483-495.

Koller M, Upadhyaya SK, 2005. Prediction of processing tomato yield using a crop growth model and remotely sensed aerial images. T ASABE 48(6): 2335-2341.

Matheron G, 1963. Principles of geostatistics. Econ Geol 58(8): 1246-1266.

Loague KM, Green RE, 1991. Statistical and graphical methods for evaluating solute transport models: overview and application. J Contam Hydrol 7: 51-73.

Ma BL, Dwyer LM, Costa C, Cober ER, Morrison MJ, 2001. Early prediction of soybean yield from canopy reflectance measurements. Agron J 93: 1227-1234.

Moral FJ, Terrón JM, Marques da Silva JR, 2010. Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil Till Res 106: 335-343.

Odeh IOA, McBratney AB, Chittleborough DJ, 1995. Further results on prediction of soil properties from terrain attributes: Heterotopic cokriging and regression-kriging. Geoderma 67: 215-226.

Panagopoulos T, Jesus J, Antunes MDC, Beltrao J, 2006. Analysis of spatial interpolation for optimising management of a salinized field cultivated with lettuce. Eur J Agron 24(1): 1-10.

Panagopoulos T, Jesus J, Blumberg D, Ben-Asher J, 2014. Spatial variability of durum wheat yield as related to soil parameters in an organic field. Comm Soil Sci Plant Anal 45(15): 2018-2031.

Price K, Egbert S, Lee R, Boyce R, Nellis MD, 1997. Mapping land cover in a high plains agroecosystem using a multi-date landsat thematic mapper modeling approach. T Kansas Acad Sci 100(1-2): 21-33.

Sellers PJ, 1985. Canopy reflectance, photosynthesis and transpiration. Int J Remote Sens 6: 1335-1372.

Stein A, Corsten LCA, 1991. Universal kriging and cokriging as a regression procedure. Biometrics 47(2): 575-588.

Stewart CM, McBratney AB, Skerritt JH, 2002. Site-specific durum wheat quality and its relationship to soil properties in a single field in northern New South Wales. Precis Agr 3(2): 155-168.

Vouillot MO, Huet P, Boissard P, 1998. Early detection of N deficiency in a wheat crop using physiological and radiometric methods. Agronomie 18: 117-130.

Xue L, Cao W, Luo W, Dai T, Zhu Y, 2004. Monitoring leaf nitrogen status in rice with canopy spectral reflectance. Agron J 96: 135-142.

Yamagishi J, Nakamoto T, Richner W, 2003. Stability of spatial variability of wheat and maize biomass in a small field managed under two contrasting tillage systems over 3 years. Field Crops Res 81(2-3): 95-108.

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.
Agricultural engineering