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Use of satellite images to differentiate productivity zones in commercial processing tomato farms

Campillo, C., Carrasco, J., Gordillo, J. L., Cordoba, A., Macua, J. I.
Acta horticulturae 2019 no.1233 pp. 97-104
commercial farms, crop production, crops, cultivars, farmers, geographic information systems, image analysis, irrigation systems, normalized difference vegetation index, planning, remote sensing, satellites, soil heterogeneity, tomatoes
New trends in crop production and new technologies are enabling better management of large fields with great soil heterogeneity. In this sense, the improvement in the temporal and spatial resolution of the new satellites produces images of the crop throughout its crop cycle. With vegetative indexes such as the normalized difference vegetation index (NDVI), an evolution of the development of the tomato plant cover of industry can be obtained with images every 5 to 7 days. This free tool available to any user with minimal knowledge of GIS will allow the farmer to identify the different productive areas of the farm in advance of planning the harvest. The objective of this study was to evaluate the capacity of satellite images to detect different productive zones in a commercial farm of processing tomato during two years of cultivation. For this study, a trial was conducted in 2016 and 2017 in commercial plots belonging to the ROMA Company, with 'H-1311' tomato cultivar of medium maturity (125-135 days). The irrigation system in the farm was subsurface drip. The spatial variability was studied with different NVDI images from Sentinel 2 satellite obtained in both years. These points were used as control point to measure the development of the crop during the entire crop cycle and to establish different productivity zones. The result in the yield data in the different control points selected showed different yields between the different zones of the farm. The harvest data were correlated with NVDI images from the Sentinel satellite 2 (R2=0.81) in 2017 to produce a map of yield of all the farm surface in both years of cultivation. In 2016 this relation was worse. The satellite images allowed the identification of zones of development and production of crops, which can be a good tool to study high and low productivity points on the farm.