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Remote precision management of turf grass sod production by means of artificial intelligence and satellite imaging (SODSAT)
Start date: Feb 1, 2014, End date: Jan 31, 2016 PROJECT  FINISHED 

The agricultural surfaces employed for turf grass sod production are increasing yearly thanks to expanding demand and thanks to the relative profitability of this type of crop. Sod (natural turf) production agriculture in the EU can be estimated in excess of 80.000 ha and 20.000 workers for a revenue of approximately 2.4 Bn €. As such, turf grass sod production is gradually shifting from the status of niche production to agricultural crop proper. In order to maintain the current profitability in an increasingly competitive market, sod growers need to increase or maintain certain quality parameters (uniformity of colour, texture, density, etc.), while addressing spiralling costs for fertilizers, pesticides and irrigation water. Naturally, an optimization in the use of these inputs would not only keep production costs down, but it would also greatly diminish the environmental impact and footprint of sod production.Satellite spectral imagery, thanks to the frequent high correlation between spectral reflectance parameters and several crop parameters, would go a long way in identifying excesses or deficiencies in irrigation and fertilization. Satellite imagery analysis for crop production currently exists, but it is aimed and calibrated for traditional crops (wheat, maize, etc.) and not for turf grass sod production, which needs the development of dedicated new tools to be used in such production fields. Adding on-site valuable sensing, increased yield on natural turf grass production may be achieved.The project solved this situation by developing a web based expert system, multi spectral satellite imaging analysis and on-site sensing and portable devices software to aid decision making in sod farms, in order to decrease chemical and agronomical inputs, while maintaining or increasing turf grass quality. The system will provide expert agronomical recommendations based on its historic and current data and current multi spectral image processing and on-site sensing
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