RapSmartN
Optimal winter oilseed rape fertilization (N) through remote sensing data and quality-assured artificial intelligence
Project description
The fertilisation of winter oilseed rape is a key aspect of agricultural practice that poses both economic and environmental challenges. Precise nitrogen fertilisation can optimise yields and reduce the environmental impact at the same time. However, there are currently deficits in the precise recording of the required nitrogen quantities.
The ‘RapSmartN’ project aims to use remote sensing data and artificial intelligence (AI) to enable more precise and needs-based nitrogen fertilisation in winter oilseed rape cultivation. To this end, AI models are being developed that accurately estimate the amount of nitrogen absorbed in autumn and calculate the further nutrient requirements for the rest of the growing season. This information will be made available to farmers in the form of nitrogen application maps.
The robust and powerful AI models should enable precise estimation of nitrogen uptake, making it easier for farmers to access high-quality application maps. This leads to more efficient nitrogen use, yield increases on underserved areas and fertiliser savings in zones with lower yield potential. This reduces potential negative environmental impacts and increases social acceptance of agricultural production. The project thus contributes to the digitalisation of agriculture and to regional value creation.This innovation significantly improves the efficiency and sustainability of winter oilseed rape cultivation, offering both economic and ecological benefits.