Assessment of remote sensed data for weed species recognition in agricultural fields.

Accurate geographical and temporal information is provided in large part by remote sensing. Advanced crop protection plans can be created by gathering and analysing data at various scales and resolutions to create emergency models, identification patterns, and site mapping. Recent developments in remote sensing enable the analysis and diagnosis of crop problems based on reflectance data through visible, multispectral, or hyperspectral detection utilizing very high-resolution satellites.

The strenuous physical removal of weed species based on field scouting is one management technique. The optimization method based on remote sensing predictions, fed by meteorological data, but also using vegetation information from several high-resolution remote sensing products and spectral data from different sensor types, combining them by data assimilation, is a novel aspect of the research. This method is used to optimize accurate weed detection and reliable discrimination between weeds and crop plants. By examining the spatial and spectral properties of the agricultural field, I will analyse the function of LIDAR and other time series remote sensing data in the field scouting (partly based on field surveys at the Hungarian case study site). The findings will establish a link between water, energy, and food production in agriculture and serve as the foundation for the creation of practical strategies for gathering data on target areas and making spatially selective weed control decisions.