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Digitalisation in agriculture is currently expanding very rapidly. Digital field maps, geo-referenced machine movements, and a vast set of sensors on different platforms are already utilised on arable farms and even robotic field management is tested under practical field conditions. Digitalisation and artificial intelligence enable more effective and precise weed management through automated weed recognition. Sensor-based recognition systems automatically differentiate between weed and crop plants allowing for selective and site-specific weed control measures. By reducing herbicide inputs, site-specific herbicide application can mitigate negative impacts on the environment and reduce production costs. With support from the Federal Ministry of Food and Agriculture (BMEL), the project “Better-Weeds” uses the technical possibilities of GIS-based imagery from unmanned aerial vehicles (UAV) and artificial intelligence (AI)-driven plant recognition on arable fields. The novel and innovative part of the project is a knowledge-based merging process. It combines the location data on the spatial distribution of different weed species with biological and ecological weed traits, as well as soil and climatic data to create extensive field maps. In addition, farm-specific agronomic conditions such as the crop rotation on the respective field and available weed control technology are considered. Together, this combined information is used to create a site-specific weed management plan for a given arable field taking into account weed control thresholds and the competitive ability and ecological benefits (e.g. habitat for beneficial insects) of the present weed species. The application of the generated management plan forms a step towards reducing reduce herbicide applications and increasing in-field weed diversity.

Site‐specific weed management (SSWM) is increasingly employed to reduce herbicide inputs. Incorporating functional traits of weed species allows for the selection of SSWM methods that effectively reduce the abundance of weeds with a high competitive potential (disservice) while preserving weeds that provide beneficial ecosystem services (service). In this study, we aim to assess relevant weed functional traits and translate this information into a spatial trait distribution map for weed (dis‐)service provision. The distribution of weed abundance in a field was recorded using a spatial grid. Data on functional traits for the recorded weed species were extracted from published datasets and combined into the two variables, service and disservice. Individual traits (service/disservice) were weighted for each pixel of the weed distribution map based on the number of individual plants per species. Principal component analysis was employed to generate independent variables to describe the potential for service and disservice provision. As a result, two (dis‐)service trait‐based distribution maps were generated: one highlights field areas that provide enhanced ecological services, while the other displays areas with a high disservice potential. The results show that around 61% of the area in the field had a high service potential. The area with a high disservice was slightly higher than the half of the area with a high service, while about 32% of the field has both high service and disservice potential in the same area. This study presents a spatially explicit approach to incorporate information on weed functional traits into SSWM approaches targeted at reducing weed competition while at the same time enhancing weed functional diversity. [ABSTRACT FROM AUTHOR]