Towards model-based automation of plant-specific weed regulation

Abstract

Weeds are commonly known as a major factor for yield losses in agriculture, competing with crops for resources like nutrients, water, and light. However, keeping specific weeds could benefit agricultural sites for example by nitrogen fixation, erosion protection, or increasing biodiversity. This comes with technological challenges like plant detection and classification, damage estimation, and selective removal. This paper presents a model-based approach to the problem of damage estimation of perceived plants. The system uses contextual and background knowledge in the form of rules about the plant count per square meter and the distance to the nearest crop together with thresholds for each weed species. The functionality is demonstrated using an artificial dataset and exemplary thresholds, showing the potential of using knowledge about plant-crop interactions for more sophisticated weed control systems.