This work presents new approaches to plant classifcation and plant position estimation to enable feld robot based precision agriculture. The developed methods are designed for challenging real world feld situations with small crop plants, presence of close-to-crop weed and overlap of plants. The plant classifcation system is able to distinguish two or more plant classes in feld images without the need for error-prone plant or leaf segmentation. The plant
position estimation pipeline solves the generic problem of determining the position of both crop and weed plants only from image data. The combination of both methods allows feld robots to autonomously determine the type and position of plants in the feld to realize precision agriculture tasks such as single plant weed control. Experiments with a feld robot prove the applicability of the presented methods for challenging feld scenarios encountered for example in organic vegetable farming.
