Accelerating and improving breeding towards more efficient crops and varieties is key for increasing yield and improving the resilience of plants. For plant breeders, it is important to observe and document phenotypic traits describing the appearance of plants in the field to evaluate the quality and success of the breeding process. Within the University of Bonn startup Pheno-Inspect, we aim to offer growers and farmers novel software solutions for automated high-throughput phenotyping in the fields. For sensing, we rely on small and lightweight aerial platforms, which enable a flexible, large-area, and time-efficient survey of fields and plot experiments. With our software toolchain, we provide breeders and farmers with a tool to gain precise knowledge of the crop and individual plants. We automatically detect phenotypic traits of crop plants, the species of plants and weeds in the field and derive site- or plot-specific statistics. Our approach relies on state-of-the-art machine learning methods optimized for the agricultural domain to semantically interpret the captured image data and to extract the desired parameters about the plants. The learning procedures developed by us use expert knowledge inserted by the user to adapt efficiently and thus deliver the desired results quickly and effectively with regard to individual problems and local characteristics of the environment.