Autonomously estimating leaf parameters of sugarbeet plants in real agricultural environments

Our society needs food, feed, and fiber which stems in large part from agricultural crop production. Producing these for a growing world population in a sustainable manner and reducing the side-effects of agricultural production processes on the environment is a key challenge for a future-oriented society. A possible solution for tackling this challenge is the breeding of novel varieties that are more resistant to plant diseases and thus require less agrochemicals, but also produce more yield at the same time. For selection of promising varieties, breeders need to assess the development of the plants regularly, this process is called phenotyping. Until today, this is mainly performed manually by well-trained experts in a process that is very time-consuming, costly and often invasive. In this work, we propose a novel robotic approach to measure phenotypic traits in an objective, non-invasive, and repeatable manner on real sugar beet fields. For this purpose, we exploit point clouds of leaves, which are then used to fit a parametric mesh template. Using such a template model has several advantages for parameter estimation. Given the fitted template, we then can extract multiple key phenotypic leaf traits, such as leaf blade length, width and petiole length. We evaluate our approach on data from a greenhouse and real agricultural fields containing sugar beets. Our experimental evaluation shows superior accuracy compared to existing approaches, paving the way for more accurate automatic trait detection for improved breeding performance for a faster development of resistant, high-yielding varieties. • Phenotypic trait estimation in the field can be performed by fitting a leaf model. • A triangle mesh as leaf model models the thin structures well. • Using a prior leaf model makes the system robust to noise in the data. • The approach works well on data collected in densely planted crop fields. • The method presents good performance also on low resolution data.