Generation of labeled leaf point clouds for plants trait estimation.

ABSTRACT: Today, leaf trait estimation remains a labor-intensive process. The effort to obtain ground truth measurements limits how accurately this task can be performed automatically. Traditionally, plant scientists manually measure the traits of harvested leaves and associate them with sensor data, which is key for training machine learning approaches and to automate the processes. In this paper, we propose a neural network-based method to generate synthetic 3D point clouds of leaves with their associated traits to support approaches for phenotyping. We use real-world leaf point clouds to learn how to generate realistic leaves from a leaf skeleton, which is automatically extracted. We use the generated leaves to fine-tune different leaf trait estimation methods. We evaluate our generated data usin

SUBMITTER: Roggiolani G 

PROVIDER: S-EPMC12710001 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature