Biomass Prediction with 3D Point Clouds from LiDAR
Liyuan Pan, Liu Liu, Anthony G. Condon, Gonzalo M. Estavillo, Robert A. Coe, Geoff Bull, Eric A. Stone, Lars Petersson, Vivien Rolland
Abstract
With population growth and a shrinking rural workforce, agricultural technologies have become increasingly important. Above-ground biomass (AGB) is a key trait relevant to breeding, agronomy and crop physiology field experiments. However, measuring the biomass of a cereal plot requires cutting, drying and weighing processes, which are laborious, expensive and destructive tasks. This paper proposes a non-destructive and high-throughput method to predict biomass from field samples based on Light Detection and Ranging (LiDAR). Unlike previous methods that are based on the density of a point cloud or plant height, our biomass prediction network (BioNet) additionally considers plant structure. Our BioNet contains three modules: 1) a completion module to predict missing points due to canopy occlusion; 2) a regularization module to regularize the neural representation of the whole plot; and 3) a projection module to learn the salient structures from a bird’s eye view of the point cloud. An attention-based fusion block is used to achieve final biomass predictions. In addition, the complete dataset, including hand-measured biomass and LiDAR data, is made available to the community. Experiments show that our BioNet achieves ≈ 33% improvement over current state-of-the-art methods.