TY - GEN
T1 - Biomass Prediction with 3D Point Clouds from LiDAR
AU - Pan, Liyuan
AU - Liu, Liu
AU - Condon, Anthony G.
AU - Estavillo, Gonzalo M.
AU - Coe, Robert A.
AU - Bull, Geoff
AU - Stone, Eric A.
AU - Petersson, Lars
AU - Rolland, Vivien
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Medical Imaging/Imaging for Bioinformatics/Biological and Cell Microscopy Low-level and Physics-based Vision
UR - https://www.scopus.com/pages/publications/85126085675
U2 - 10.1109/WACV51458.2022.00178
DO - 10.1109/WACV51458.2022.00178
M3 - Conference contribution
AN - SCOPUS:85126085675
T3 - Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
SP - 1716
EP - 1726
BT - Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
Y2 - 4 January 2022 through 8 January 2022
ER -