BioNet and NeFF: Crop Biomass Prediction from Point Clouds to Drone Imagery

Xuesong Li, Zeeshan Hayder, Ali Zia, Connor Cassidy, Shiming Liu, Warwick Stiller, Eric Stone, Warren Conaty, Lars Petersson, Vivien Rolland; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 7754-7764

Crop biomass offers crucial insights into plant health and yield making it essential for crop science farming systems and agricultural research. However current measurement methods which are labor-intensive destructive and imprecise hinder large-scale quantification of this trait. To address this limitation we present a biomass prediction network (BioNet) designed for adaptation across different data modalities including point clouds and drone imagery. Our BioNet utilizing a sparse 3D convolutional neural network (CNN) and a transformer-based prediction module processes point clouds and other 3D data representations to predict biomass. To further extend BioNet for drone imagery we integrate a neural feature field (NeFF) module enabling 3D structure reconstruction and the transformation of 2D semantic features from vision foundation models into the corresponding 3D surfaces. For the point cloud modality BioNet demonstrates superior performance on two public datasets with an approximate 6.1% relative improvement (RI) over the state-of-the-art. In the RGB image modality the combination of BioNet and NeFF achieves a 7.9% RI. Additionally the NeFF-based approach utilizes inexpensive portable drone-mounted cameras providing a scalable solution for large field applications.