DIGITAL ORCHARD CONSTRUCTION BASED ON NEURAL RADIANCE FIELD AND GEOREFERENCING TECHNOLOGY

This study aims to construct digital fruit trees with high-precision geolocation and high-quality canopy phenotypic details, supporting the development of digital fruit tree technology and the establishment of smart orchards. The Neural Radiance Fields (NeRF) theory was integrated with georeferencing technology. Firstly, multiple ground control points were placed around the tree, and their WGS-84 coordinates were recorded using an RTK surveying instrument. Next, a drone captured multi-view images of the fruit tree, recording the camera poses during the image acquisition. The multi-view fruit tree images undergo ray casting, hierarchical sampling, and high-frequency position encoding before being input into a Multilayer Perceptron (MLP). The MLP was then supervised through volume rendering to obtain a convergent radiance field that reflects the true form of the fruit tree, resulting in the generation of a fruit tree point cloud. Finally, by establishing correspondences between the points in the fruit tree point cloud and the ground control points in the real world, a rigid transformation matrix was computed to convert the point cloud from a local coordinate system to WGS-84 coordinates, yielding a geographically informed digital fruit tree. The experiments demonstrate that the constructed digital fruit tree exhibits excellent phenotypic details and accurately represents multi-scale characteristics. The accuracy of tree morphology indicators, such as tree height, crown length, and width, reached 99.12%, 99.34%, and 99.22%, respectively. Compared to point clouds generated by traditional Structure from Motion-Multi View Stereo (SFM-MVS) methods, the root mean square errors were reduced by 61.24%, 73.48%, and 62.32%, respectively. Additionally, the georeferencing accuracy achieved millimeter-level precision, with registration errors generally below 2 mm. The proposed method can construct digital fruit trees with high geolocation accuracy, detailed phenotypic information, and scale consistency, overcoming key barriers in the development of digital fruit tree technology. It can provide comprehensive data for various production operations in smart orchards.