Fusion-Driven Tree Reconstruction and Fruit Localization: Advancing
  Precision in Agriculture
Kaiming Fu Peng Wei Juan Villacres Zhaodan Kong Stavros G. Vougioukas Brian N. Bailey
Fruit distribution is pivotal in shaping the future of both agriculture and
agricultural robotics, paving the way for a streamlined supply chain. This
study introduces an innovative methodology that harnesses the synergy of RGB
imagery, LiDAR, and IMU data, to achieve intricate tree reconstructions and the
pinpoint localization of fruits. Such integration not only offers insights into
the fruit distribution, which enhances the precision of guidance for
agricultural robotics and automation systems, but also sets the stage for
simulating synthetic fruit patterns across varied tree architectures. To
validate this approach, experiments have been carried out in both a controlled
environment and an actual peach orchard. The results underscore the robustness
and efficacy of this fusion-driven methodology, highlighting its potential as a
transformative tool for future agricultural robotics and precision farming.