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Fruit distribution is pivotal in shaping the future of both agriculture andagricultural robotics, paving the way for a streamlined supply chain. Thisstudy introduces an innovative methodology that harnesses the synergy of RGBimagery, LiDAR, and IMU data, to achieve intricate tree reconstructions and thepinpoint localization of fruits. Such integration not only offers insights intothe fruit distribution, which enhances the precision of guidance foragricultural robotics and automation systems, but also sets the stage forsimulating synthetic fruit patterns across varied tree architectures. Tovalidate this approach, experiments have been carried out in both a controlledenvironment and an actual peach orchard. The results underscore the robustnessand efficacy of this fusion-driven methodology, highlighting its potential as atransformative tool for future agricultural robotics and precision farming.

Fusion-Driven Tree Reconstruction and Fruit Localization: Advancing Precision in Agriculture