LiDAR-sensed tree canopy correction in uneven terrain conditions using a sensor fusion approach for precision sprayers

Abstract

Precision spraying is one of the most promising techniques to produce healthy and sustainably profitable crops. However, accurate canopy density measurements for precision spraying decisions are still a challenging endeavor, especially in orchards with uneven terrain conditions. A sensor fusion-based canopy point correction system was developed with a 3D light detection and ranging (LiDAR) sensor and an inertial navigation system-global navigation satellite system (INS-GNSS) for accurate tree canopy density measurement. The LiDAR sensor was used to acquire the tree canopy architectures, while the INS-GNSS sensor was to evaluate the terrain slopes and the tree georeferenced locations. A mathematical model was developed to perform the simulation for correction of canopy points based on given changes in the roll, pitch, and yaw angles. A sensor fusion algorithm was developed to process the canopy point corrections for the tree fruit orchards with three different sloping conditions, including longitudinal, lateral, and combination of both slopes. Simulation results reported that the developed model established the correction of tree canopy points with varying roll, pitch, and yaw angles. Field evaluation results suggested that the developed system could be used for correcting canopy points at any sloping conditions in various terrains. The measured tree canopy density from the corrected canopy points reported a possible of off-target chemical reduction up to 13.87%, 5.19%, and 15.45% in orchard sites 1, 2 and 3, respectively. With the accurate tree canopy density measurement, it is anticipated that the developed system could be used to reduce the off-target deposition for precision spraying applications in uneven tree fruit orchards.