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ARS Home » Midwest Area » Wooster, Ohio » Application Technology Research » Research » Publications at this Location » Publication #382152

Research Project: Coordinated Precision Application Technologies for Sustainable Pest Management and Crop Protection

Location: Application Technology Research

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

Author
item MAHMUD, MD SULTAN - Pennsylvania State University
item ZAHID, AZLAN - Pennsylvania State University
item HE, LONG - Pennsylvania State University
item CHOI, DAEUN - Pennsylvania State University
item KRAWCZYK, GRZEGORZ - Pennsylvania State University
item Zhu, Heping

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/9/2021
Publication Date: 11/18/2021
Citation: Mahmud, M., Zahid, A., He, L., Choi, D., Krawczyk, ., Zhu, H. 2021. LiDAR-sensed tree canopy correction in uneven terrain conditions using a sensor fusion approach for precision sprayers. Computers and Electronics in Agriculture. 191. Article 106565. https://doi.org/10.1016/j.compag.2021.106565.
DOI: https://doi.org/10.1016/j.compag.2021.106565

Interpretive Summary: Accurate characterization of fruit tree architectures is required for modern sprayers equipped with sensors to apply correct amounts of agrochemicals to protect apple trees and minimize pesticide waste. This requirement is especially important when there are terrain variations with slopes. In this research, the positioning error of apple tree canopy points was corrected using a sensor fusion-based laser range guided system. A fusion algorithm was developed to process the laser data through the corresponding timestamps. Mathematical models were developed to correct acquired signal positions on the target canopy due to terrain slope variations. The system was tested in three orchard sites with Gala and Golden Delicious varieties planted at the longitudinal, lateral, and combination of longitudinal and lateral slopes, respectively. Field evaluation results demonstrated that the system was able to correct the apple tree canopy points in different sloping conditions in orchards. In the future, the integration of the state-of-art of this study will be integrated with the canopy density algorithms for accurate measurements of tree foliage densities and for automatic managements of variable-rate agrochemical applications.

Technical 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 light detection and ranging (LiDAR) sensor and an inertial navigation system-global navigation satellite system (INS-GNSS). 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 location. 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. With the accurate tree canopy points, it is anticipated that the accurate canopy density measurement methodology could be used for precision spraying applications in tree fruit orchards.