Design and Testing of a Fruit Tree Variable Spray System Based on ExG-AABB

Agronomy, Journal Year: 2024, Volume and Issue: 14(10), P. 2199 - 2199

Published: Sept. 25, 2024

This paper addresses the issue of pesticide waste and low utilization rates resulting from traditional plant protection via spraying operations, which apply equal dosages to different targets or parts same target. To tackle this problem, we designed a variable fruit tree system based on ExG-AABB (excess green axis-aligned bounding box) algorithm. We used Kinect depth camera capture information about canopy constructed spray flow model using pulse width modulation control technology. Variable multi-nozzle was guided by combining data. evaluated accuracy each in calculating volume comparing coefficient determination (R2) root mean square error (RMSE) with slice convex hull method, voxel three-dimensional alpha-shape QuickHull method. The algorithm had highest R2 value (0.9334) lowest RMSE (0.0353 m3) among five models, indicating that it most accurately reflects true canopy. validates effectiveness volume. established correlation between volume, canopy-adaptive layering method point cloud processing, achieved precise calculation nozzle flow. Comparative field experiments were conducted analyze coverage rate observed flow, thereby evaluating effect system. experimental results showed compared conventional continuous spraying, not only achieves more uniform but also significantly reduces usage 48.1%. Furthermore, through optimization, average middle layer decreased 17.53%, effectively reducing phenomenon overlapping multiple nozzles improving efficiency.

Language: Английский

Optimizing GEDI Canopy Height Estimation and Analyzing Error Impact Factors Under Highly Complex Terrain and High-Density Vegetation Conditions

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 2024 - 2024

Published: Nov. 17, 2024

The Global Ecosystem Dynamics Investigation (GEDI) system provides essential data for estimating forest canopy height on a global scale. However, factors such as complex topography and dense can significantly reduce the accuracy of GEDI estimations. We selected South Taihang region Henan Province, China, our study area proposed an optimization framework to improve estimation accuracy. This includes correcting geolocation errors in footprints, screening analyzing features that affect errors, combining two regression models with feature selection methods. Our findings reveal error 4 6 m footprints at orbital scale, along overestimation region. Relative (RH), waveform characteristics, topographic features, cover influenced error. Some studies have suggested estimates areas high lead underestimation, found increased higher terrain vegetation. model’s performance improved after incorporating parameter into model. Overall, R2 best-optimized model was from 0.06 0.61, RMSE decreased 8.73 2.23 m, rRMSE 65% 17%, resulting improvement 74.45%. In general, this reveals affecting vegetation cover, premise minimizing errors. Employing enhanced estimates. also highlighted crucial role improving precision estimation, providing effective approach monitoring regions conditions. Future should further classification tree species expand diversity sample test estimated by different structures, consider distortion optical remote sensing images caused rugged terrain, mine information waveforms so enhance applicability more diverse environments.

Language: Английский