Spray drift evaluation with point clouds data of 3D LiDAR as a potential alternative to the sampling method

Abstract

Spray drift is an inescapable consequence of agricultural plant protection operation, which has always been one of the major concerns in the spray application industry. Spray drift evaluation is essential to provide a basis for the rational selection of spray technique and working surroundings. Nowadays, conventional sampling methods with passive collectors used in drift evaluation are complex, time-consuming, and labor-intensive. The aim of this paper is to present a method to evaluate spray drift based on 3D LiDAR sensor and to test the feasibility of alternatives to passive collectors. Firstly, a drift measurement algorithm was established based on point clouds data of 3D LiDAR. Wind tunnel tests included three types of agricultural nozzles, three pressure settings, and five wind speed settings were conducted. LiDAR sensor and passive collectors (polyethylene lines) were placed downwind from the nozzle to measure drift droplets in a vertical plane. Drift deposition volume on each line and the number of LiDAR droplet points in the corresponding height of the collecting line were calculated, and the influencing factors of this new method were analyzed. The results show that 3D LiDAR measurements provide a rich spatial information, such as the height and width of the drift droplet distribution, etc. High coefficients of determination (R2 > 0.75) were observed for drift points measured by 3D LiDAR compared to the deposition volume captured by passive collectors, and the anti-drift IDK12002 nozzle at 0.2 MPa spray pressure has the largest R2 value, which is 0.9583. Drift assessment with 3D LiDAR is sensitive to droplet density or drift mass in space and nozzle initial droplet spectrum; in general, larger droplet density or drift mass and smaller droplet size are not conducive to LiDAR detection, while the appropriate threshold range still needs further study. This study demonstrates that 3D LiDAR has the potential to be used as an alternative tool for rapid assessment of spray drift.

Visualization of Lidar-Based 3D Droplet Distribution Detection for Air-Assisted Spraying

“…Current studies using LiDAR to detect droplets tend to focus on drift rather than droplets. The main methods used to quantify the distribution are droplet density [17,20,22,28] and emission intensity [29,30]. Since the spatial density of droplets cloud is much higher than that of drift, it is not easy for droplets to obscure droplets in the drift test, and the depth of the test field is sufficient to achieve droplet density detection over a larger area.…”

“…This was achieved by scanning in a 2D plane using a pulsed LiDAR and using the LiDAR reflection signal intensity to obtain a 2D measurement of the plume [18]. However, the research on spray 3D distribution is currently focused more on drift measurement [19,20], with less emphasis on spray droplets. Li et al used 3D LiDAR to achieve the droplet distribution measurement of a single nozzle and performed quantitative analysis [21], and Boqin Liu et al used 3D LiDAR to perform droplet detection on the air-assisted unit and fitted the deposition based on the droplet density distribution [22].…”

Forward Collision Warning Strategy Based on Millimeter-Wave Radar and Visual Fusion

“…In order to achieve target detection accuracy, many research groups and companies have intensively studied various sensor systems such as radars, cameras, light detection and ranging (LiDAR) sensors, ultrasonic radar sensors, and infrared sensors [5][6][7]. Due to the inherent limitations of the sensor, autonomous vehicles cannot meet the demands of environmental perception by relying on a single sensor in different weather conditions and complex road scenarios [8].…”