During the process of plant protection in agriculture, the distribution and deposition of droplets or fog fields could directly influence the effectiveness and efficiency of spray. The traditional method of measurement of the distribution of droplets mainly used water sensitive papers, glass containers or flour to collect data and inverse results, while a new method of measurement based on the principle of reflection of LIDAR was presented. Droplets were the major targets of the study, and four important algorithms were primarily developed, including the recognition and extraction of targets, the superposition in time-domain, the calculation of effective ranges of distribution, and the development of 3D distribution models. Combined with these algorithms, in order to eliminate the environmental noise, the methods of Fuzzy Environment Matching and Secondary Filter were created and utilized. Meanwhile, the statistics was used for analysis of the duration of scanning as well as computation of the distribution, with enough datasets but the minimum length of time. The results of the experiments showed that the relative error of measurement was less than 7% and Relative Standard Deviation was less than 16%, compared with the values of manual measurement. Furthermore, the 3D models were accurate and clarified in the wind-tunnel experiment. The completed system based on this method could adapt to the requirements of both indoor and outdoor detection. Besides, it is capable of the quantized detection of droplet distribution, providing an effective way of tests for spray technique, especially for the research of the application of plant protection by UAVs.
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.
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