Particle Deposition Distribution of Multi-Rotor UAV-Based Fertilizer Spreader under Different Height and Speed Parameters

Abstract

As an effective supplement to ground machinery, UAVs play an important role in agriculture and have become indispensable intelligent equipment in the development of precision agriculture. Various types of agricultural UAV-based spreading devices, mainly disc-type and pneumatic-type, have appeared in domestic and foreign markets. UAV-based rice topdressing has gradually become a widely recognized application with great market potential. In the process of UAV-based low-altitude fertilization, due to the existence of the rotor wind field, the environment for particle air diffusion is complex, and the movement trajectory and deposition distribution of fertilizer are affected by many factors, resulting in large differences in the spreading. The flight height and speed have a great influence on particle movement and deposition, and a reasonable combination of work parameters can be used for efficient and high-quality particle deposition. In order to obtain better particle deposition distribution, this paper uses the method of a single flight line to test and analyze the characteristics of particle deposition distribution for fertilization using multi-rotor UAVs at different flight heights and speeds. The effective swath width and deposition uniformity obtained via the simulation of overlapped route superposition were used to optimized the appropriate work parameters to ensure that a reasonable and effective deposition amount can be obtained during actual application. The results show that the flight height and speed and the interaction of both have an important influence on the deposition amount and the effective width, but it is not a simple linear relationship. On the whole, as the flight height increases, the coefficient of variation decreases and the effective width increases, but it is not obvious when the speed is low. For the R20, when the flight speed is 2 m/s, the effective width first increases and then decreases with the increase in flight height, and the difference in the deposition amount at a height of 5 m is larger than that at other heights. Under the three working heights, the effective swath width is the same when the flight speed is 4 m/s and 6 m/s, and the effective swath width is also the same when the speed is 7 m and 9 m. For the T16, when the flight speed is 4 m/s, the deposition uniformity is relatively good, and the effective width increases with the increase in flight height. Therefore, the combination of 7–6 m/s and 9–4 m/s parameters will be the best operating parameters for R20 and T16. However, considering the actual dynamic meteorological environment in the field, the operating height can be appropriately lowered according to the influence of the crosswind during actual operation. The research results of this paper can provide scientific reference and suggestions for further improving the effect of UAV-based fertilization.

Peculiarities of Unmanned Aerial Vehicle Use in Crop Production in Russia: A Review

“…Fluid flow, spray uniformity, and working width control. As shown in [7,11,35], these parameters are controlled by the flight speed and altitude of the UAV. In [11], it is shown that by varying the UAV flight speed in the range of 10 to 50 km/h and the flight altitude from 1 to 5 m, it is possible to control the fluid flow from 1 to 5 L/min.…”

“…By analyzing Table 9, it is evident that the thickness of the film containing the PPA can be adjusted by increasing the speed and altitude of the UAV. This relationship is explained in [35], where the opening of the spray cone changes in response to increasing speed or altitude, which ultimately changes the coating surface and subsequently the film thickness. The accuracy of the data presented in Tables 8 and 9, and therefore the conclusions drawn, are supported by field experiments [11,72].…”

“…A variety of geographic, regulatory [30], and other factors [31][32][33] influence the use of UAVs. At the same time, the introduction of UAVs in agriculture should be accompanied by a thorough analysis of the technical and theoretical aspects of agricultural technology [34][35][36][37], taking into account both the degree of implementation and the areas of application [38][39][40]. The importance of these studies for Russia arises from the elimination of technical services and training farms in agricultural research institutes, similar to the American Extension Service, during perestroika.…”

Evaluation of Mosaic Image Quality and Analysis of Influencing Factors Based on UAVs

“…In a similar vein, Faiçal et al [12] integrated ground sensors with UAV spraying, dynamically adjusting flight parameters based on measured environ mental parameters to achieve more uniform pesticide spraying. Song et al [13] optimized the distribution of fertilizer particle deposition under different flight altitudes and speeds of multi-rotor UAVs during fertilization, ensuring a reasonable and effective deposition amount during actual fertilization processes. Furthermore, Gu et al [14] explored the in fluence of different flight parameters on point cloud data quality, revealing a positive cor relation between the root mean square error of the airborne lidar flight trajectory and fligh altitude and speed.…”

Advanced Technologies and Artificial Intelligence in Agriculture

“…They can perform tasks such as soil analysis [30][31][32], seedling density tracking [33,34], weed and pest detection and classification [35][36][37][38], yield prediction [39][40][41], and harvest readiness assessment. In some rare cases, UAVs can be used for harvesting, precision fertilization [42][43][44], pesticide injections [45][46][47], or even mechanical pest destruction. The Internet of Things and sensors provide farmers with real-time information on soil parameters, temperature, gases in the air, weather conditions, and many other parameters, which are often passed to cloud infrastructures and can then be used for analysis and prediction [48][49][50][51].…”