Browsing by Person "Wang, Shubo"

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    Development of multifunctional unmanned aerial vehicles versus ground seeding and outplanting: What is more effective for improving the growth and quality of rice culture?
    (2022) Qi, Peng; Wang, Zhichong; Wang, Changling; Xu, Lin; Jia, Xiaoming; Zhang, Yang; Wang, Shubo; Han, Leng; Li, Tian; Chen, Bo; Li, Chunyu; Mei, Changjun; Pan, Yayun; Zhang, Wei; Müller, Joachim; Liu, Yajia; He, Xiongkui
    The agronomic processes are complex in rice production. The mechanization efficiency is low in seeding, fertilization, and pesticide application, which is labor-intensive and time-consuming. Currently, many kinds of research focus on the single operation of UAVs on rice, but there is a paucity of comprehensive applications for the whole process of seeding, fertilization, and pesticide application. Based on the previous research synthetically, a multifunctional unmanned aerial vehicle (mUAV) was designed for rice planting management based on the intelligent operation platform, which realized three functions of seeding, fertilizer spreading, and pesticide application on the same flight platform. Computational fluid dynamics (CFD) simulations were used for machine design. Field trials were used to measure operating parameters. Finally, a comparative experimental analysis of the whole process was conducted by comparing the cultivation patterns of mUAV seeding (T1) with mechanical rice direct seeder (T2), and mechanical rice transplanter (T3). The comprehensive benefit of different rice management processes was evaluated. The results showed that the downwash wind field of the mUAV fluctuated widely from 0 to 1.5 m, with the spreading height of 2.5 m, and the pesticide application height of 3 m, which meet the operational requirements. There was no significant difference in yield between T1, T2, and T3 test areas, while the differences in operational efficiency and input labor costs were large. In the sowing stage, T1 had obvious advantages since the working efficiency was 2.2 times higher than T2, and the labor cost was reduced by 68.5%. The advantages were more obvious compared to T3, the working efficiency was 4 times higher than in T3, and the labor cost was reduced by 82.5%. During the pesticide application, T1 still had an advantage, but it was not a significant increase in advantage relative to the seeding stage, in which operating efficiency increased by 1.3 times and labor costs were reduced by 25%. However, the fertilization of T1 was not advantageous due to load and other limitations. Compared to T2 and T3, operational efficiency was reduced by 80% and labor costs increased by 14.3%. It is hoped that this research will provide new equipment for rice cultivation patterns in different environments, while improving rice mechanization, reducing labor inputs, and lowering costs.
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    Method of 3D voxel prescription map construction in digital orchard management based on LiDAR-RTK boarded on a UGV
    (2023) Han, Leng; Wang, Shubo; Wang, Zhichong; Jin, Liujian; He, Xiongkui
    Precision application of pesticides based on tree canopy characteristics such as tree height is more environmentally friendly and healthier for humans. Offline prescription maps can be used to achieve precise pesticide application at low cost. To obtain a complete point cloud with detailed tree canopy information in orchards, a LiDAR-RTK fusion information acquisition system was developed on an all-terrain vehicle (ATV) with an autonomous driving system. The point cloud was transformed into a geographic coordinate system for registration, and the Random sample consensus (RANSAC) was used to segment it into ground and canopy. A 3D voxel prescription map with a unit size of 0.25 m was constructed from the tree canopy point cloud. The height of 20 trees was geometrically measured to evaluate the accuracy of the voxel prescription map. The results showed that the RMSE between tree height calculated from the LiDAR obtained point cloud and the actual measured tree height was 0.42 m, the relative RMSE (rRMSE) was 10.86%, and the mean of absolute percentage error (MAPE) was 8.16%. The developed LiDAR-RTK fusion acquisition system can generate 3D prescription maps that meet the requirements of precision pesticide application. The information acquisition system of developed LiDAR-RTK fusion could construct 3D prescription maps autonomously that match the application requirements in digital orchard management.