Unmanned farms employ a variety of sensors, automated systems, and data analysis techniques to enable fully automated and intelligent management. This not only heightens agricultural production efficiency but also reduces the costs associated with human resources. As integral components of unmanned farms’ automation systems, agricultural UAVs have been widely adopted across various operational stages due to their precision, high efficiency, environmental sustainability, and simplicity of operation. However, present-day technological advancement levels and relevant policy regulations pose significant restrictions on UAVs in terms of payload and endurance, leading to diminished task efficiency when a single UAV is deployed over large areas. Accordingly, this paper aggregates and analyzes research pertaining to UAV swarms from databases such as Google Scholar, ScienceDirect, Scopus, IEEE Xplorer, and Wiley over the past decade. An initial overview presents the current control methods for UAV swarms, incorporating a summary and analysis of the features, merits, and drawbacks of diverse control techniques. Subsequently, drawing from the four main stages of agricultural production (cultivation, planting, management, and harvesting), we evaluate the application of UAV swarms in each stage and provide an overview of the most advanced UAV swarm technologies utilized therein. Finally, we scrutinize and analyze the challenges and concerns associated with UAV swarm applications on unmanned farms and provide forward-looking insights into the future developmental trajectory of UAV swarm technology in unmanned farming, with the objective of bolstering swarm performance, scalability, and adoption rates in such settings.

In order to realize the high-quality row seeding operation with unmanned aerial vehicle (UAV) in paddy field, a shot seeding device that can sow five rows of pelleted rice seeds at the same time was designed. The shot seeding device mainly consists of an external grooved wheel seed metering device and five shot seeding modules. The designed external grooved wheel seed metering device can take the seeds out of the seed box and divide the seeds into five parts. The shot seeding module can accelerate the pelleted rice seeds to reduce the impact of the UAV wind on the direction of seed movement. Furthermore, an angle adjustment mechanism for the shot seeding module was used to change the row spacing. The seed metering device test verified that when the speed of the seed metering wheel was 15 r/min, the coefficient of variation of the discharge rate of each row (C.V1) and total seed discharge rate stability (C.V2) were 1.70% and 1.04%, respectively. Image processing technique was used to test the UAV seeding performance. The distribution characteristics of seeds on the ground showed that the number of seeds in each row gradually increased from both sides to the middle in the width direction. According to the statistics, there were 60%-70% of the seeds in each row in the 100 mm width range. The field test showed that when the working height was 1.5 m and the seeding quantity was 38.56 kg/hm2, the performance of sowing in rows was obvious, the deviation rate of seeding quantity was 1.89%. After 16 d of sowing, the seeds’ emergence rate was stable, and the average emergence rate was 82.63% and the yield was 6775.50 kg/hm2. Keywords: unmanned aerial vehicle, rice direct seeding, row seeding, pelleted rice seeds DOI: 10.25165/j.ijabe.20221506.7301 Citation: Liu W, Zou S S, Xu X L, Gu Q Y, He W Z, Huang J, et al. Development of UAV-based shot seeding device for rice planting. Int J Agric & Biol Eng, 2022; 15(6): 1–7.

Introduction: As a vital component of the ecosystem of the Qinghai-Tibet Plateau, alpine wetlands coexist with their vulnerability, sensitivity, and abundant biodiversity, propelling the material cycle and energy flux of the entire plateau ecosystem. In recent decades, climate change and human activities have significantly altered the regional landscape. Monitoring and assessing changes in the alpine wetlands on the Qinghai-Tibet Plateau requires the efficient and accurate collection of long-term information.Methods: Here, we interpreted the remote sensing data of the first meander of the Yellow River of alpine wetlands from 1990 to 2020 based on Google Earth Engine (GEE) platform, using geographic information system (GIS) and landscape pattern index to analyze the spatial and temporal evolution of wetland landscape patterns, and the primary drivers of changes in wetland area were explored by GeoDetector.Results: Our result showed that most wetland areas were found in regions with gradients less than 12° and elevations between 3315 and 3600 m. From 1990 to 2010, the area of alpine wetland in the study area decreased by 25.43%. During the period between 2010 and 2020 to the 1990s, the wetland area decreased by 322.9 km². Conversion to and from grassland was the primary form of wetland transfer out and in, respectively. The overall migration of the wetland centroid in the study area was to the southwest between 1990 and 2010 and to the north between 2010 and 2020. The geometry of the wetland landscape was relatively simple, the landscape was relatively intact, and patches retained a high level of agglomeration and connectivity. However, their level of agglomeration and connectivity was disrupted. A quantitative analysis of the factor detector in GeoDetector revealed that the DEM, slope, and evaporation were the most important driving factors influencing the change of wetland area, with socioeconomic development also influencing changes in the wetland area to a lesser extent.Discussion: Using interaction detectors, it was discovered that the interaction of various driving factors could better explain the long-term variations in wetland areas, with a greater degree of explanation than that of each driving factor alone.

Unmanned aerial vehicles (UAVs) are an important part of smart farms and have been widely used in granular fertilizer spreading. The multichannel pneumatic granular fertilizer spreader (MPGFS) has the advantages of light weight and precision spreading, and has been applied to UAV variable rate fertilization. Based on the problem that the airflow field disorder of the existing MPGFS reduces the uniformity of spreading, the aim of this study was to further improve the performance of the MPGFS through rectification. The computational fluid dynamics and discrete element method (CFD-DEM) and coupling simulation method were used to study the characteristics of the airflow field and fertilizer particle motion, and a honeycomb rectifier and grid rectifier were developed. The aperture of the honeycomb rectifier and the grid size of the grid rectifier were optimized. Then, the test bench was built to test the consistency of the discharge rate of each channel and the spreading uniformity of the MPGFS. The simulation results of the existing MPGFS showed that the airflow provided by the axial flow fan was rotational, and this caused the particles’ motion to be skewed in the shrinkage section, so the discharge rate of each channel was inconsistent. The airflow field analysis results of the shrinkage section showed that the airflow rotation was reduced after the rectification of the honeycomb rectifier and the grid rectifier. The bench test results showed that the coefficient of variation (CV) of each channel discharge rate of the existing MPGFS was 20.16%, the optimal honeycomb rectifier was 13.07%, and the optimal grid rectifier was 5.27%. The bench test results of spreading uniformity show that the CV of spreading uniformity of the existing MPGFS was 15.32%, the optimal honeycomb rectifier was 15.81%, and the optimal grid rectifier was 8.02%. The grid rectifier spread pattern was more reasonable and the CV of uniformity was better. This study demonstrated that the use of a grid rectifier to rectify the airflow field of MPGFS can effectively improve its spreading uniformity, which was of guiding significance for the design and research of MPGFS.