Spray drift has always been a focus research area in the field of unmanned aerial vehicle (UAV) application. Under the fixed premises of UAV operating parameters, such as height, speed and spraying liquid, the droplet drift is mainly affected by meteorological conditions. In this research, the spray drift and deposition tests were conducted using a QuanFeng120 UAV in a pineapple field under various different meteorological conditions. The experimental results showed that with the changes of UAV operating height and wind speed, the start position of the in-swath deposition area changed 4 m in the extreme situation. The percentage of the total spray drift was from 15.42% to 55.76%. The position of cumulative spray drift that accounted for 90% of the total spray drift was from 3.70 m to 46.50 m relative to the flight line. According to the downwind spray drift curve, the nonlinear equations of the same type under the four operating conditions of the UAV were fitted. The spray drift and the deposition of UAV application were significantly affected by different meteorological conditions and UAV operating heights. The results could provide a theoretical basis for UAV spraying in pineapple plants and support for spray drift control and prediction.

Background The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI. Results In this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with $$R^2$$ R 2 and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with $$R^2$$ R 2 and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the $$R^2$$ R 2 and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively. Conclusions Dense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density.

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Remote-controlled (RC) unmanned aerial vehicles (UAVs) have been extensively applied in agricultural areas, such as remote sensing, precise spraying pesticides for crop protection, agricultural situation inspection and so on, but these telemanipulated UAVs systems are operated entirely by a ground-based pilot with a need of eyes focus on the remote site UAV flight. The key issue existed in agricultural UAV teleoperation area is a longtime training needed. In this paper, a novel natural UAV teleoperation control system in agricultural application was proposed. In UAV teleoperation scenario, human operator gestures measured by using Kinect sensor can be used as control commands for UAV flight. Moreover, some UAV teleoperation control commands related to human hand gestures were defined, which is similar to the radio gymnastic exercises in China. Therefore, gesture recognition-based UAV teleoperation control is easy to learn and master. In addition, a new real time human hand gesture recognition algorithm was proposed. The stability of UAV flight dynamic of roll, pitch and yaw as well as attitude control were verified with the experiments based on the proposed method. At last, the usability and effectiveness of the proposed method has been verified by the experimental results.

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