Unmanned aerial vehicle (UAV)-based spraying systems have recently become important for the precision application of pesticides, using machine learning approaches. Therefore, the objective of this research was to develop a machine learning system that has the advantages of high computational speed and good accuracy for recognizing spray and non-spray areas for UAV-based sprayers. A machine learning system was developed by using the mutual subspace method (MSM) for images collected from a UAV. Two target lands: agricultural croplands and orchard areas, were considered in building two classifiers for distinguishing spray and non-spray areas. The field experiments were conducted in target areas to train and test the system by using a commercial UAV (DJI Phantom 3 Pro) with an onboard 4K camera. The images were collected from low (5 m) and high (15 m) altitudes for croplands and orchards, respectively. The recognition system was divided into offline and online systems. In the offline recognition system, 74.4% accuracy was obtained for the classifiers in recognizing spray and non-spray areas for croplands. In the case of orchards, the average classifier recognition accuracy of spray and non-spray areas was 77%. On the other hand, the online recognition system performance had an average accuracy of 65.1% for croplands, and 75.1% for orchards. The computational time for the online recognition system was minimal, with an average of 0.0031 s for classifier recognition. The developed machine learning system had an average recognition accuracy of 70%, which can be implemented in an autonomous UAV spray system for recognizing spray and non-spray areas for real-time applications.

The aim of this study was to design a navigation system composed of a human-controlled leader vehicle and a follower vehicle. The follower vehicle automatically tracks the leader vehicle. With such a system, a human driver can control two vehicles efficiently in agricultural operations. The tracking system was developed for the leader and the follower vehicle, and control of the follower was performed using a camera vision system. A stable and accurate monocular vision-based sensing system was designed, consisting of a camera and rectangular markers. Noise in the data acquisition was reduced by using the least-squares method. A feedback control algorithm was used to allow the follower vehicle to track the trajectory of the leader vehicle. A proportional–integral–derivative (PID) controller was introduced to maintain the required distance between the leader and the follower vehicle. Field experiments were conducted to evaluate the sensing and tracking performances of the leader-follower system while the leader vehicle was driven at an average speed of 0.3 m/s. In the case of linear trajectory tracking, the RMS errors were 6.5 cm, 8.9 cm and 16.4 cm for straight, turning and zigzag paths, respectively. Again, for parallel trajectory tracking, the root mean square (RMS) errors were found to be 7.1 cm, 14.6 cm and 14.0 cm for straight, turning and zigzag paths, respectively. The navigation performances indicated that the autonomous follower vehicle was able to follow the leader vehicle, and the tracking accuracy was found to be satisfactory. Therefore, the developed leader-follower system can be implemented for the harvesting of grains, using a combine as the leader and an unloader as the autonomous follower vehicle.

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Background: High-intensity exercise consumes a large amount of energy and tends to induce post-exercise fatigue. Promoting physical and psychological recovery after exercise can enable individuals to perform better in subsequent training or competitions and reduce the risk of injury. This study aims to investigate the effects of post-exercise recovery methods on exercise-induced hormones and blood fatigue factors.Methods: PubMed, Embase and Web of Science databases were queried to collect literature on the correlation between post-exercise recovery methods and the expression of exercise-induced hormones and blood fatigue factors. The search time ranged between inception to July 2020. Stata (version 15.0) was used for meta-analysis.Results: A total of 10 studies were included, involving the data of 278 cases. Among these, 148 people were placed in the study group and assigned active post-exercise recovery measures while 130 people were placed in the control group and assigned no post-exercise recovery measures. The results of this meta-analysis showed that there was significant difference between the study group and the control group [relative risk (RR) =15.62, 95% confidence interval (CI): 3.25, 75.06, P<0.05]. The subgroup analysis on the effect of active and passive recovery on the blood lactate concentration (BLC) and creatine kinase (CK) concentration revealed that the CK concentration [standardized mean difference (SMD) =−0.76, 95% CI: −1.47, −0.04] and BLC (SMD =−1.16, 95% CI: −2.30, −0.02) were significantly lower in the study group compared with the control group. Further analysis on the effect of different post-exercise recovery methods on the BLC and CK concentrations indicated that BLC (SMD =−1.16, 95% CI: −2.30, −0.02) was significantly lower in the group with cold water immersion compared with the control group, while there was no significant difference in the changes of CK concentration. Additionally, food supplementation was shown to reduce CK concentration (SMD =−1.16, 95% CI: −4.69, 2.36).Conclusions: Recovery measures after high-intensity exercise can accelerate the reduction of BLC and the activity and concentration of CK, thus helping the body quickly return to a pre-exercise state.

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 &gt; 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.