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Plant height is regarded as a key indicator that is crucial for assessing the crop growth status and predicting yield. In this study, an advanced method based on solid-state LiDAR technology is proposed, which is specifically designed to accurately capture the phenotypic characteristics of plant height during the maize growth cycle. By segmenting the scanned point cloud of maize, detailed point cloud data of a single maize plant were successfully extracted, from which stem information was accurately measured to obtain accurate plant height information. In this study, we will concentrate on the analysis of individual maize plants. Leveraging the advantages of solid-state LiDAR technology in precisely capturing phenotypic information, the data processing approach for individual maize plants, as compared to an entire maize community, will better restore the maize's original growth patterns. This will enable the acquisition of more accurate maize plant height information and more clearly demonstrate the potential of solid-state LiDAR in capturing detailed phenotypic information. To enhance the universality of the research findings, this study meticulously selected key growth stages of maize for data validation and comparison, encompassing the tasseling, silking, and maturity phases. At these crucial stages, 20 maize plants at the tasseling stage, 40 at the flowering stage, and 40 at the maturity stage were randomly selected, totaling 100 samples for analysis. Each sample not only included actual measurement values but also included plant height information extracted using point cloud technology. The observation period was set from 20 June to 20 September 2021. This period encompasses the three key growth stages of maize described above, and each growth stage included one round of data collection, with three rounds of data collection each, each spaced about a week apart, for a total of nine data collections. To ensure the accuracy and reliability of the data, all collections were performed at noon when the natural wind speed was controlled within the range of 0 to 1.5 m/s and the weather was clear. The findings demonstrate that the root mean square error (RMSE) of the maize plant height data, procured through LiDAR technology, stands at 1.27 cm, the mean absolute percentage error (MAPE) hovers around 0.77%, and the peak R2 value attained is 0.99. These metrics collectively attest to the method's ongoing high efficiency and precision in capturing the plant height information. In the comparative study of different stem growth stages, especially at the maturity stage, the MAPE of the plant height was reduced to 0.57%, which is a significant improvement compared to the performance at the nodulation and sprouting stage. These results effectively demonstrate that the maize phenotypic information extraction method based on solid-state LiDAR technology is not only highly accurate and effective but is also effective on individual plants, which provides a reliable reference for applying the technique to a wider range of plant populations and extending it to the whole farmland. [ABSTRACT FROM AUTHOR]

Wind field is one of the important factors affecting the distribution characteristics of aerial spraying droplet deposition. In order to reveal the impact mechanism of droplet deposition distribution by the wind field below agricultural unmanned helicopter rotor, in this study, the wind field distribution below uniaxial single-rotor electric unmanned helicopter rotor was measured by using a wireless wind speed sensor network measurement system for unmanned helicopter. The effects of wind field in three directions (X, Y, Z) below the rotor on droplet deposition distribution were analyzed with the condition of aerial spraying droplet deposition in rice canopy, and the regression model was established via variance and regression analyses of experiment results. The results showed that, the wind field in Y direction had a significant effect on droplet deposition in effective spray area, the wind field in Z direction had an extremely significant effect on droplet deposition in effective spray area, and the corresponding significance (sig.) values were 0.011 and 0.000. Furthermore, the wind field in Z direction had a significant effect on the penetrability and uniformity of droplet deposition in effective spray area, the corresponding sig. values were 0.025 and 0.011 respectively. The wind speed in Y direction at the edge of effective spray area had a significant effect on droplet drift, the sig. value was 0.021. In addition, the correlation coefficient R of the regression model was 0.869 between droplet deposition in effective spray area and the wind speed in Y and Z directions, and 0.915 between the uniformity of droplet deposition in effective spray area and the maximum wind speed in Z direction. The result revealed the influencing mechanism of the wind field below the rotor of uniaxial single-rotor electric unmanned helicopter on the distribution of aerial spraying droplet deposition. The results can provide guidance for the actual production application of aerial spraying to reduce liquid drift and improve the utilization rate of pesticide. [ABSTRACT FROM AUTHOR]

Modern technologies and information tools can be used to maximize agricultural aviation productivity allowing for precision application of agrochemical products. This paper reviews and summarizes the state-of-the-art in precision agricultural aviation technology highlighting remote sensing, aerial spraying and ground verification technologies. Further, the authors forecast the future of precision agricultural aviation technology with key development directions in precision agricultural aviation technologies, such as real-time image processing, variable-rate spraying, multi-sensor data fusion and RTK differential positioning, and other supporting technologies for UAV-based aerial spraying. This review is expected to provide references for peers by summarizing the history and achievements, and encourage further development of precision agricultural aviation technologies. [ABSTRACT FROM AUTHOR]

The wind field produced by the rotor-wing UAV has a significant impact on the distribution of rice pollen, which directly influences hybrid rice breeding. This research aimed to explore the distribution law of rice pollen in the wind field of small UAV. Aviation Beidou Positioning System UB351 positions all sampling nodes for precise corresponding coordinates and spacing information, and draws UVA's flying trajectories, thus providing accurate data for tests. Wireless sensor network measurement system was used to study the three-direction wind field produced by the rotor-wing UAV under various experimental factors and acquires wind field width and wind speed, and the data were compared with the area ratio and width of pollen distribution. Test of univariate normality was conducted through Shapiro-Wilk test and Kolmogorov-Smimov test. In order to figure out the appropriate flight speed for UAV's pollination under pollen distribution law, it is also necessary to perform analysis of variances on regression model. The comparison of wind speed in longitudinal (X) and lateral (Y) direction show that as the major force of the horizontal wind field produced by the rotor-wing UAV, the wind from Y-direction forms the widest wind field. Moreover, flight speed mainly influences wind field width. To be specific, the width of horizontal wind field decreases as flight speed increases. Meanwhile, UAV flight speed also exerts significant impact on vertical wind field. Both the pollen distribution width of more than 5 pollen grains and the area ratio reached the maximum when the UAV flight was at 4.53 m/s, which was the most favorable speed to pollination. In addition, pollen quantity is closely associated with both horizontal and vertical wind field. With comparison of the pollen quantity of sampling nodes, it was found that the wind field produced by the rotor-wing UAV exerted asymmetrical impact on pollen distribution. Q-Q plot of SPSS verifies that pollen distribution is against normal distribution. The establishment of a multiple linear regression model of pollen distribution and wind speed in three directions indicates that pollen distribution quantity only shares positive linear relation with the wind field in X-direction. These findings provide a theoretical guidance for rice pollination by using agricultural UAVs. [ABSTRACT FROM AUTHOR]