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Obtaining canopy area, crown width, position, and other information from UAV aerial images and adjusting spray parameters in real-time according to this information is an important way to achieve precise pesticide application in orchards. However, the natural illumination environment in the orchard makes extracting the fruit tree canopy difficult. Hereto, an effective unsupervised image segmentation method is developed in this paper for fast fruit tree canopy acquisition from UAV images under natural illumination conditions. Firstly, the image is preprocessed using the shadow region luminance compensation method (SRLCM) that is proposed in this paper to reduce the interference of shadow areas. Then, use Naive Bayes to obtain multiple high-quality color features from 10 color models was combined with ensemble clustering to complete image segmentation. The segmentation experiments were performed on the collected apple tree images. The results show that the proposed method’s average precision rate, recall rate, and F1-score are 95.30%, 84.45%, and 89.53%, respectively, and the segmentation quality is significantly better than ordinary K-means and GMM algorithms.

The accurate setting of input parameters in the numerical simulation of downwash airflow from a UAV sprayer is important for acceptable simulation results. To provide real data of simulation parameters (rotor speed and pitch angle) for the numerical simulation of downwash airflow, a wireless simulation parameter measurement system (WSPM-System) was designed and tested in this study. The system consists of hardware and software designed based on Arduino and LabVIEW, respectively. Wireless communication was realized by nRF24L01. The lattice Boltzmann method (LBM) was applied for the numerical simulation of downwash airflow. The results showed that the valid communication distance of the WSPM-System was 100 m, with a packet loss rate of less than 1%. While hovering, the rotor speed dropped by about 30% when the load of the UAV sprayer changed from 16 kg to 4 kg, which resulted in the maximum vertical downward velocity (VVD) on the horizontal detection surface dropping by about 23%. Under forward flight, the rotor speed in the front (n1, n6) and rear (n3, n4) of the UAV sprayer, respectively, showed a negative linear correlation and positive linear correlation with flight speed (R2 > 0.95). Meanwhile, the rotor speed in the middle (n2, n5) was consistent with the rotor speed while hovering under the same load; the pitch angle showed a positive linear correlation with flight speed (R2 > 0.94). A correlation analysis of measured and simulated values of the VVD revealed that the numerical simulation of downwash airflow with the parameters provided by the WSPM-System was reliable (R2 = 0.91). This study confirmed that the input value of the rotor speed in the fluid software needed to be determined according to the application parameters of the UAV sprayer, thus providing a feasible method and system for obtaining real simulation parameters.

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