The aerial atomizer is the most essential component of the plant protection UAV (unmanned aerial vehicle). However, the structural optimization of existing aerial atomizers lacks comprehensive consideration of spray parameters and structural parameters, and there is a shortage of available atomizer spray models, resulting in the unstable effect of UAV application. In our previous work, an aerial dual-atomization centrifugal atomizer was developed. In order to obtain an aerial atomizer with good atomization effect and its atomization model, structural optimization at different rotation speeds and flow rates of the atomizer, and its atomization performance, are studied in this paper. Firstly, with the droplet volume median diameter (VMD) and spectral width (SRW) as the evaluation index, through the single-factor, Plackett–Burman and Box–Behnken tests, the influence of rotation speed, flow rate, tooth number and tooth shape were studied. The regression models of the droplet VMD and SRW were established using multiple quadratic regression fitting of the test data. Secondly, in order to achieve the lowest droplet VMD and SRW, the response surface method and post-hoc multiple comparison method were used to obtain the optimized structure of the atomizer’s rotation ring at different rotation speeds (600–7000 r/min) and flow rates (500–1000 mL/min). Lastly, with the effective swath width (ESW) of the optimized atomizer as the evaluation index, through the Box–Behnken test, the influence of rotation speed, flow rate and spray height were studied. The multiple quadratic regression model of ESW was established with the test data. The test results indicated that rotation speed, flow rate and tooth number had a significant effect on droplet VMD and SRW; tooth shape had no significant effect on the droplet VMD and SRW, however, the square tooth shape had the best atomization effect; and rotation speed, flow rate and spray height had a significant effect on ESW. The optimized structural parameters were tooth shape: square, and tooth number: 20. The determination coefficient $R^2$ of the regression model of VMD, SRW and ESW were 0.9976, 0.9770 and 0.9974, respectively, which indicates that the model was accurate, and can evaluate and predict the spray effect. This paper provides an optimized dual-atomization centrifugal atomizer, and its regression models of VMD, SRW and ESW for UAV applications can provide a reference for efficient UAV spraying.

Detecting banana pseudostems is an indispensable part of the intelligent management of banana cultivation, which can be used in settings such as counting banana pseudostems and smart fertilization. In complex environments, dense and occlusion banana pseudostems pose a significant challenge for detection. This paper proposes an improved YOLOV7 deep learning object detection algorithm, YOLOV7-FM, for detecting banana pseudostems with different growth conditions. In the loss optimization part of the YOLOV7 model, Focal loss is introduced, to optimize the problematic training for banana pseudostems that are dense and sheltered, so as to improve the recognition rate of challenging samples. In the data augmentation part of the YOLOV7 model, the Mixup data augmentation is used, to improve the model’s generalization ability for banana pseudostems with similar features to complex environments. This paper compares the AP (average precision) and inference speed of the YOLOV7-FM algorithm with YOLOX, YOLOV5, YOLOV3, and Faster R-CNN algorithms. The results show that the AP and inference speed of the YOLOV7-FM algorithm is higher than those models that are compared, with an average inference time of 8.0 ms per image containing banana pseudostems and AP of 81.45%. This improved YOLOV7-FM model can achieve fast and accurate detection of banana pseudostems.

In recent years, as an important part of precision agricultural aviation, the plant protection unmanned aerial vehicle (UAV) has been widely studied and applied worldwide, especially in East Asia. Banana, as a typical large broad-leaved crop, has high requirements for pests and diseases control. The mechanization degree of plant protection management in banana orchard is low. Therefore, our study focuses on the effects of different flight heights (3-5 m) and droplet sizes (50-150 μm) of plant protection UAV on the droplet deposition distribution characteristics of banana canopy. And the droplet deposition distribution in banana canopy and spraying drift of plant protection UAV and ground air-assisted sprayer were compared. The results showed that droplet size was the main factor affecting droplet deposition density, coverage, uniformity and penetration on both sides of banana canopy leaves. The droplet deposition density and coverage on the adaxial side of leaves were mostly significantly larger than that on the abaxial side. The flight height of 4 m and the droplet size of 100 μm could make the adaxial side of banana canopy leaves have higher droplet deposition density (63.77 droplets per square cm) and coverage (12.75%), and can make the droplets effectively deposit on the abaxial side of banana canopy leaves, with droplet deposition density of 17.46 droplets per square cm and coverage of 1.24%. Choosing an appropriate flight height and a droplet size could improve the droplet deposition uniformity on both sides of banana canopy leaves, but the improvement was not significant. Moreover, at a same operational parameter combination, it was difficult to achieve the best droplet deposition density, coverage, uniformity and penetration at the same time. In addition, appropriately increasing the flight height and droplet size could help to improve the droplet deposition penetration on the adaxial side of banana canopy leaves, but there were few significant improvements. Compared with the plant protection UAV, the ground air-assisted sprayer had higher droplet deposition density and coverage on the abaxial side of banana canopy leaves, but had smaller droplet deposition coverage on the adaxial side. The droplet deposition density and coverage on the abaxial side of banana canopy leaves were obviously larger than the adaxial side during the spraying of ground air-assisted sprayer. The droplet drift distance of the ground air-assisted sprayer was farther than the plant protection UAV. The test results of this study can provide practical and data support for the UAV aerial application in banana orchard, and provide a valuable reference for the implementation of air-ground cooperation spraying strategy in banana orchard, which is of great significance to promote sustainable and intelligent phytoprotection of banana orchard.

Potted plant canopy extraction requires a fast, accurate, stable, and affordable detection system for precise pesticide application. In this study, we propose a new method for extracting three-dimensional canopy information of potted plants using millimeter-wave radar and evaluate the system on plants in static, rotating, and rotating-while-spraying states. The position and rotation speed of the rotating platform are used to compute the rotation–translation matrix between point clouds, enabling the multi-view point clouds to be overlaid on the world coordinate system. Point cloud extraction is performed by applying the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN), while an Alpha-shape algorithm is used for three-dimensional reconstruction of the canopy. Our measurement results for the 3D reconstruction of plants at different growth stages showed that the reconstruction model has higher accuracy under the rotation condition than that under the static condition, with average relative errors of 41.61% and 10.21%, respectively. The significant correlation between the sampling data with and without spray reached 0.03, indicating that the effect of the droplets on radar detection during the spray process can be neglected. This study provides guidance for plant canopy detection using millimeter-wave radar for advanced agricultural informatization and automation.