Yongkui Jin

We propose a hybrid algorithm based on the nested genetic algorithm (GA) and integer particle swarm optimization (PSO) for multiple unmanned aerial systems (multi-UASs) plant protection operations in multiple segmented fields, with multibases loading pesticides and power. In the proposed algorithm, a preprocessing phase is adopted that includes segmented fields splitting and merging, to shrink the operation field number (i.e., multi-UASs total sorties) and ensure that each generated field area remains smaller than (but close to) the unmanned aerial system (UAS) maximum single-sortie operation area. An improved one-sortie coverage path generation model is established, avoiding power waste in the case when UAS needs to operate on more than one subfield in one sortie. The integer PSO is then used to optimize the initial assignment for UASs on multibases, with the purpose of reducing total operation time and nonoperation flight distance of overall coverage path planning. The proposed algorithm solves the problems of how to design pesticide spraying coverage path on segmented agricultural fields with several bases, and allocate to multiple plant protection UASs with constrained loading capacity. Computer simulations are provided to validate the effectiveness of our algorithms and the performance compared with other algorithms. The test results show that the total number of flight sorties using nested GA (53) is smaller than that using some representative metaheuristic algorithms (57), density-based spatial clustering of applications with noise (60), and traditional planning methods (83). Both nonoperation distance and total operation time obtained by nest GA and integer PSO are smaller than those using other planning methods. The appearance of multibases loading pesticide and power, would reduce the nonspraying flight energy and operation time wastage of UASs.

The effective coverage and velocity of downwash are directly related to the assemblage of spraying system and spraying effect. The downwash of the unmanned agricultural helicopter (UAH) N-3 was discussed in the paper. The computational fluid dynamics (CFD) methods were used to simulate and analyze the distribution of the downwash, and a wind field measurement device had been designed to test the downwash of UAH N-3. In the tests, the UAH N-3 was raised up to 5.0 m, 6.0 m and 7.0 m from the ground, “annular-radial-distribution- point” method was introduced, 8 directions separated by an angle of 45° (the radial direction) with the intersection point of the main rotor shaft and the ground plane as the center, 0.5 m as the step length for the longitudinal (to 2.5 m) and radial (to 4.0 m) direction to set the sample points, considering the range of the rotor rotating circular area mainly. The 5 m height results of N-3 were fully discussed to describe the downwash distribution with the longitudinal altitude increased and the radial distance increased. The standard deviations of five test altitudes for eight directions were comparatively analyzed, the results showed that the total standard deviation was not greater than 0.6 m/s. The overall relative maximum margin of error calculated from the simulation and measurement data was between 0.6 and 0.7, which verified the credibility of the simulation data. High-order polynomials were used to fitting the simulation and measurement data, the fitting results showed that the polynomial coefficient of determination R2 met or exceeded 0.75 when the altitudes were more than 1 m, indicating the fit equation having the reference values. When the altitudes equal or less than 0.5 m, the polynomial coefficient of determination R2 was smaller, ranging during 0.3 to 0.7. The study would provide some foundations for the optimization of the assemblage of spraying system on the single-rotor UAH, which would promote China aviation plant protection. Keywords: unmanned agricultural helicopter, single rotor, CFD simulation, downwash distribution, spraying effect DOI: 10.25165/j.ijabe.20171005.3079 Citation: Zhang S C, Xue X Y, Sun Z, Zhou L X, Jin Y K. Downwash distribution of single-rotor unmanned agricultural helicopter on hovering state. Int J Agric & Biol Eng, 2017; 10(5): 14–24.

Accurate crop row detection is an important foundation for agricultural machinery to realize autonomous operation. Existing methods often compromise between real-time performance and detection accuracy, limiting their practical field applicability. This study develops a high-precision, efficient crop row detection algorithm specifically optimized for soybean–corn compound planting conditions, addressing both computational efficiency and recognition accuracy. In this paper, a real-time soybean–corn crop row detection method based on GD-YOLOv10n-seg with principal component analysis (PCA) fitting was proposed. Firstly, the dataset of soybean–corn seedling crop rows was established, and the images were labeled with line labels. Then, an improved model GD-YOLOv10n-seg model was constructed by integrating GhostModule and DynamicConv into the YOLOv10n-segmentation model. The experimental results showed that the improved model performed better in MPA and MIoU, and the model size was reduced by 18.3%. The crop row center lines of the segmentation results were fitted by PCA, where the fitting accuracy reached 95.08%, the angle deviation was 1.75°, and the overall processing speed was 61.47 FPS. This study can provide an efficient and reliable solution for agricultural autonomous navigation operations such as weeding and pesticide application under a soybean–corn compound planting mode.