Publications

Development and evaluation of precision liquid pollinator for kiwifruit

Published in Journal 1, 2023

Cultivation of kiwifruit heavily relies on assisted pollination, and sufficient pollination can enhance fruit quality. A precision liquid pollinator for kiwifruit robotic pollination was developed as part of the research, and it was optimized with the aim of improving pollination quality while saving pollen usage. The pollinator employs a grating ruler to measure the stroke of a pneumatic hydraulic cylinder to achieve precise control of pollen suspension dosage, and uses an internal mixing air-assisted nozzles to spray pollen suspension for pollination. Through experiments of optimizing pollen suspension volume control, constructing of pollination distance prediction model, and measuring droplet size distribution, a working range of air pressure and liquid pressure was established. Eight sets of air–liquid pressure parameters were selected to evaluate the planar pollen deposition of the pollination device, and the optimal parameters were applied to pollinate kiwifruit flowers. The results indicated that reducing droplet size enhanced the overall pollen deposition rate. In a windless laboratory environment, the developed pollinator achieved a pollen deposition efficiency of 21.15% under an air pressure of 0.15 MPa and liquid pressure of 0.25 MPa. This precision liquid pollination device is advantageous for precise control under high pressure and low flow conditions, suitable for the end effector of autonomous pollination robots. The optimization and evaluation methods established within this study provide reference for the development of precision pollination devices.

Recommended citation: Hao, Wei, Xinting Ding, Zhi He, Kai Li, Weixin Gong, Zixu Li, Zhen Yang, and Congjie Cui. "Development and evaluation of precision liquid pollinator for kiwifruit." Computers and Electronics in Agriculture 213 (2023): 108193. (Q1 Top) http://nwafu-davyhao.github.io/files/paper1.pdf

Kiwifruit Harvesting Damage Analysis and Verification

Published in Journal 1, 2023

In order to reduce the mechanical damage during the kiwifruit picking process, the fruit rate of the picked fruit should be improved. The mechanical properties of the epidermis and interior of the fruit during the harvesting process were studied, so as to analyze the damage principle of the fruit. Firstly, a three-dimensional model of kiwifruit was constructed by point cloud scanning, and the flesh and placenta were filled in order to become a complete kiwifruit model. The elastic modulus, failure stress, and density of the kiwifruit skin, flesh, and placenta were obtained experimentally, and the material properties of the kiwifruit model were endowed with properties. Secondly, the finite element method was used to analyze the epidermis and internal stress of the kiwifruit by simulating the two processes of grabbing kiwifruit and picking to fruit boxes. The results show that the relative error of the simulation and test of the simulated grasping of kiwifruit was 6.42%, and the simulation and test of picking to fruit box confirmed the existence of damage, and the reflectivity of the damaged point in the detection was 6.18% on average, and the hardness value decreased to 8.30 kg/cm2 on average. The results from this study can provide a reference for control strategies and damage avoidance during grasping.

Recommended citation: Li, Zixu, Zhi He, Wei Hao, Kai Li, Xinting Ding, and Yongjie Cui. "Kiwifruit Harvesting Damage Analysis and Verification." Processes 11, no. 2 (2023): 598. http://nwafu-davyhao.github.io/files/paper3.pdf

Calibration of simulation parameters of tea seed based on RSM and GA-BP-GA optimization

Published in Journal 1, 2023

In the study of production and processing technologies such as mechanical shelling, sowing and planting of Camellia oleifera seeds, due to the lack of accurate discrete element simulation models and parameters, the simulation and actual errors of design equipment are large. Reverse engineering techniques were used to establish a discrete element model of Camellia oleifera seeds in EDEM software. 〖JP2〗Through physical tests, the angle of repose (AOR) of Camellia oleifera seeds was measured to be (27.93±1.46)°. The parameter intervals of density, collision recovery coefficient and static friction coefficient between camellia seed and plate were measured. The discrete model parameters of Camellia oleifera seeds were filtered by using the Plackett-Burman Design to obtain the parameters that had a significant impact on the AOR. The path of steepest ascent method was carried out to determine the optimal value range of the parameters. The central composite design (CCD) response surface method (RSM) and machine learning were used to establish the regression models involving the AOR and the significant parameters. The results showed that the predictive ability and stability of BP artificial neural network based on genetic algorithm (GA) were better than that of random forest, support vector regression and BP artificial neural network. GA optimization was used to obtain the static friction coefficient between seeds, which was 0.443, the static friction coefficient between seeds and steel plates was 0.319, and the rolling friction coefficient between seeds was 0.063. The simulated AOR was measured to be 27.63°, and the relative error from the actual AOR was 1.09%. RSM optimization was used to obtain the static friction coefficient between seeds, which was 0.383, the static friction coefficient between seeds and steel plates was 0.335, and the rolling friction coefficient between seeds was 0.064. The simulated AOR was measured to be 26.99°, and the relative error from the actual AOR was 3.33%. The results showed that GA-BP-GA had better parameter optimization effect than RSM in the parameter calibration of Camellia oleifera seeds. Moreover, the built model and parameter calibration results of Camellia oleifera seeds can be used for discrete element simulation research.

Recommended citation: Ding Xinting, Li Kai, Hao Wei, Yang Qichang, Yan Fengxin, Cui Yongjie*. 2023. Calibration of simulation parameters of tea seed based on RSM and GA-BP-GA optimization. Journal of Agricultural Machinery, 54(2):139-150. http://nwafu-davyhao.github.io/files/paper4.pdf

Development of a combined orchard harvesting robot navigation system

Published in Journal 1, 2022

Our research concerned the development of an autonomous robotic navigation system for orchard harvesting with a dual master-slave mode, the autonomous navigation tractor orchard transport robot being the master followed by a navigation orchard picking robot as the slave. This addresses the problem that in single master-slave navigation mode, agricultural combined harvesting equipment cannot stop repeatedly between rows of apple trees and drive continuously when turning. According to distances obtained from a global positioning system (GNSS), ground points were used to switch the navigation mode of the transport and picking robot. A cloth simulation filter (CSF) and random sample consensus (RANSAC) algorithm was used to obtain inter-row waypoints. The GNSS point was manually selected as the turn waypoint of the master and a kinematic model was used to compute the turn waypoints of the slave. Finally, we used a pure pursuit algorithm to track these waypoints sequentially to achieve master-slave navigation and ground head master-slave command navigation. The experimental results show that the data packet loss rate was less than 1.2% when the robot communicated in the orchard row within 50 m which meets the robot orchard communication requirements. The master-slave robot can achieve repeated stops in the row using follow navigation, which meets the demands of joint orchard harvesting. The maximum, minimum, mean and standard deviation of position deviation of the master robot were 5.3 cm, 0.8 cm, 2.4 cm, and 0.9 cm, respectively. The position deviations of the slave robot were larger than those of the master robot, with maximum, minimum, mean and standard deviation of 39.7 cm, 1.1 cm, 4.1 cm, and 5.6 cm, respectively. The maximum, minimum, mean and standard deviation of the following error between the master-slave robot were 4.4 cm, 0 cm, 1.3 cm, and 1 cm respectively. Concerning the ground head turn, the command navigation method allowed continuous turning, but the lateral deviation between robots was more than 0.3 m and less than 1 m, and the heading deviation was more than 10◦ and less than 90◦.

Recommended citation: Mao, Wenju, Heng Liu, Wei Hao, Fuzeng Yang, and Zhijie Liu. "Development of a combined orchard harvesting robot navigation system." Remote Sensing 14, no. 3 (2022): 675. http://nwafu-davyhao.github.io/files/paper2.pdf