Full
Outline
In order to improve the online yield measurement accuracy of grain combine harvester and suppress the interference of body tilting and mechanical vibration during the yield measurement process, a pressure-type grain yield measurement system based on tilting angle and vibration compensation strategy was developed. The system consisted of a pressure-type grain yield measurement device, a GNSS positioning module, a data processor and a host computer. Firstly, a mathematical model of grain yield monitoring was constructed based on the functional relationship between grain yield and sensor pressure, inclination, vibration and humidity, and a test rig for grain yield monitoring was built to optimize the structural parameters of the device through orthogonal test combined with response surface analysis. The effects of the number of pressure sensors, the mounting position of the vibration absorber and the inclination angle of the device on the measurement error were investigated, and the optimal parameter combinations were determined to be five pressure sensors, the 3rd reference position (175 mm from the left slot wall) for the vibration absorber and the 21° inclination angle of the device, respectively. The results of univariate (flow rate change) and multivariate (flow rate, inclination angle and vibration change) tests showed that the yield measurement error of 20 groups of tests under different working conditions was stably controlled within 3%; the results of field tests showed that the average yield measurement error in actual operation was 3.41%, with a maximum of no more than 4%, which verified that the system can effectively inhibit the interference of the tilting of the vehicle body and the vibration of the machinery on the accuracy of the measurement of the yield and provide an effective technical support to obtain the information of the distribution of the yield of the precision agriculture. The system can effectively suppress the interference of body tilt and mechanical vibration on yield measurement accuracy, and provide effective technical support for the acquisition of precise agricultural yield distribution information.
203
97
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
