- Chinese Core Journal
- Chinese Science Citation Database (CSCD) Source journal
- Journal of Citation Report of Chinese S&T Journals (Core Edition)
| Citation: |
DING Youchun, CHEN Liyuan, WANG Denghui, et al. Design and test of monitoring system for rapeseed sowing quality [J]. Journal of South China Agricultural University, 2021, 42(6): 43-51. DOI: 10.7671/j.issn.1001-411X.202107029
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To obtain rapeseed sowing quality information and realize information display, remote transmission and cloud storage, a rape sowing quality monitoring system was proposed.
This system consisted of rapeseed sensor detection device, seeding monitoring terminal and seeding quality information cloud storage platform. Various forms of small particle size seed sensor detection devices were used to achieve real-time acquisition of sowing quality information, and a radio frequency communication module was used to achieve data interaction with the sowing monitoring terminal. The monitoring terminal completed the information display and realized the accurate positioning of the sowing quality information position through the Beidou positioning unit. This system realized the remote transmission and cloud storage of rape sowing quality information through wireless transmission module. A bench test of the rape sowing quality information collection system was built and field performance tests were conducted to verify the stability and reliability of this system.
The designed seed quality information acquisition system could obtain seeder longitude and latitude information through the embedded Beidou positioning unit, and transmitted the seed quality information and positioning information to the cloud storage platform by 4G wireless transmission module. The bench test results showed that the detection accuracy rate was not lower than 97.1% when the seed dropping frequency of seed platter was 16.5−26.2 Hz. The collected seed quality information of rapeseed could be transmitted to the seed monitoring terminal and displayed. The seed quality information was accurately uploaded to the database of cloud storage platform, the transmission time did not exceed 2 s, and the information was consistent with the terminal display data. The field test results showed that the detection accuracy was no less than 96.6% at the seed dropping frequency of 17.4−25.5 Hz, and the system operated normally.
This system provides the support for the intelligent improvement of seeding process, the generation of seeding state diagram, and yield prediction.
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