Development and Application of a Remote Monitoring System for Agricultural Machinery Operation in Conservation Tillage
文献类型: 外文期刊
作者: Luo, Changhai 1 ; Chen, Jingping 1 ; Guo, Shuxia 1 ; An, Xiaofei 1 ; Yin, Yanxin 1 ; Wen, Changkai 1 ; Liu, Huaiyu 1 ; Meng, Zhijun 1 ; Zhao, Chunjiang 2 ;
作者机构: 1.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China
2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
关键词: conservation tillage; subsoiling operation; operation quality; tillage depth; corn straw cover rate; operation remote monitoring
期刊名称:AGRICULTURE-BASEL ( 影响因子:3.408; 五年影响因子:3.459 )
ISSN:
年卷期: 2022 年 12 卷 9 期
页码:
收录情况: SCI
摘要: There is an increasing demand for remote monitoring and management of agricultural machinery operation in conservation tillage. Considering the problems of large errors in detecting operation quality parameters, such as tillage depth and corn straw cover rate, in complex farmland environments, this paper proposes a tillage depth measurement method based on the dual attitude compound of a tractor body and three-point hitch mechanism with lower pull rod and an online measurement method based on K-means clustering of the corn straw cover rate on farmland surface. An operation monitoring terminal was developed for the remote collection of quality parameters of conservation tillage field operation. A remote monitoring system of agricultural machinery operation was constructed and applied over a large area. The field tests showed that the static mean error and root-mean-square error of this method were 0.16 and 0.67 cm for uphill and 0.36 and 0.57 cm for downhill, respectively. For the 28 and 33 cm tillage depth tests, the mean dynamic measurement errors of this method were 0.55 and 0.61 cm, and the root means square errors were 0.64 and 0.73 cm, respectively, and the coefficient of variation of tillage depth did not exceed 3%. The correlation coefficient between the corn straw cover rate detection algorithm based on K-means clustering and the manual image marking method reached 0.92, with an average error of 9.69%, and the accuracy filled the demand for straw cover rate detection. The detection accuracy of tillage depth and straw cover rate was high and thus provides an effective means of information technology support for the quality monitoring and production management of conservation tillage farming operations.
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