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.
- 相关文献
作者其他论文 更多>>
-
Development and Testing of Row-Controlled Weeding Intelligent Robot for Corn
作者:Zhang, Ya-wei;Chen, Du;Xu, Xiu-ming;Lu, Jinbo;Lai, Han-rong;Wen, Changkai;Zhang, Ya-wei;Liu, Meng-nan;Chen, Du;Wen, Changkai;Yin, Yan-xin;Yin, Yan-xin
关键词:agriculture robotics; navigation; path planning; row-controlled weeding; weeding device
-
Recognition of maize seedling under weed disturbance using improved YOLOv5 algorithm
作者:Tang, Boyi;Zhao, Chunjiang;Tang, Boyi;Zhou, Jingping;Pan, Yuchun;Qu, Xuzhou;Cui, Yanglin;Liu, Chang;Li, Xuguang;Zhao, Chunjiang;Gu, Xiaohe;Li, Xuguang
关键词:Object detection; Maize seedlings; UAV RGB images; YOLOv5; Attention mechanism
-
Boosting Cost-Efficiency in Robotics: A Distributed Computing Approach for Harvesting Robots
作者:Xie, Feng;Xie, Feng;Li, Tao;Feng, Qingchun;Li, Tao;Feng, Qingchun;Chen, Liping;Zhao, Chunjiang;Zhao, Hui
关键词:5G network; computation allocation; edge computing; harvesting robot; visual system
-
Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering
作者:Xu, Bo;Zhao, Chunjiang;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao
关键词:tassel; 3D phenotyping; TreeQSM; genotyping; clustering
-
High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges
作者:Cheng, Tao;Zhang, Dongyan;Cheng, Tao;Wang, Zhaoming;Zhang, Dongyan;Zhang, Gan;Yuan, Feng;Liu, Yaling;Wang, Tianyi;Ren, Weibo;Zhao, Chunjiang
关键词:Forage; High-throughput phenotyping; Precision identification; Sensors; Artificial intelligence; Efficient breeding
-
Study on seeding delay time and lag distance of automatic section control system for maize seeder
作者:Ling, Lin;Li, Hanqing;Xiao, Yuejin;Fu, Weiqiang;Dong, Jianjun;Li, Liwei;Liu, Rui;Huang, Xinguang;Wu, Guangwei;Meng, Zhijun;Yan, Bingxin;Ling, Lin;Fu, Weiqiang;Wu, Guangwei;Meng, Zhijun;Yan, Bingxin
关键词:Automatic section control; Seeding delay time; Seeding lag distance; Influencing factor; Variation pattern
-
Enhancing potato leaf protein content, carbon-based constituents, and leaf area index monitoring using radiative transfer model and deep learning
作者:Feng, Haikuan;Fan, Yiguang;Ma, Yanpeng;Liu, Yang;Chen, Riqiang;Bian, Mingbo;Fan, Jiejie;Yang, Guijun;Zhao, Chunjiang;Feng, Haikuan;Zhao, Chunjiang;Yue, Jibo;Fu, Yuanyuan;Leng, Mengdie;Jin, Xiuliang;Zhao, Yu
关键词:Potato; Deep learning; Radiative transfer model; Transfer learning; Leaf protein content



