Research and implementation of no-tillage information monitoring strategy based on Hadoop

Based on the Hadoop, this paper uses linear regression algorithm to study the intelligent monitoring strategy of no-tillage information. In this paper, the regional computers in this system use Flume and Kafka to transfer large amounts of farming information from local node databases to HDFS (Hadoop...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2020-11, Vol.1651 (1), p.12046
Main Authors: Yang, Hongwei, Xue, Fucheng, Gao, Sheng, Zhu, Hongmei, Li, Li
Format: Article
Language:English
Subjects:
Algorithms
Data storage
Economic impact
Fault tolerance
Monitoring
Physics
Regression analysis
Systems stability
Tillage
Time lag
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Based on the Hadoop, this paper uses linear regression algorithm to study the intelligent monitoring strategy of no-tillage information. In this paper, the regional computers in this system use Flume and Kafka to transfer large amounts of farming information from local node databases to HDFS (Hadoop Distributed File System), then process this data with Spark, whatever, the system uses time series linear regression algorithm to predict the fault of no-tillage. Compared with the no-tillage designed before, the system described in this paper not only reduces time delay but also improves the fault tolerance of data storage and the system stability. Field experiments have proved that the research of this strategy has an accuracy rate of up to 90%, which reduces the huge economic losses caused by machine failure.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1651/1/012046