基于多条件时间序列的免耕播种机作业数据清洗方法

为提高作业监测数据状态预测精度,并保证无效数据的实时清洗,提高数据质量并降低监测设备的缓存压力,从而降低对后续地块作业质量评价准确性的影响,减轻数据并发带来的网络压力,本文针对免耕播种机长时序的田间周期性作业规律,提出基于多条件时间序列分析的监测数据清洗方法及模型,该模型包含3个长短时记忆特征提取模块,分别提取了工况参...

Data Cleaning Method of No-tillage Seeder Monitoring Data Based on Multi-conditional Time Series

Improving the prediction accuracy of working state of no-tillage seeder and cleaning the invalid data timely will improve the data quality and reduce the cache pressure of monitoring equipment. However, as the agricultural machinery moved back and forth in the farmland, monitoring equipment captured a large number of invalid images at both ends of the farmland or when the vehicle stopped. These images affected the accuracy of farmland operation quality evaluation and created congestion in transmission network. A data cleaning method based on multi-condition time series, mainly vehicle speed, seeding rate and instantaneous area, was proposed to deal with the periodic change of long time series of agricultural machinery in the farmland. The model included multiple long-short term memory (LSTM) and spatiotemporal feature channel fusion (CONCAT connect) to maintain the individual difference under multi-condition. The current time sequence state of the agricultural machinery working condition can be predicted, and the real-time cleaning state of the image capture system can be indirectly acquired. Due to screen and capture valid image from captured image every three minutes by cleaning state, the system achieved the maximum efficiency in transmission channel and memory space. The comparison results of different models after 40 iterations showed that the prediction accuracy of this method for both valid and invalid samples was over 85% and the average accuracy of image cleaning was 92.4%. The data cleaning results showed that about 63% of the redundant data was removed after data cleaning. Therefore, the research method took the working condition of no-tillage seeder as the basis of image cleaning was effective, which had high research value and application prospect.