Research On The Measurement Scheme Of Paddy Rotary Tillage Depth And Flatness

Tillage depth and flatness are important indicators for evaluating the quality of paddy field tillage operations.Due to the unique soil properties of paddy fields,traditional manual measurement is time consuming and less accurate,with the current methods of measuring tillage depth and flatness in paddy fields being susceptible to cost and external interference.In this research,we measure the tillage depth and the flatness of the dry tillage in the paddy field,develop a tillage depth measuring device and a soil bin test-bed,Study of tillage depth and flatness measurement methods and data processing methods,and verification by corresponding tests.The test results show that the device and the test-bed work properly,that the data fusion processing algorithm can effectively combine the sensor measurements,and that the time-series prediction algorithm can predict the trend of the corresponding measurement values and assist the efficient operation of the corresponding equipment.The main research work is as follows.(1)The device is designed and tested to measure the tillage depth and the flatness of the soil trough for the dryland working environment of the paddy field during the rotary tillage and land preparation operation.The device and the test-bed use magnetostrictive displacement sensors and ultrasonic sensors to acquire data,attitude sensors to correct the data acquisition value,GPS to determine the measurement location point,and the measurement process error analysis,3D scanner scanning and installation calibration to obtain the measurement data.At the same time,the paddy fields tillage depth and flatness measurement control system is developed,and the monitoring software is designed to realise the real-time display of the working status of the sensors and the measured data values.The test results show that the device and test-bed can effectively measure the tillage depth and flatness data.(2)Noise interference exists on the complex sensor measurement of the paddy field environment.This research uses the Kalman filter distributed fusion algorithm to filter out the noise points in the sensor measurement,and fuses the magnetostrictive displacement sensor and ultrasonic sensor data to obtain the optimal measurement value.By comparing the accuracy of the processed measurements of the actual measurements of the corresponding indicators,it was verified that the processed measurements accurately reflected the actual tillage depth and flatness data.the results show that the mean,variance and standard deviation of the fused data are close to the relevant parameters of the actual measurement data,the curve of the fused data fits the actual measurement data curve,and the fused data can be used as parameters of the corresponding equipment operation index.(3)To address the problems that the preset ploughing depth of the equipment may shift during rotary tillage operation without timely warning,and that the land preparation equipment cannot adjust the power quickly and effectively when the terrain changes,this research conducted ARIMA model and LSTM neural network temporal prediction analysis of different sets of fusion processed data,and compared the predicted values with accuracy of the filtered fusion value to verify the feasibility of the prediction algorithm to assist the tillage equipment to solve such problems.the results show that both prediction algorithms are effective in predicting data and the more data available the better the prediction algorithm performs,with the LSTM neural network predicting better than the ARIMA model and better matching the actual values.the LSTM neural network is more feasible in assisting the rotary tillage equipment to maintain the preset tillage depth smoothly and aiding the tilling equipment to adjust the power quickly with changes in the flatness value.