Method for the height measurement of agricultural implements based on variable parameter Kalman filter

To improve the GNSS receiver’s accuracy,continuity,and stability in measuring the height of agricultural implements,this study proposed a variable-parameter Kalman filter(VPKF)algorithm based on GNSS and accelerometer to estimate the height of the implements optimally.The VPKF was verified,and its accuracy was evaluated by parallel rail platform and field tests.From the parallel rail test results,when the GNSS receiver was in real-time kinematic(RTK)positioning and the time delay of differential correction data(TDDCD)was less than or equal to 4 s,the root mean square error(RMSE)of the VPKF estimation was 9.82 mm.The RMSE of the GNSS measurement was 18.85 mm.When the GNSS receiver lost differential correction data within 28 s,the absolute error of VPKF was less than 30 mm,and the RMSE was 16.93 mm.The field test results showed that when the GNSS receiver was in RTK positioning and the TDDCD was less than or equal to 4 s,the RMSE of VPKF estimation was 13.43 mm,and the GNSS measurement was 14.56 mm.When the GNSS receiver lost differential correction data within 28 s,the RMSE of the VPKF estimate was 15.22 mm.These results show that VPKF can optimally estimate implement height with better accuracy.Overall,the VPKF can obtain a more accurate,continuous,and stable height of the implement,and increase the application scenarios of the GNSS receiver to measure the implement height.

Quantification of traffic-induced compaction based on soil and agricultural implement parameters

Vehicle-induced soil compaction occurs when agricultural machinery is working in the fields.The accumulated soil compaction could destroy soil structure and inhibit crop growth.The low degree of visualization of soil compaction has always been an important reason for restricting the development of compaction alleviation technology.Therefore,the main objective of this study was to predict soil compaction based on soil and agricultural implement parameters.The component of soil compaction prediction includes traffic-induced stress transmission evaluation and the quantitative relationship between soil stress and bulk density.The modified FRIDA model was used to elucidate the soil stress propagation,which has been validated by previous studies.The Bailey formula was used to establish the intrinsic relationship between soil stress and bulk density.The soil uniaxial compression test was applied to obtain the parameters of the Bailey formula,and soil samples were prepared with three different levels of water content.After fitting with the Bailey formula,under the condition that the soil moisture contents were 16%,20%,and 24%,the fitting coefficients of soil bulk density were respectively 0.980,0.959,and 0.975,which were close to 1.The results indicated that the Bailey formula could be used to calculate soil bulk density based on the stress conditions of the soil.To verify the practicality of the soil compaction prediction model,a field experiment was carried out in Zhuozhou City,Hebei Province,China.The treatment was set for 1,3,5,7,and 9 times compaction with two different loads of compaction equipment.The results showed that the fit coefficient between the predicted and measured values of soil bulk density was greater than 0.641.The slope of the equation was greater than 0.782,proving that the soil bulk density prediction model based on agricultural implements and soil parameters has a good predictive effect on soil bulk density.The soil compaction evaluation model can provide a theoretical basis to further understand the soil compaction mechanism,allowing rational measures of soil compaction alleviation to be made.