Rotary Tillage Depth Detection Based on Multi-sensor Data Fusion
Abstract
Tillage depth is usually used to measure the quality of rotary tillage. Suitable tillage depth can break soil compaction, improve soil structure and promote crop root growth. At present, the research scene of ploughing depth detection is mainly dry field, and the detection method is mainly indirect detection by inclination sensor. However, the soil of paddy field is soft, and the problems of sinking and attitude change will occur in the operation process. Therefore, a tillage depth detection system suitable for different soil environments was designed. In order to detect terrain relief and tool sag, a ground copying mechanism was designed, and its design parameters were explored through coupling simulation to verify its feasibility. In order to improve the indirect detection stability of single sensor, the detection method of three-point suspension inclination detection and relative elevation data fusion of Beidou system (BDS) were used, and a tillage detection model (TDM) was established. Adaptive iterative extended Kalman filter (AIEKF) was proposed to filter and then fuse data acquired by sensors to obtain more stable and accurate depth. On the basis of referring to the traditional tillage depth measurement method, the RTK-BDS elevation difference measurement method was proposed to obtain the true tillage depth value, and compared with the traditional method. The coupling simulation results showed that the absolute error was less than 0.5 cm and the maximum shape variable of soil trough elevation was 2.89 mm. Field experiment results showed that TDM detection model can effectively reflect the change of ploughing depth. The signal to noise ratio of AIEKF processing data was increased by 1.41 dB on average compared with that of KF processing data, and the ploughing depth data obtained by fusion was increased by 2.30% and 2.07% on average compared with the ploughing depth MAPE detected by two single sensors, respectively. After fusion, the average MAPE was 3.95% and the average RMSE was 1.08 cm. The maximum absolute error of RTK-BDS difference truth detection method and traditional detection method was 2.45 cm, which can complete the truth detection of ploughing depth well.
Key words: rotary tillage preparation, motion simulation, tillage depth detection , RTK-BDS
Download Full Text:
PDFReferences
LIU Chengliang,LIN Hongzhen,LI Yanming,et al. Analysis on status and development trend of intelligent control technology for agricultural equipment [J]. Transactions of the Chinese Society for Agricultural Machinery ,2020 ,51 ( 1 ) : 1 — 18. (in Chinese)
MUDARISOV S, GAINULLIN I, GABITOV I, et al. Soil compaction management: reduce soil compaction using a chain-track tractor. Journal of Terramechanics, 2020, 89: 1 -12.
TEKN A B, YALIN H. Design and development of a front mounted on-the-go soil strength profile sensor [J]. Turkish Journal of Agriculture and Forestry, 2019, 43(2) ; 151 - 163.
SHAHh A N, TANVEER M, SHAHZAD В, et al. Soil compaction effects on soil health and cropproductivity: an overview [J]. Environmental Science & Pollution Research International, 2017, 24( 11): 1 - 12.
EDRRIS M K, AL-GAADI К A, HASSABALLA A A, et al. Impact of soil compaction on the engineering properties of potato tubers[J]. International Journal of Agricultural and Biological Engineering, 2020, 13(2) : 163.
ZHENG Kan, HE Jin, WANG Qingjie, et al. The current research status of ccombined tillage machine [J]. Journal of Agricultural Mechanization Research ,2016, 38(1): 257 -263. ( in Chinese)
LI Zhenxiao. Research and design of operating depth measurement apparatus for farm implement[J]. Transactions of the Chinese Society for Agricultural Machinery ,2000,31 ( 3 ) : 88 -91. (in Chinese)
XIE Bin, LI Hao, ZHU Zhongxiang, et al. Measuring tillage depth for tractor implement automatic using inclinometer [J]. Transactions of the CSAE,2013, 29(4) : 15 -21. (in Chinese)
VAN DER L S, MOVAZEN A M, ANTHONIS J, et al. Infrared laser sensor for depth measurement to improve depth control in intra-row mechanical weeding[J]. Biosystems Engineering, 2008, 100(3) : 309 -320.
MICHAEL E, ROBERT В G. Computer vision based pose detection of agricultural implements without a priori knowledge of their geometry and visual appearance [ С ] // 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2018.
HIREMATH S A, VAN DER HEIJDEN G W A M, VAN EVERT F K, et al. Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter [J] . Computers and Electronics in Agriculture, 2014, 100: 41 -50.
HUANG Peikui, ZHANG Zhigang, LUO Xiwen, et al. Design and test of tilt angle measurement system for agricultural implements [J]. Transactions of the CSAE, 2017, 33(22) ; 9 - 16. (in Chinese)
SESNIE S E, JOHNSON L, YURCICH E, et al. Multisensor models for assessing recurrent fire compatibility with habitat recovery for a critically endangered species [j]. Remote Sensing of Environment, 2022, 269: 112824.
XIA J unfang, LI Dong,LIU Guoyang,et al. Design and test of electro-hydraulic monitoring device for hitch tillage depth based on measurement of tractor pitch angle [ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2021 , 52 ( 8 ) ; 386 -395. (in Chinese)
WANG Lei, DU Zhiqian, WANG Ling,et al. Research on tillage depth measuring device and prediction analysis of MLR model [J] . Journal of Agricultural Mechanization Research, 2023, 45(6) : 139 - 145. ( in Chinese)
JIANG Xiaohu, TONG Jin, MA Yunhai,et al. Study of tillage depth detecting device based on Kalman filter and fusion algorithm [J] . Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(9) ; 53 -60. (in Chinese)
KIM Y, KIM T, KIM Y, et al. Development of a real-time tillage depth measurement system for agricultural tractors: application to the effect analysis of tillage depth on draft force during plow tillage[ J]. Sensors, 2020. 20(3) :912 -918.
WU Q, MA Y, LI D, et al. Denoising algorithm of ground-airborne tiine-domain electromagnetic method based on Variational Bayesian-based adaptive Kalman filter (VBAKF)[J]. Journal of Applied Geophysics, 2022, 202: 104674.
YUAN Z Q, WANG Z C, XIN Y, et al. Synchronous identification of nonlinear structural parameters and unknown external excitation based on improved UКF[ J ]. Engineering Structures, 2024, 298; 117094.
LIU N, SU Z, LI Q , et al. A combined CKF — PSR method for random noise compensation of vibratory gyroscopes[J]. Journal of Industrial Information Integration, 2022, 25: 100241.
GAO Z, GE M, LI Y, et al. Modeling of multi-sensor tightly aided BDS triple-frequency precise point positioning and initial assessments[J]. Information Fusion, 2020, 55: 184 -198.
CHIANG К W, TSAI G J, CHANG H W, et al. Seamless navigation and mapping using an INS/GNSS/grid-based SLAM semi-tightly coupled integration scheme [J]. Information Fusion, 2019, 50; 181 - 196.
Lt Jinqing, LIU Jinni, ZHAO Zhiming, et al. Design and test of key components of potato soil-crushing site preparation machine [J] . Transactions of the Chinese Society for Agricultural Machinery ,2023 ,54( 8 ) ;19 -29. (in Chinese)
ZHANG Chong, FAN Xuhui, LI Mingsen, et al. Simulation analysis and test of soil disturbance by chisel plow based on EDEM [J] . Transactions of the Chinese Society for Agricultural Machinery ,2022 ,53 ( Supp. 2 ) : 52 -59.
Refbacks
- There are currently no refbacks.
