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

Gaolong Chen, Xiwen Luo, Lian Hu, Pei Wang, Jie He, Dawen Feng, Weicong Li, Jinkang Jiao

Abstract


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.
Key words: agricultural implement, height, Kalman filter, GNSS, accelerometer
[DOI] 10.25165/j.ijabe.20241702.8026

Citation: Chen G L, Luo X W, Hu L, Wang P, He J, Feng D W, et al. Method for the height measurement of agriculturalimplements based on variable parameter Kalman filter. Int J Agric & Biol Eng, 2024; 17(2): 193–199.

Keywords


agricultural implement, height, Kalman filter, GNSS, accelerometer

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References


Nielsen S K, Munkholm L J, Lamandé M, Nørremark M, Skou-Nielsen N, Edwards G T C, et al. Seed drill instrumentation for spatial coulter depth measurements. Computers and Electronics in Agriculture, 2017; 141: 207–214.

Partel V, Costa L, Ampatzidis Y. Smart tree crop sprayer utilizing sensor fusion and artificial intelligence. Computers and Electronics in Agriculture, 2021; 191: 106556.

Dou H J, Zhai C Y, Chen L P, Wang S L, Wang X. Field variation characteristics of sprayer boom height using a newly designed boom height detection system. IEEE Access, 2021; 9: 17148–17160.

Chang Y K, Zaman Q U, Rehman T U, Farooque A A, Esau T, Jameel M W. A real-time ultrasonic system to measure wild blueberry plant height during harvesting. Biosystens Engineering, 2017; 157: 35–44.

Bhatt R, Singh P, Hossain A, Timsina J. Rice-wheat system in the northwest indo-gangetic plains of south asia: issues and technological interventions for increasing productivity and sustainability. Paddy and Water Environment, 2021; 19(3): 345–365.

Aryal J P, Mehrotra M B, Jat M L, Sidhu H S. Impacts of laser land leveling in rice-wheat systems of the north-western indo-gangetic plains of India. Food Security, 2015; 7: 725–738.

Jat M L, Gupta R, Saharawat Y S, Khosla R. Layering precision land leveling and furrow irrigated raised bed planting: productivity and input use efficiency of irrigated bread wheat in indo-gangetic plains. American Journal of Plant Sciences, 2011; 2(4): 578–588.

Zhao R M, Hu L, Luo X W, Zhou H, Du P, Tang L M, et al. A novel approach for describing and classifying the unevenness of the bottom layer of paddy fields. Computers and Electronics in Agriculture, 2019; 162: 552–560.

Hu L, Yang W W, He J, Zhou H, Zhang Z G, Luo X W, et al. Roll angle estimation using low cost MEMS sensors for paddy field machine. Computers and Electronics Agriculture, 2019; 158: 183–188.

Zhou H, Hu L, Luo X W, Tang L M, Du P, Mao T, et al. Design and test of laser-controlled paddy field levelling-beater. Int J Agric & Biol Eng, 2020; 13(1): 57–65.

Zhao R M, Hu L, Luo X W, Zhang W Y, Chen G L, Huang H, et al. Method for estimating vertical kinematic states of working implements based on laser receivers and accelerometers. Biosystems Engineering, 2021; 203: 9–21.

Hu L, Xu Y, He J, Du P, Zhao R M, Luo X W. Design and test of tractor-attached laser-controlled rotary scraper land leveler for paddy fields. Journal of Irrigation and Drainage Engineering, 2020; 146(4): 1–8.

Tang L M, Hu L, Zang Y, Luo X W, Zhou H, Zhao R M, et al. Method and experiment for height measurement of scraper with water surface as benchmark in paddy field. Computers and Electronics in Agriculture, 2018; 152: 198–205.

Wang L, Liu G, Liu Y, Li H P. GPS-based land slope leveling technique and its implementation. Journal of Drainage and Irrigation Machinery Engineering, 2013; 31(5): 456–460. (in Chinese)

Hu L, Yang W W, Xu Y, Zhou H, Luo X W, Ke X R, et al. Design and experiment of paddy field leveler based on GPS. Journal of South China Agriculture University, 2015; 36(5): 130–134. (in Chinese)

Xia Y X, Liu G, Kang X, Jing Y P. Optimization and analysis of location accuracy based on GNSS-controlled precise land leveling system. Transaction of the CSAM, 2019; 48(S1): 40–44. (in Chinese)

Wang L, Li Z S, Yuan H, Zhao J J, Zhou K, Yuan C. Influence of the time-delay of correction for BDS and GPS combined real-time differential positioning. Electronics Letters, 2016; 52(12): 1063–1065.

No S, Han J, Kwon J H. Accuracy analysis of network-RTK(VRS) for real time kinematic positioning. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 2012; 30(4): 389–396. (in Korean

Refan M H, Dameshghi A, Kamarzarrin M. Improving RTDGPS accuracy using hybrid PSOSVM prediction model. Aerospace Science and Technology, 2014; 37: 55–69.

Rudolph S, Marchant B P, Weihermüller L, Vereecken H. Assessment of the position accuracy of a single-frequency GPS receiver designed for electromagnetic induction surveys. Precision Agriculture, 2019; 20(1): 19–39.

Soycan M. Polynomial versus similarity transformations between GPS and Turkish reference systems. Survey Review, 2005; 38(295): 59–69.

Chen X. Key Technology Research on Attitude Measurement Based on MEMS Sensors. Master’s dissertation. Zhejiang: Zhejiang University, 2014, 26p. (in Chinese)

Pearson R K, Neuvo Y, Astola J, Gabbouj M. Generalized hampel filters. EURASIP Journal on Advances in Signal Processing, 2016; Article No. 87. doi: 10.1186/s13634-016-0383-6.

Akhlaghi S, Zhou N, Huang Z Y. Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation. IEEE Power & Energy Society General Meeting, Chicago: IEEE, 2017; pp.1–5. doi: 10.1109/PESGM.2017.8273755.




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