Wang Song, Yi Shujuan, Zhao Bin, Li Yifei, Li Shuaifei, Tao Guixiang, Mao Xin, Sun Wensheng
College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
Provincial Key Laboratory of Intelligent Agricultural Machinery Equipment, Daqing 163319, China.
Sensors (Basel). 2024 Sep 30;24(19):6331. doi: 10.3390/s24196331.
High-speed precision planters are subject to high-speed (12~16 km/h) operation due to terrain undulation caused by mechanical vibration and sensor measurement errors caused by the sowing depth monitoring system's accuracy reduction problems. Thus, this study investigates multi-sensor data fusion technology based on the sowing depth monitoring systems of high-speed precision planters. Firstly, a sowing depth monitoring model comprising laser, ultrasonic, and angle sensors as the multi-sensor monitoring unit is established. Secondly, these three single sensors are filtered using the Kalman filter. Finally, a multi-sensor data fusion algorithm for optimising four key parameters in the extended Kalman filter (EKF) using an improved sparrow search algorithm (ISSA) is proposed. Subsequently, the filtered data from the three single sensors are integrated to address the issues of mechanical vibration interference and sensor measurement errors. In order to ascertain the superiority of the ISSA-EKF, the ISSA-EKF and SSA-EKF are simulated, and their values are compared with the original monitoring value of the sensor and the filtered sowing depth value. The simulation test demonstrates that the ISSA-EKF-based sowing depth monitoring algorithm for high-speed precision planters, with a mean absolute error () of 0.083 cm, root mean square error () of 0.103 cm, and correlation coefficient () of 0.979 achieves high-precision monitoring. This is evidenced by a significant improvement in accuracy when compared with the original monitoring value of the sensor, the filtered value, and the SSA-EKF. The results of a field test demonstrate that the ISSA-EKF-based sowing depth monitoring system for high-speed precision planters enhances the precision and reliability of the monitoring system when compared with the three single-sensor monitoring values. The average and are reduced by 0.071 cm and 0.075 cm, respectively, while the average is improved by 0.036. This study offers a theoretical foundation for the advancement of sowing depth monitoring systems for high-speed precision planters.
由于机械振动导致地形起伏以及播种深度监测系统精度降低问题引起的传感器测量误差，高速精密播种机需要在高速（12~16公里/小时）下运行。因此，本研究探讨了基于高速精密播种机播种深度监测系统的多传感器数据融合技术。首先，建立了以激光、超声波和角度传感器为多传感器监测单元的播种深度监测模型。其次，使用卡尔曼滤波器对这三个单传感器进行滤波。最后，提出了一种基于改进麻雀搜索算法（ISSA）优化扩展卡尔曼滤波器（EKF）中四个关键参数的多传感器数据融合算法。随后，将三个单传感器的滤波后数据进行融合，以解决机械振动干扰和传感器测量误差问题。为了确定ISSA-EKF的优越性，对ISSA-EKF和SSA-EKF进行了仿真，并将它们的值与传感器的原始监测值和滤波后的播种深度值进行比较。仿真试验表明，基于ISSA-EKF的高速精密播种机播种深度监测算法，平均绝对误差（）为0.083厘米，均方根误差（）为0.103厘米，相关系数（）为0.979，实现了高精度监测。与传感器的原始监测值、滤波后的值和SSA-EKF相比，精度有显著提高。田间试验结果表明，基于ISSA-EKF的高速精密播种机播种深度监测系统与三个单传感器监测值相比，提高了监测系统的精度和可靠性。平均和分别降低了0.071厘米和0.075厘米，而平均提高了0.036。本研究为高速精密播种机播种深度监测系统的发展提供了理论基础。
