耕作机具耕深在线测量装置设计与试验

Design and Experiment of Online Tillage Depth Measurement Device for Tillage Implements

摘要:

耕深是评价耕作机具作业质量的重要指标，针对人工耕深测量存在误差大、效率低、无法在线监测等问题，本文设计了一种具备通用性的耕深在线测量装置。通过激光测距传感器与九轴姿态传感器协同采集耕作机具动态作业数据，结合高斯滤波与卡尔曼滤波算法实现数据降噪与融合，实时在线计算耕深数据，并通过LoRa数传电台将数据实时回传到操作终端显示并存储，经土槽平台试验验证了方案可行性。试验结果表明，静态测试中，加权融合数据与人工测量数据对比，误差最大值为0.43cm，误差平均值为0.26cm，均方根误差为0.24cm；在预定耕深为8、12、15cm的动态测量中，最大偏差为1.63、1.80、1.18cm，耕深变异系数为6.37%、5.28%和2.68%。研究结果表明，本装置可提高农机试验鉴定效率、精度与信息化程度。

Abstract:

Tillage depth is a critical parameter for evaluating the performance of agricultural tillage equipment. To overcome the limitations of manual measurement methods, including high error rates, low efficiency, and the lack of real-time monitoring, a universal online tillage depth measurement system was presented. The system integrated a laser distance sensor with a nine-axis attitude sensor to dynamically capture operational data from tillage tools. Utilizing Gaussian and Kalman filtering algorithms, the system effectively reduced noise and fuses data, enabling real-time calculation of tillage depth. The results were transmitted wirelessly via LoRa to an operator terminal for display, storage, and analysis. Comprehensive soil bin experiments were conducted to validate the system’s performance. In static tests, the weighted fusion data demonstrated a maximum error of 0.43cm, an average error of 0.26cm, and a root mean square error of 0.24cm when compared with results of manual measurements. Dynamic tests with target depths of 8cm, 12cm, and 15cm yielded maximum deviations of 1.63cm, 1.80cm, and 1.18cm, respectively, with corresponding depth variation coefficients of 6.37%, 5.28%, and 2.68%. These results confirmed the system’s ability to significantly enhance the efficiency, accuracy, and digitalization of agricultural machinery testing. The proposed system provided a reliable, real-time monitoring solution for precision agriculture, reducing reliance on manual methods and improving operational transparency. Its adaptability to various tillage conditions and high measurement reliability make it a valuable tool for advancing agricultural mechanization and smart farming practices.