在田间管理决策过程中,冠层高度是作物生长的重要动态指标。与其他常用的冠层高度测量技术相比,超声波传感器价格低廉,可以长时间暴露在野外,以获得易于处理的数据。然而,声波特性和作物冠层结构影响测量精度。提高超声波传感器测量精度,对玉米和小麦进行了为期四年(2018-2021年)的田间试验,并开发了测量平台。进行了一系列单因素实验,以调查影响测量的重要因素,包括观察角(0-60°),观测高度(0.5-2.5米),观察期(8:00-18:00),平台相对于作物的移动速度(0-2.0mmin-1),种植密度(标准种植密度的0.2-1倍),和生长阶段(玉米从三叶到收获期,小麦从变绿到成熟期)。结果表明,观测角度和种植密度对超声测量结果有显著影响(p值<0.05)。而其他因素对测量准确性的影响可忽略不计(p值>0.05)。此外,利用归一化植被指数和超声波测量,建立了双输入因子校准模型,以评估不同年份下的冠层高度。该模型是采用最小二乘法建立的,综合冠层高度测量值和归一化植被指数(NDVI),超声测量精度明显提高。玉米测量精度的均方根误差(RMSE)范围为81.4mm至93.6mm,而小麦测量精度的RMSE为37.1mm至47.2mm。研究成果将稳定、低成本的商用传感器与地面农业机械平台有效结合,使作物高度信息的有效和非破坏性的获取。
Canopy height serves as an important dynamic indicator of crop growth in the decision-making process of field management. Compared with other commonly used canopy height measurement techniques, ultrasonic sensors are inexpensive and can be exposed in fields for long periods of time to obtain easy-to-process data. However, the acoustic wave characteristics and crop canopy structure affect the measurement accuracy. To improve the ultrasonic sensor measurement accuracy, a four-year (2018-2021) field experiment was conducted on maize and
wheat, and a measurement platform was developed. A series of single-factor experiments were conducted to investigate the significant factors affecting measurements, including the observation angle (0-60°), observation height (0.5-2.5 m), observation period (8:00-18:00), platform moving speed with respect to the crop (0-2.0 m min-1), planting density (0.2-1 time of standard planting density), and growth stage (maize from three-leaf to harvest period and
wheat from regreening to maturity period). The results indicated that both the observation angle and planting density significantly affected the results of ultrasonic measurements (p-value< 0.05), whereas the effects of other factors on measurement accuracy were negligible (p-value > 0.05). Moreover, a double-input factor calibration model was constructed to assess canopy height under different years by utilizing the normalized difference vegetation index and ultrasonic measurements. The model was developed by employing the least-squares method, and ultrasonic measurement accuracy was significantly improved when integrating the measured value of canopy heights and the normalized difference vegetation index (NDVI). The maize measurement accuracy had a root mean squared error (RMSE) ranging from 81.4 mm to 93.6 mm, while the
wheat measurement accuracy had an RMSE from 37.1 mm to 47.2 mm. The research results effectively combine stable and low-cost commercial sensors with ground-based agricultural machinery platforms, enabling efficient and non-destructive acquisition of crop height information.