基于称重式蒸渗仪及多种传感器的作物表型及蒸散监测系统研制

作物蒸散量测量与估算在农业方面有着重要作用,而当前对于作物蒸散量的估算主要以试验的方式进行,有一定局限性,且测量面积小,与实际应用还有一定距离。针对以上问题,该文在已有24座小型称重式蒸渗仪基础上,集成RGB成像传感器、多光谱成像传感器和二维激光扫描仪于一体,配合龙门架进行移动控制,构建称重式蒸渗仪植物表型监测系统,实现18 m~2植物生长过程中的RGB、红(668 nm)、绿(560 nm)、蓝(475 nm)、红边(717 nm)、近红外(840 nm)图像信息和植株高度信息的自动监测。最后通过试验,在已设定好的常用速度下,系统单趟运行用时142 s,可采集28组RGB、多光谱图像及所有植株高度信息,速度相对误差在1.8%～6.0%之间。通过对获取的夏玉米多光谱图像和激光扫描仪数据信息分析,系统能够可靠获取归一化差异植被指数等作物指数及植株高度信息。并结合气象站数据,对冬小麦主要耗水期的RGB图像进行分析,对其蒸散量进行了估计,与蒸渗仪获取的实际蒸散量对比,其平均相对误差为16.62%。该系统为大面积作物蒸散量的实时监测和精确诊断以及作物生长状况研究提供有效技术与装备支撑。

Design of crop phenotype and evapotranspiration monitoring system based on weighing lysimeter and multi-sensors

Abstract: The measurement and estimation of evapotranspiration plays an important role in agriculture. In this study, we designed a plant phenotype and evapotranspiration monitoring system based on weighing lysimeter and multi-images. A total of 24 small weighing lysimeters, integrated RGB imaging sensors, multispectral imaging sensors and a 2D laser scanner were integrated with a gantry to control the movement in order to build weighing lysimeter plant phenotypic monitoring system, realizing automatic monitoring of RGB, red (668 nm), green (560 nm), blue (475 nm), the red edge (717 nm), near infrared (840 nm) image information and plant height information during plant growth period. Each lysimeter had the length of 1 m, a the width of 0.75 m and the depth of 2 m. The effective planting area was 0.75 m2. The total area was 18 m2. The intact soil was filled into the lysimeter. The lysimeter was equipped with data collecting system. The pressure signal was transformed into electrical signals. Wheather stations were installed to measure air temperature, air humidity, radiation, wind speed, precipitation, and the others. The phenotypic monitoring module was composed of RGB high speed color camera, 5-channel multi-spectral camera and laser scanner. The motion control module was of programmable logic controller in motor control cabinet in charge of moving ganty. The outside of programmable logic controller had the man-machine interaction interface. If the automatic control system failed, it could be manually controlled through the man-machine interaction interface. In this paper, phenotypic data, meteorological station data and lysimeter data were combined to not only estimate crop evapotranspiration in a large area, but also obtain various crop index and plant height information. The system was then tested at the designed normal speed and sampling frequency meeting the practical requirements. The results showed that the single journey time of the system was 142 s when the RGB and multi-spectral imaging sensor images were taken every 5 s, laser scanning once every 1 s. After a journey, the system could automatically collect 28 RGB and multi-spectral images, from which plant growth information could be derived. The obtained image data were stored in time format in a folder. During the motion control test, the single journey, time for single journey, pulse for single journey were recorded at designed motor rotation speed of 0.111 and 0.167 m/s. The relative error between the measured and the designed values was 1.8%-6.0%, indicating that the motion control performance was well. The system was used for estimation of evapotranspiration after seedling estimation of winter wheat. The RGB images were collected every 10 days. The average daily coverage and crop coefficient were calculated to calculate evapotranspiration. Finally, the estimated evapotranspiration had the relative error of 16.62% averagely, indicating the reliability of evapotranspiration estimation by the system. In addition, the acquired multi-spectral images and laser scanner data of summer maize were revealed, suggesting that the system could reliably obtain crop index and plant height information such as normalized difference vegetation index, difference vegetation index, ratio vegetation index, normalized difference green index, soil adjusted vegetation index and so on. In sum, this system integrated the lysimeter and multispectral images so as to provide an valuable technology and equipment support for real-time monitoring, accurate diagnosis of crop evapotranspiration and researches on crop growth status. In future, It is necessary to carry out researches on the acquisition of other phenotypic information and image fusion so as to obtain more crop information.