桁架式可移动作物生长远程监控系统设计

针对固定式图像采集设备终端在实时采集过程中存在的采集角度和范围有限等问题,研究开发了一种固定桁架式地面移动多点作物图像远程获取系统。系统采用网络摄像机和电动变焦镜头作为图像摄取设备,通过全方位智能控制云台、螺旋四杆机构和水平滑块导轨机构的设计与选型,建立了作物图像摄取设备的水平移动、垂直移动、360°空间转动、俯仰运动等子系统。开发了远程控制软件,建立了作物图像数据库和远程管理系统,实现了监测区域内所有植株不同部位图像信息的远程控制获取和管理。系统在温室中进行了运行测试,运行正常可靠。该系统可为作物远程在线水肥诊断提供了一种空间移动多点实时监测和远程管理平台。

基于单片机的吊秤式盆栽作物蒸渗仪设计

为实时、方便、准确地测定盆栽作物的蒸腾蒸发量，采用上位机和下位机集成、优化的方法，设计了一种0～100kg适用于盆栽作物的吊秤式蒸渗仪。蒸渗仪主要包括称量传感器、下位机、上位机、连接件、支架和盆栽容器6部分。对蒸渗仪下位机的主控制器、数据采集模块、显示模块、通信模块、实时时钟和数据存储模块以及电源模块进行了硬件选型和电路设计。试验结果表明，在无人监管的工作环境下，蒸渗仪下位机能够自动采集、显示和存储数据，并通过串口将数据上传至上位机，上位机完成数据的显示、存储和分析。蒸渗仪最大绝对误差为100g，最大相对误差为0．50％，测量结果较好地反映了盆栽作物蒸腾蒸发的实际变化趋势，能够满足盆栽作物蒸腾蒸发量方便、准确和实时测量的需要。

基于RGB图像处理的玉米叶片含水率监测方法研究

为探求作物叶片RGB图像特征与其含水率的相关性,本文使用佳能数码相机采集抽穗期玉米离体叶片的图像信息,利用烘干法测量叶片样本的含水率;研究采用灰度共生矩阵和灰度直方图的图像处理技术,提取了玉米叶片的纹理特征参数和颜色特征参数,分析其与含水率的关系。结果表明,玉米叶片RGB图像的纹理特征参数对比度、相关度、熵和能量与含水率相关性不强,难以用来预测叶片含水率。在叶片颜色特征参数均值、方差、能量、熵、峰态和歪斜度的六组特征值中,均值和峰态与含水率存在较好的相关性,可以用来预测叶片含水率。均值与含水率的关系模型决定系数R2为0.7017,含水率预测结果误差范围在±2%以内,峰态与含水率的关系模型决定系数R2为0.7175,预测结果误差范围在±1.5%以内。本文研究结果证明,利用作物叶片的RGB图像可以检测其含水率。

Dynamic remote monitoring system for plant root growth and water consumption

In order to improve the level of multi-functional and automatic observation of crop root system growth,a soil column monitoring system was designed to facilitate in situ dynamic monitoring of root growth and water consumption.The system consists of 20 plastic tubular backfill soil columns,each with an inner diameter of 32 cm and height of 300 cm.The crops were planted at the top of the soil column with the surrounding leveled with the ground surface and the site is in a greenhouse.The underground portion of the soil column contains small round windows on the tube through which root growth can be monitored,roots can be pruned and soil samples can be obtained.A multiport serial weighing system was designed and placed at the base of the soil column.Twenty electronic balances were connected to the personal computer through three CP-168U multiport serial cards and RS-232 serial cables.The host software was developed on the browser/server(Browser/Server),and data collection and remote data transmission and data sharing were implemented using the Java programming language and applying Internet data transmission technology and Web application technology.System tests showed a relatively good stability and real-time capability,and with accuracy up to 50 g and the evapotranspiration of each soil column was 0.25-0.65 kg per day.The root-system observation system developed in this study surpassed the traditional method of root-digging sampling and thus provided an alternative that could be used to automatically monitor the root system growth status.

Detecting maize leaf water status by using digital RGB images

To explore the correlation between crop leaf digital RGB(Red,Green and Blue)image features and the corresponding moisture content of the leaf,a Canon digital camera was used to collect image information from detached leaves of heading-stage maize.A drying method was adopted to measure the moisture content of the leaf samples,and image processing technologies,including gray level co-occurrence matrices and grayscale histograms,was used to extract the maize leaf texture feature parameters and color feature parameters.The correlations of these feature parameters with moisture content were analyzed.It is found that the texture parameters of maize leaf RGB images,including contrast,correlation,entropy and energy,were not significantly correlated with moisture content.Thus,it was difficult to use these features to predict moisture content.Of the six groups of eigenvalues for the leaf color feature parameters,including mean,variance,energy,entropy,kurtosis and skewness,mean and kurtosis were found to be correlated with moisture content.Thus,these features could be used to predict the leaf moisture content.The correlation coefficient(R2)of the mean-moisture content relationship model was 0.7017,and the error of the moisture content prediction was within±2%.The R2 of the kurtosis-moisture content relationship model was 0.7175,and the error of the moisture content prediction was within±1.5%.The study results proved that RGB images of crop leaves could be used to measure moisture content.

Real-time remote monitoring system for crop water requirement information

Rapidly acquiring and real-time transmitting crop water requirement information constitute the basis for achieving intelligent diagnosis and precision irrigation.In order to collect and transmit crop water requirement information at real time,a new microcontroller-based real-time remote monitoring system was designed,including system hardware design,software and anti-jamming design.The system achieved the functions including clock reading,information configuration,LCD display,keyboard control,data sending and receiving,multi-channel information acquisition,conversion and storage.Laboratory and field tests showed that the system can achieve data acquisition and real-time display of the crop water requirement information.Unlike the current weather station,the system collects crop water information,meteorological factors and soil parameters at the same time.It has a high level of stability and acquisition accuracy,and can meet the requirements for real-time remote monitoring of the crop water requirement information for irrigation decision-making.