水稻盆栽试验水分消耗检测系统研制及其试验研究
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摘要
农作物的田间耗水量包括蒸腾蒸发量(简称腾发量)和耕层渗漏量，其中蒸腾蒸发量被称为作物的需水量。作物腾发量是农田水分消耗的最主要形式，准确地估算作物腾发量，对研究作物的水分消耗规律、提高水分利用效率、发展节水农业具有重要的意义。
本文以实时检测盆栽水稻的耕层渗漏、地表蒸发和植株蒸腾为目标，在综合分析国内外相关研究的基础上，设计开发了盆栽水稻水分消耗数据自动采集与处理系统，构建了该系统的硬件和软件平台。同时，针对检测植株蒸腾所需的水稻鲜生物量(鲜重)难以无损测量的问题，研究了水稻鲜生物量与株型参数的相关关系，探讨了基于图像处理技术的株型参数检测方法，建立了水稻鲜生物量预测模型。通过水稻盆栽试验，验证了系统的工作性能。主要研究内容如下：
(1)设计开发了水分消耗数据自动采集与处理系统。在已有盆栽水稻水分消耗检测系统硬件平台的基础上，构建了传感器数据自动采集与处理的硬件和软件系统，硬件系统由盆栽试验台、动态电阻应变仪、数据采集卡和计算机组成，软件系统包括数据采集、数据处理、系统标定以及信息显示与回放模块，实现了多传感器信息的自动采集、处理和结果显示等功能。
(2)利用计算机视觉技术对水稻的株型参数进行无损检测，研究了水稻植株鲜生物量与株型参数的相关关系。制定了株型参数的无损测量方案，分析了不同拍摄时间的植株图像处理效果和株型参数检测结果。试验研究了水稻鲜生物量与株型参数的相关关系，结果表明，水稻鲜生物量与各株型参数之间具有显著的线性相关关系，初步确定分蘖、像株高、正像面积、侧像面积和俯像面积为自变量，利用自变量建立鲜生物量预测模型，为植株鲜生物量的无损测量提供理论基础。
(3)利用自动求取阈值法，实现了水稻植株图像与背景的分割。为实现水稻株型参数的无损检测，研究了水稻植株图像的分割算法，通过不同算法分割效果的对比分析，确定了图像处理的最优算法(即采用修正的超绿色法对图像进行灰度化，选用中值滤波法去除图像噪声，利用最大类间方差阈值分割法对图像进行二值化)，实现了水稻植株图像与背景的分割。试验结果表明，该图像处理算法对光照变化的适应性较强，植株图像的分割效果满足试验要求。
(4)构建了植株图像采集实验台，开发了图像采集与处理分析软件系统。在分析植株图像采集要求的基础上，根据需要测量的水稻株型参数，构建了图像采集实验台，以Matlab软件的GUIDE为开发工具，设计了具有人机交互式界面的图像采集与处理分析软件系统，实现了水稻像株高、正像面积、侧像面积和俯像面积等株型参数的自动化测量。
(5)建立了水稻植株鲜生物量预测模型。利用SPSS软件建立了鲜生物量的经典线性回归模型和主成分线性回归模型(PCR)，采用Matlab软件的神经网络分析法建立了鲜生物量的非线性模型，采用相关系数(R)、校正集均方根误差(RMSEC)和预测集均方根误差(RMSEP)对模型精度进行检验。将各阶段模型应用于盆栽水稻鲜生物量的预测，并对模型进行了修正。研究结果表明，分阶段的修正模型对盆栽水稻鲜生物量的预测精度最高。
(6)通过对比试验验证了水分消耗自动检测系统的工作性能。利用蒸渗测筒和水分消耗系统分别检测盆栽水稻的水分消耗量，对比分析结果表明，两种方法对水稻地表蒸发、耕层渗漏和植株蒸腾的检测结果具有显著的线性相关关系。同时，对系统的连续检测和定点检测功能进行了试验研究，分析了盆栽水稻的水分消耗规律，试验结果与前人的研究结果一致，符合水稻生长和水分消耗的一般规律。通过整机工作性能检验，该检测系统能够满足水分消耗盆栽试验的要求，系统的工作性能稳定，故障率低。
Crops water consumption in field includes evapotranspiration and amount of the toplayer leakage, which evapotranspiration is called plant water requirement. Evapotranspirationis the main style of water consumption in field, so, accurately estimating it is vital to study onregular pattern of crop water consumption, enhancing water utilization and developingwater-saving agriculture.
Based on analyzing the domestic and foreign correlation research, this paper is aimed toreal-time detect top layer leakage, soil surface evaporation and plant transpiration, developswater consumption data collection and processing system for the potted crops, and constructsthe hardware and software platform. At the same time, considering it is very difficult to fulfillnon-destructive measurement rice fresh biomass needed in testing plant transpiration, thecorrelation relationship between the plant fresh biomass and the plant-type parameters wasstudied, the plant-type parameters detective method based on image processing technologywas discussed, and the plant fresh biomass prediction model was established. The workingperformance of system was verified by potted crops experiments. The main contents are asfollows:
(1) Water consumption data collection and processing system was developed. Thehardware and software system for sensor data collection and processing was constructed,which was based on existing hardware platform of potted crops water consumption detectionsystem. The hardware system consists of potted test-bed, strain indicator for dynamicresistance, data acquisition card and computer, and the software system includes the datacollection and processing, system calibration and information display and replay modules,which realized the function of multi-sensor information collection, processing and resultdisplay.
(2) Nondestructive test of the plant-type parameters based on computer vision wasstudied, correlativity between rice fresh biomass and plant-type parameters was analyzed. Thedetective scheme was established, processing result of image and detection result of plantparameters in the different time was analyzed comparatively. The relationship betweenplant-type parameters and rice fresh biomass was researched by experiments. The resultsshowed that the relationship of plant-type parameters and rice fresh biomass is significantlylinear, on the basis, tillering, plant height, area of front image, area of lateral image and areaof top-view image were identified as the arguments, then prediction model of fresh biomasswas made through the arguments, which provides theoretical basis for nondestructive test of plant fresh biomass.
(3) The segmentation of image and background of rice plants was fulfilled by automaticcalculating a threshold. The different methods of image segmentation were studied andanalyzed in order to realize the automatic measurement of the crop plant type parameters, inthe end, the best methods of image processing(graying image by revised super-green method,eliminating image noise by median filter, fulfilling image binarization by Otsu’s method)were obtained, and the image of rice plant and background was efficiently divided. Theexperimental results show that the image processing algorithms have better adaptability toillumination changes, and the effect of plant image segmentation conforms to theexperimental requirement.
(4) Constructing a test bench for plant image acquisition, and developing imageacquisition, processing and analysis software system. Based on the analysis of plant imageacquisition requirements, the test bench for image capture was constructed according to riceplant-type parameters needed to be measured. Image acquisition and processing softwaresystem with human-machine interaction interface was designed by using GUIDE tool inMatlab, which may automatically achieve plan-type parameters, such as plant height, area offront image, area of lateral image, area of top-view image, etc.
(5) Prediction model of rice-plant fresh biomass was established. Classic linearityregression and Principal Component Regression(PCR) models were obtained by using SPSS,the nonlinear model was obtained by using neural network in Matlab, and the accuracy of themodels was verified by adopting correlation coefficient R, Root Mean Square CorrectionError(RMSEC), Root Mean Square Error Pridiction(RMSEP). The models in all stages wereused to predict fresh biomass of the potted rice, and then the models were revised. The resultsshow the prediction accuracy of Error Correction Model(ECM) with different stages is thehighest.
(6) Work performance of automatically test system for water consumption was verifiedby comparative experiments. Water consumption of potted rice was tested by a lysimeter andwater consumption system respectively, and comparative results illustrate that detectionresults to the soil surface evaporation of rice, top layer leakage and plant transpiration, whichwere obtained by the two methods are the significant linear correlations. At the same time,continuous detection and fixed-point detection test performances of the system wereconfirmed by the experiments, and the water consumption law potted rice was analyzed. Theresults show the test findings are consistent with other scholars, and in line with the generallaws of the rice growth and water consumption. In summary, the detection system can meetexperimental requirements of water consumption detection for potted rice, and the system performance is stable, failure rate is lower.
本文以实时检测盆栽水稻的耕层渗漏、地表蒸发和植株蒸腾为目标，在综合分析国内外相关研究的基础上，设计开发了盆栽水稻水分消耗数据自动采集与处理系统，构建了该系统的硬件和软件平台。同时，针对检测植株蒸腾所需的水稻鲜生物量(鲜重)难以无损测量的问题，研究了水稻鲜生物量与株型参数的相关关系，探讨了基于图像处理技术的株型参数检测方法，建立了水稻鲜生物量预测模型。通过水稻盆栽试验，验证了系统的工作性能。主要研究内容如下：
(1)设计开发了水分消耗数据自动采集与处理系统。在已有盆栽水稻水分消耗检测系统硬件平台的基础上，构建了传感器数据自动采集与处理的硬件和软件系统，硬件系统由盆栽试验台、动态电阻应变仪、数据采集卡和计算机组成，软件系统包括数据采集、数据处理、系统标定以及信息显示与回放模块，实现了多传感器信息的自动采集、处理和结果显示等功能。
(2)利用计算机视觉技术对水稻的株型参数进行无损检测，研究了水稻植株鲜生物量与株型参数的相关关系。制定了株型参数的无损测量方案，分析了不同拍摄时间的植株图像处理效果和株型参数检测结果。试验研究了水稻鲜生物量与株型参数的相关关系，结果表明，水稻鲜生物量与各株型参数之间具有显著的线性相关关系，初步确定分蘖、像株高、正像面积、侧像面积和俯像面积为自变量，利用自变量建立鲜生物量预测模型，为植株鲜生物量的无损测量提供理论基础。
(3)利用自动求取阈值法，实现了水稻植株图像与背景的分割。为实现水稻株型参数的无损检测，研究了水稻植株图像的分割算法，通过不同算法分割效果的对比分析，确定了图像处理的最优算法(即采用修正的超绿色法对图像进行灰度化，选用中值滤波法去除图像噪声，利用最大类间方差阈值分割法对图像进行二值化)，实现了水稻植株图像与背景的分割。试验结果表明，该图像处理算法对光照变化的适应性较强，植株图像的分割效果满足试验要求。
(4)构建了植株图像采集实验台，开发了图像采集与处理分析软件系统。在分析植株图像采集要求的基础上，根据需要测量的水稻株型参数，构建了图像采集实验台，以Matlab软件的GUIDE为开发工具，设计了具有人机交互式界面的图像采集与处理分析软件系统，实现了水稻像株高、正像面积、侧像面积和俯像面积等株型参数的自动化测量。
(5)建立了水稻植株鲜生物量预测模型。利用SPSS软件建立了鲜生物量的经典线性回归模型和主成分线性回归模型(PCR)，采用Matlab软件的神经网络分析法建立了鲜生物量的非线性模型，采用相关系数(R)、校正集均方根误差(RMSEC)和预测集均方根误差(RMSEP)对模型精度进行检验。将各阶段模型应用于盆栽水稻鲜生物量的预测，并对模型进行了修正。研究结果表明，分阶段的修正模型对盆栽水稻鲜生物量的预测精度最高。
(6)通过对比试验验证了水分消耗自动检测系统的工作性能。利用蒸渗测筒和水分消耗系统分别检测盆栽水稻的水分消耗量，对比分析结果表明，两种方法对水稻地表蒸发、耕层渗漏和植株蒸腾的检测结果具有显著的线性相关关系。同时，对系统的连续检测和定点检测功能进行了试验研究，分析了盆栽水稻的水分消耗规律，试验结果与前人的研究结果一致，符合水稻生长和水分消耗的一般规律。通过整机工作性能检验，该检测系统能够满足水分消耗盆栽试验的要求，系统的工作性能稳定，故障率低。
Crops water consumption in field includes evapotranspiration and amount of the toplayer leakage, which evapotranspiration is called plant water requirement. Evapotranspirationis the main style of water consumption in field, so, accurately estimating it is vital to study onregular pattern of crop water consumption, enhancing water utilization and developingwater-saving agriculture.
Based on analyzing the domestic and foreign correlation research, this paper is aimed toreal-time detect top layer leakage, soil surface evaporation and plant transpiration, developswater consumption data collection and processing system for the potted crops, and constructsthe hardware and software platform. At the same time, considering it is very difficult to fulfillnon-destructive measurement rice fresh biomass needed in testing plant transpiration, thecorrelation relationship between the plant fresh biomass and the plant-type parameters wasstudied, the plant-type parameters detective method based on image processing technologywas discussed, and the plant fresh biomass prediction model was established. The workingperformance of system was verified by potted crops experiments. The main contents are asfollows:
(1) Water consumption data collection and processing system was developed. Thehardware and software system for sensor data collection and processing was constructed,which was based on existing hardware platform of potted crops water consumption detectionsystem. The hardware system consists of potted test-bed, strain indicator for dynamicresistance, data acquisition card and computer, and the software system includes the datacollection and processing, system calibration and information display and replay modules,which realized the function of multi-sensor information collection, processing and resultdisplay.
(2) Nondestructive test of the plant-type parameters based on computer vision wasstudied, correlativity between rice fresh biomass and plant-type parameters was analyzed. Thedetective scheme was established, processing result of image and detection result of plantparameters in the different time was analyzed comparatively. The relationship betweenplant-type parameters and rice fresh biomass was researched by experiments. The resultsshowed that the relationship of plant-type parameters and rice fresh biomass is significantlylinear, on the basis, tillering, plant height, area of front image, area of lateral image and areaof top-view image were identified as the arguments, then prediction model of fresh biomasswas made through the arguments, which provides theoretical basis for nondestructive test of plant fresh biomass.
(3) The segmentation of image and background of rice plants was fulfilled by automaticcalculating a threshold. The different methods of image segmentation were studied andanalyzed in order to realize the automatic measurement of the crop plant type parameters, inthe end, the best methods of image processing(graying image by revised super-green method,eliminating image noise by median filter, fulfilling image binarization by Otsu’s method)were obtained, and the image of rice plant and background was efficiently divided. Theexperimental results show that the image processing algorithms have better adaptability toillumination changes, and the effect of plant image segmentation conforms to theexperimental requirement.
(4) Constructing a test bench for plant image acquisition, and developing imageacquisition, processing and analysis software system. Based on the analysis of plant imageacquisition requirements, the test bench for image capture was constructed according to riceplant-type parameters needed to be measured. Image acquisition and processing softwaresystem with human-machine interaction interface was designed by using GUIDE tool inMatlab, which may automatically achieve plan-type parameters, such as plant height, area offront image, area of lateral image, area of top-view image, etc.
(5) Prediction model of rice-plant fresh biomass was established. Classic linearityregression and Principal Component Regression(PCR) models were obtained by using SPSS,the nonlinear model was obtained by using neural network in Matlab, and the accuracy of themodels was verified by adopting correlation coefficient R, Root Mean Square CorrectionError(RMSEC), Root Mean Square Error Pridiction(RMSEP). The models in all stages wereused to predict fresh biomass of the potted rice, and then the models were revised. The resultsshow the prediction accuracy of Error Correction Model(ECM) with different stages is thehighest.
(6) Work performance of automatically test system for water consumption was verifiedby comparative experiments. Water consumption of potted rice was tested by a lysimeter andwater consumption system respectively, and comparative results illustrate that detectionresults to the soil surface evaporation of rice, top layer leakage and plant transpiration, whichwere obtained by the two methods are the significant linear correlations. At the same time,continuous detection and fixed-point detection test performances of the system wereconfirmed by the experiments, and the water consumption law potted rice was analyzed. Theresults show the test findings are consistent with other scholars, and in line with the generallaws of the rice growth and water consumption. In summary, the detection system can meetexperimental requirements of water consumption detection for potted rice, and the system performance is stable, failure rate is lower.
引文
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