CN103278503A - Multi-sensor technology-based grape water stress diagnosis method and system therefor - Google Patents
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Abstract
本发明公开了一种基于多传感器技术的葡萄水分胁迫诊断方法及系统，方法包括以下步骤：（1）采集建模葡萄样本的冠层覆盖率值、冠层温度特征值和冠层光合有效辐射值；（2）以冠层覆盖率值、冠层温度特征值和冠层光合有效辐射值为输入变量，冠层水分胁迫等级为输出变量建立检测模型；（3）按照步骤（1）的方法采集待测葡萄样本的冠层覆盖率值、冠层温度特征值和冠层光合有效辐射值，代入检测模型计算出待测葡萄样本的冠层水分胁迫等级。本发明通过引入多光谱成像技术、热红外成像技术以及多信息的数据融合技术，可实现葡萄水分胁迫程度的早期、快速、实时检测，提高检测精度。The invention discloses a method and system for diagnosing grape water stress based on multi-sensor technology. The method includes the following steps: (1) Collecting canopy coverage rate values, canopy temperature characteristic values and canopy photosynthetically active radiation of modeled grape samples (2) Establish a detection model with canopy coverage rate value, canopy temperature characteristic value and canopy photosynthetically active radiation value as input variable, and canopy water stress level as output variable; (3) Follow the method in step (1) The canopy coverage rate value, canopy temperature characteristic value and canopy photosynthetic active radiation value of the grape sample to be tested were collected, and substituted into the detection model to calculate the canopy water stress level of the grape sample to be tested. The present invention can realize early, fast and real-time detection of grape water stress degree by introducing multi-spectral imaging technology, thermal infrared imaging technology and multi-information data fusion technology, and improve detection accuracy.
Description
技术领域technical field
本发明涉及农作物水分胁迫诊断的检测领域，尤其涉及一种基于多传感器技术的葡萄水分胁迫诊断方法及系统。The invention relates to the detection field of crop water stress diagnosis, in particular to a grape water stress diagnosis method and system based on multi-sensor technology.
背景技术Background technique
水分对葡萄产量和自身生长的影响是深刻的，水分胁迫首先影响植株生理代谢过程，最终表现为对生长发育的影响。具体表现在：水分亏缺，葡萄各组织和器官的发育就会受阻，光合作用减弱。水分胁迫对果树叶、根的形态指标及其显微结构、叶片气孔行为、光合作用、光抑制、酶活性、内源激素变化等生理生化方面的影响都有所报道，其中对光合作用的影响尤为突出。水分胁迫使葡萄生长发育产生生理障碍，降低葡萄产量，影响葡萄浆果品质，是制约葡萄及其相关产业的重要环境因子。在葡萄种植面积很广的北方和丘陵地区，普遍存在干旱缺水的情况，快速诊断植株缺水状况，科学精确地指导灌溉，有效利用有限的水资源，保证葡萄的优质高产，成为亟需解决的问题。The impact of water on grape yield and its own growth is profound. Water stress first affects the physiological metabolic process of the plant, and finally manifests as an impact on growth and development. The specific manifestations are: water shortage, the development of grape tissues and organs will be hindered, and photosynthesis will be weakened. The effects of water stress on the morphological indicators and microstructure of fruit tree leaves and roots, leaf stomata behavior, photosynthesis, light inhibition, enzyme activity, endogenous hormone changes and other physiological and biochemical aspects have been reported. Among them, the effects on photosynthesis particularly prominent. Water stress causes physiological obstacles to the growth and development of grapes, reduces grape yield, and affects the quality of grape berries. It is an important environmental factor restricting grapes and related industries. In the northern and hilly areas where grapes are widely planted, drought and water shortages are common. Rapid diagnosis of water shortages in plants, scientific and accurate guidance of irrigation, effective use of limited water resources, and ensuring high-quality and high-yield grapes have become urgent solutions. The problem.
目前在国内，植物水分胁迫的检测手段相对落后，绝大部分靠农民长期积累的经验进行感官识别判断，这种主观评定方法受个人经验、色彩分辨力和光线等条件的影响，而且大多数停留在定性判断上，其客观性、准确性较差，容易引起因作物的缺水而导致减产等。植物水分胁迫的快速、无损检测技术综合运用了计算机和光电传感器等高新技术，目前已引起了国内外相关领域的高度重视，迄今为止已经出现了诸如声学检测、叶绿素荧光技术、光谱检测技术、以及机器视觉等技术。At present, in China, the detection methods of plant water stress are relatively backward, and most of them rely on the long-term accumulated experience of farmers for sensory identification and judgment. This subjective evaluation method is affected by personal experience, color resolution and light conditions, and most of them stay In terms of qualitative judgment, its objectivity and accuracy are poor, and it is easy to cause production reduction due to water shortage of crops. The rapid and non-destructive detection technology of plant water stress has comprehensively used high-tech technologies such as computers and photoelectric sensors, and has attracted great attention from related fields at home and abroad. technologies such as machine vision.
多光谱成像技术是一种能够同时采集可见光谱和红外光谱等波段数字图像并进行分析的技术。它结合了光谱分析技术（敏感波段提取）和计算机图像处理技术的长处，同时可以弥补光谱仪抗干扰能力较弱和RGB图像感受范围窄的缺点。针对错综复杂的外部环境和形状各异的作物冠层结构，利用多光谱成像技术，获取近红外光谱图像中植物的形状信息以及特征信息，对植物冠层结构进行快速、准确的检测。Multispectral imaging technology is a technology that can simultaneously collect and analyze digital images in bands such as visible spectrum and infrared spectrum. It combines the strengths of spectral analysis technology (sensitive band extraction) and computer image processing technology, and can make up for the shortcomings of the spectrometer's weak anti-interference ability and narrow RGB image perception range. For the intricate external environment and crop canopy structures of various shapes, multi-spectral imaging technology is used to obtain the shape information and characteristic information of plants in near-infrared spectral images, and to quickly and accurately detect plant canopy structures.
作物冠层温度已成为判别作物水分状况的重要指标，利用作物冠层温度探测作物水分状况日益受到关注。用冠层温度诊断作物水分状况能克服测定单片叶温存在的取样误差，它可以快速而准确地测定较大面积范围内的作物水分状况。作物水分胁迫指数监测作物是否遭受水分胁迫，是一个非常有效的指标，但是在国内，采用热红外成像对葡萄水分胁迫的研究还鲜见报道。Crop canopy temperature has become an important indicator for judging crop water status, and using crop canopy temperature to detect crop water status has attracted increasing attention. The use of canopy temperature to diagnose crop water status can overcome the sampling error in measuring single leaf temperature, and it can quickly and accurately measure crop water status in a large area. Crop water stress index is a very effective index to monitor whether crops suffer from water stress. However, in China, there are few reports on grape water stress using thermal infrared imaging.
光合有效辐射是指太阳辐射中能被绿色植物的叶绿体吸收来用于光合作用，从而实现物质积累的这部分辐射。植物的冠层结构即是植物群体地上部分总的绿色覆盖层，它影响着植物对太阳有效辐射的截获，是一种间接评价作物水分状况的指标。目前采用光合有效辐射值评价冠层结构从而对作物的水分胁迫进行相关研究开展得比较少。Photosynthetically active radiation refers to the part of the solar radiation that can be absorbed by the chloroplasts of green plants for photosynthesis to achieve material accumulation. The canopy structure of plants is the total green cover of the aboveground part of the plant population, which affects the interception of effective solar radiation by plants, and is an indicator for indirectly evaluating the water status of crops. At present, relatively little research has been carried out on crop water stress by using photosynthetically active radiation value to evaluate canopy structure.
以上所采用的测量作物水分胁迫的技术都存在一个共同的问题，即检测精度不高。The above-mentioned techniques for measuring water stress in crops all have a common problem, that is, the detection accuracy is not high.
发明内容Contents of the invention
本发明提供了一种基于多传感器技术的葡萄水分胁迫诊断方法及系统，通过引入多光谱成像技术、热红外成像技术以及多信息的数据融合技术，可实现葡萄水分胁迫程度的早期、快速、实时检测，提高检测精度。The invention provides a method and system for diagnosing grape water stress based on multi-sensor technology. By introducing multi-spectral imaging technology, thermal infrared imaging technology and multi-information data fusion technology, early, rapid and real-time diagnosis of grape water stress can be realized. detection and improve detection accuracy.
一种基于多传感器技术的葡萄水分胁迫诊断方法，包括以下步骤：A method for diagnosing grape water stress based on multi-sensor technology, comprising the following steps:
（1）选取建模葡萄样本，采集所述建模葡萄样本的冠层覆盖率值、冠层温度特征值和冠层光合有效辐射值；(1) Select a modeled grape sample, and collect the canopy coverage value, canopy temperature characteristic value and canopy photosynthetically active radiation value of the modeled grape sample;
（2）以所述冠层覆盖率值、冠层温度特征值和冠层光合有效辐射值为输入变量，冠层水分胁迫等级为输出变量建立如式（1）所示模型：(2) The canopy coverage rate value, canopy temperature characteristic value and canopy photosynthetically active radiation value are input variables, and the canopy water stress level is used as output variable to establish a model as shown in formula (1):
Y＝-0.03x1+3.43x2+2.25x3 （1）Y＝-0.03x 1 +3.43x 2 +2.25x 3 (1)
其中，x1为冠层覆盖率值，x2为冠层温度特征值，x3为冠层光合有效辐射值；Y为冠层水分胁迫等级；Among them, x 1 is the canopy coverage rate value, x 2 is the canopy temperature characteristic value, x 3 is the canopy photosynthetic active radiation value; Y is the canopy water stress level;
（3）按照步骤（1）的方法采集待测葡萄样本的冠层覆盖率值、冠层温度特征值和冠层光合有效辐射值，代入式（1）中，计算出待测葡萄样本的冠层水分胁迫等级。(3) According to the method of step (1), collect the canopy coverage value, canopy temperature characteristic value and canopy photosynthetic active radiation value of the grape sample to be tested, and substitute them into the formula (1) to calculate the canopy coverage of the grape sample to be tested. level of water stress.
图像的分割质量直接决定了水分特征提取和所述模型的精度，优选地，步骤（1）中采集所述建模葡萄样本冠层在近红波段的单色图像，采用二维最大信息熵阈值分割法对所述单色图像进行背景分割，计算得到所述葡萄冠层覆盖率值。The segmentation quality of the image directly determines the water feature extraction and the accuracy of the model. Preferably, the monochrome image of the modeled grape sample canopy in the near-red band is collected in step (1), and the two-dimensional maximum information entropy threshold is used The segmentation method performs background segmentation on the monochrome image, and calculates the grape canopy coverage value.
更优选地，对所述单色图像进行背景分割前采用中值滤波法对所述单色图像进行预处理。较传统图像分割法更有优势。More preferably, the monochrome image is preprocessed by using a median filter method before performing background segmentation on the monochrome image. It has more advantages than traditional image segmentation methods.
作为优选，步骤（1）中采集所述建模葡萄样本冠层的热红外图像，提取冠层温度信息，同时采集当时大气的温度信息，以冠层温度信息和大气温度信息的差值作为所述冠层温度特征值。Preferably, in step (1), the thermal infrared image of the modeled grape sample canopy is collected, the canopy temperature information is extracted, and the atmospheric temperature information is collected at the same time, and the difference between the canopy temperature information and the atmospheric temperature information is used as the The characteristic value of canopy temperature.
葡萄的冠层光合有效辐射值是指太阳光透过葡萄冠层在地面上获取的光能量的比值，其中采用的计算公式为：The photosynthetically active radiation value of the grape canopy refers to the ratio of the light energy obtained by sunlight through the grape canopy on the ground, and the calculation formula used is:
LI=[1-(冠层以下部分的太阳有效辐射值)*(冠层以上部分的太阳有效辐射值)-1]（2）LI=[1-(solar effective radiation value below the canopy)*(solar effective radiation value above the canopy) -1 ](2)
其中LI为葡萄的冠层光合有效辐射值；Where LI is the canopy photosynthetically active radiation value of the grape;
采集时，分别采集冠层以上部分的太阳有效辐射值和冠层以下部分的太阳有效辐射值，然后通过式（2）计算出冠层光合有效辐射值LI。During the collection, the solar effective radiation value above the canopy and the solar effective radiation value below the canopy were collected respectively, and then the photosynthetically active radiation value LI of the canopy was calculated by formula (2).
本发明所建模型采用了多元线性回归技术，它是一种较为广泛使用的多元校正方法，它通过对自变量权重的优化，提高回归模型的解释能力和预测效果，能较好地解决多变量的线性回归问题，采用所述多元线性回归建模方法建立模型，能够保证所述模型的精确性。The model built by the present invention adopts multiple linear regression technology, which is a widely used multiple correction method. It improves the explanatory ability and prediction effect of the regression model by optimizing the weight of independent variables, and can better solve the problem of multiple variables. For the linear regression problem, using the multiple linear regression modeling method to establish a model can ensure the accuracy of the model.
本发明还提供了一种基于多传感器技术的葡萄水分胁迫诊断系统，包括：The present invention also provides a grape water stress diagnosis system based on multi-sensor technology, comprising:
用于采集葡萄样本冠层在近红外波段的单色图像的多光谱成像仪；A multispectral imager for acquiring monochromatic images of grape sample canopies in the near-infrared band;
用于采集葡萄样本冠层的热红外图像的热红外成像仪；A thermal infrared imager for acquiring thermal infrared images of grape sample canopies;
用于采集葡萄样本冠层光合有效辐射值的线状光合有效辐射测定仪；A linear photosynthetically active radiation measuring instrument for collecting photosynthetically active radiation values of grape sample canopies;
以及用于处理所述单色图像、热红外图像和冠层光合有效辐射值并输出所述葡萄样本冠层水分胁迫等级的计算机。and a computer for processing the monochromatic image, thermal infrared image and canopy photosynthetically active radiation value and outputting the canopy water stress level of the grape sample.
所述多光谱成像仪与计算机之间通过图象采集卡传输数据。Data is transmitted between the multispectral imager and the computer through an image acquisition card.
所述的多光谱成像仪优选为美国Redlake公司的MS3100DuncanCamera，可实现对不同波段图像的同步获取，有利于各独立波段图像特征的提取，因无需进行图形配准，也易于实现多光谱图像的像素级运算。The described multispectral imager is preferably the MS3100DuncanCamera of Redlake Corporation in the United States, which can realize synchronous acquisition of images of different bands, which is conducive to the extraction of image features of each independent band, and is also easy to realize the pixel of multispectral images without graphic registration. level operation.
所述的图象采集卡优选为美国National Instrument公司的PCI1424或1428数据采集卡，PCI1424或1428数据采集卡不仅与MS3100DuncanCamera相匹配，同时能满足图像采集通道数、采样率和分辨率等需要。Described image acquisition card is preferably the PCI1424 or 1428 data acquisition card of U.S. National Instrument company, and PCI1424 or 1428 data acquisition card not only matches with MS3100DuncanCamera, can satisfy the needs such as image acquisition channel number, sampling rate and resolution simultaneously.
所述的热红外成像仪采用FLIR SC655热红外成像仪；所述的多光谱成像仪采集图像以及FLIR SC655热红外成像仪采集冠层温度信息所用光源优选为自然光，采用自然光能够使采得的图像光线均匀，与卤素灯等人造光源相比，采用自然光得到的图像能够更好得进行后续的图像预处理等分析，并且无需对光源进行人为的调节等，而且方便田间操作。Described thermal infrared imager adopts FLIR SC655 thermal infrared imager; Described multi-spectral imager collects images and FLIR SC655 thermal infrared imager collects canopy temperature information. The light is uniform. Compared with artificial light sources such as halogen lamps, the images obtained by using natural light can be better analyzed for subsequent image preprocessing, and there is no need for artificial adjustment of the light source, and it is convenient for field operations.
所述多光谱成像仪、FLIR SC655热红外成像仪可通过设置可调节角度、高度、移动底座的三脚架或安装有可调节机械伸展臂高度、角度的车辆等固定装置进行固定，当用于温室内时采用三脚架安装，当用于田间时采用车辆安装。The multi-spectral imager and FLIR SC655 thermal infrared imager can be fixed by a tripod with adjustable angle, height, and mobile base, or a vehicle with adjustable mechanical extension arm height and angle. When used in a greenhouse It is mounted on a tripod when it is used, and it is mounted on a vehicle when it is used in the field.
相对于现有技术，本发明具有以下优点：Compared with the prior art, the present invention has the following advantages:
（1）功能强大，可实现葡萄水分胁迫程度的快速、稳定、非破坏性的诊断，并且做到尽可能地早期检测；(1) With powerful functions, it can realize rapid, stable and non-destructive diagnosis of grape water stress, and achieve early detection as much as possible;
（2）准确度高，整个系统受外界环境干扰小，所建立的模型对水分胁迫等级的预测准确度高。(2) The accuracy is high, the whole system is less disturbed by the external environment, and the established model has high accuracy in predicting the level of water stress.
（3）运算速度快，葡萄水分胁迫诊断模型一旦建立以后，可实现农田葡萄水分胁迫的实时获取。(3) The calculation speed is fast. Once the grape water stress diagnosis model is established, real-time acquisition of grape water stress in farmland can be realized.
（4）使用方便，当检测系统的各组件都连接完毕后，最后的图像采集分析工作通过图像分析处理软件完成。(4) Easy to use. After all the components of the detection system are connected, the final image acquisition and analysis work is completed by image analysis and processing software.
附图说明Description of drawings
图1是模型验证时12个葡萄冠层样本的实测水分等级与拟合的水分百分比间的关系。Figure 1 shows the relationship between the measured moisture level and the fitted moisture percentage of 12 grape canopy samples during model validation.
具体实施方式Detailed ways
本发明用于检测葡萄冠层信息的系统包括多光谱成像仪、热红外成像仪、线状光合有效辐射测定仪和计算机，多光谱成像仪与计算机之间通过图象采集卡传输数据，图像采集卡连接于多光谱成像仪上，多光谱成像仪通过RS-232串口线及图象采集卡数据线和计算机连接，计算机设有图像处理软件。The system for detecting grape canopy information of the present invention comprises a multispectral imager, a thermal infrared imager, a linear photosynthetically active radiation measuring instrument and a computer, and the multispectral imager and the computer transmit data through an image acquisition card, image acquisition The card is connected to the multi-spectral imager, and the multi-spectral imager is connected to the computer through the RS-232 serial line and the data line of the image acquisition card, and the computer is equipped with image processing software.
其中，多光谱成像仪为美国Redlake公司的MS3100Duncan Camera，热红外成像仪型号为FLIR SC655，它们的底部设有可调节角度、高度、移动底座的三脚架，镜头垂直向下采集图像信息，图象采集卡为美国National Instrument公司的PCI1424或1428数据采集卡，多光谱成像仪的图象采集与热红外成像仪的冠层温度采集所用光源为自然光。Among them, the multi-spectral imager is MS3100Duncan Camera from Redlake Company in the United States, and the thermal infrared imager model is FLIR SC655. The bottom of them is equipped with a tripod with adjustable angle, height, and movable base. The lens is vertically downward to collect image information. The card is a PCI1424 or 1428 data acquisition card from National Instrument Corporation of the United States, and the light source used for the image acquisition of the multispectral imager and the canopy temperature acquisition of the thermal infrared imager is natural light.
线状光合有效辐射测定仪型号:ACCUPAR LP-80,CID,Inc.,Vancouver,WA。Linear Photosynthetically Active Radiation Meter Model: ACCUPAR LP-80, CID, Inc., Vancouver, WA.
利用多光谱成像仪获取40个葡萄冠层样本在绿光波段（550nm）、红光波段（650nm）、近红外波段（800nm）三个波段通道的单色图像，单色图像通过图像采集卡传输至计算机，通过计算机上的图像处理软件（Matlab9.0），采用中值滤波法对单色图像进行预处理，预处理后采用二维最大信息熵阈值分割法进行背景分割，计算得到40个葡萄冠层覆盖率值。参照文献：张晓东，毛罕平，左志宇，高鸿燕，孙俊，2011.基于多光谱视觉技术的油菜水分胁迫诊断.农业工程学报，27(3):152-157.中公开的方法。Use the multispectral imager to acquire monochrome images of 40 grape canopy samples in the three band channels of green light band (550nm), red light band (650nm), and near-infrared band (800nm), and the monochrome images are transmitted through the image acquisition card To the computer, through the image processing software (Matlab9.0) on the computer, the monochrome image is preprocessed by the median filter method, and the background is segmented by the two-dimensional maximum information entropy threshold segmentation method after preprocessing, and 40 grapes Canopy cover value. References: Zhang Xiaodong, Mao Hanping, Zuo Zhiyu, Gao Hongyan, Sun Jun, 2011. The method disclosed in Rapeseed Water Stress Diagnosis Based on Multispectral Vision Technology. Journal of Agricultural Engineering, 27(3):152-157.
利用FLIR SC655热红外成像仪采集葡萄冠层的热红外图像，采用FLIR ExaminIR software(FLIR SC655,FLIR systems)处理软件处理热红外图像获得葡萄冠层温度信息，并采集实时的大气温度信息，将获得的葡萄冠层温度值与大气温度的差值作为葡萄冠层温度特征值。Use FLIR SC655 thermal infrared imager to collect thermal infrared images of grape canopy, use FLIR ExaminIR software (FLIR SC655, FLIR systems) to process thermal infrared images to obtain grape canopy temperature information, and collect real-time atmospheric temperature information, will get The difference between the grape canopy temperature value and the atmospheric temperature is used as the characteristic value of the grape canopy temperature.
用线状光合有效辐射测定仪采集葡萄冠层以上及以下部分的太阳辐射值，通过如下公式计算得到待测葡萄冠层的光合有效辐射值，Use a linear photosynthetically active radiation meter to collect the solar radiation values above and below the grape canopy, and calculate the photosynthetically active radiation value of the grape canopy to be measured by the following formula,
LI=[1-(冠层以下部分的太阳有效辐射值)*(冠层以上部分的太阳有效辐射值)-1]其中LI为葡萄的冠层光合有效辐射值；LI=[1-(the solar effective radiation value of the part below the canopy)*(the solar effective radiation value of the above part of the canopy) -1 ] wherein LI is the canopy photosynthetic active radiation value of grape;
其中将28个葡萄样本的冠层覆盖率值，冠层温度特征值，冠层光合有效辐射值用于模型的校正。Among them, the canopy coverage rate value, canopy temperature characteristic value and canopy photosynthetic active radiation value of 28 grape samples are used for model correction.
在建模过程中，以28个葡萄样本的冠层覆盖率值、冠层温度信息、冠层光合有效辐射值作为模型的输入，以葡萄水分胁迫等级为输出，对冠层覆盖率值，冠层温度特征值，冠层光合有效辐射值与葡萄水分胁迫等级之间进行基于多元线性回归理论的数值拟合，可得以下模型：In the modeling process, the canopy coverage value, canopy temperature information, and canopy photosynthetically active radiation value of 28 grape samples were used as the input of the model, and the grape water stress level was used as the output. The numerical fitting based on multiple linear regression theory between the canopy photosynthetically active radiation value and grape water stress level can get the following model:
Y＝-0.03x1+3.43x2+2.25x3 Y＝-0.03x 1 +3.43x 2 +2.25x 3
其中，x1，x2，x3分别为葡萄冠层覆盖率值，葡萄冠层温度特征值，葡萄冠层光合有效辐射值；Y为葡萄水分胁迫等级。Among them, x 1 , x 2 , and x 3 are the grape canopy coverage rate value, grape canopy temperature characteristic value, and grape canopy photosynthetic active radiation value respectively; Y is the grape water stress level.
以其余12个葡萄冠层样本作为待测冠层样本，将其冠层覆盖率值，冠层温度特征值以及冠层光合有效辐射值代入上述模型，得出拟合水分等级值；同时，利用水分传感器获取12个待测葡萄冠层样本的实测水分等级，分为50%，75%和100%，如表1所示：Taking the remaining 12 grape canopy samples as canopy samples to be tested, the canopy coverage rate value, canopy temperature characteristic value and canopy photosynthetic active radiation value were substituted into the above model to obtain the fitted moisture level value; at the same time, using The moisture sensor obtains the measured moisture levels of 12 grape canopy samples to be tested, which are divided into 50%, 75% and 100%, as shown in Table 1:
表1Table 1
建立以上所述12个待测葡萄冠层样本的拟合水分百分比与水分等级间的相关关系模型，如图1所示，拟合水分含量值与实测水分等级间的决定系数是0.927，模型预测偏差是0.073。由此证明本发明所建模型提高了预测精度，且检测方法简便。Establish the correlation model between the fitted moisture percentage and the moisture grade of the above-mentioned 12 grape canopy samples to be measured, as shown in Figure 1, the coefficient of determination between the fitted moisture content value and the measured moisture grade is 0.927, and the model predicts The deviation is 0.073. This proves that the model built by the present invention improves the prediction accuracy, and the detection method is simple and convenient.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103674998A (en) * | 2013-10-24 | 2014-03-26 | 北京大学深圳研究生院 | Plant health state detection method based on thermal imaging technology |
| CN104568750A (en) * | 2015-01-19 | 2015-04-29 | 无锡桑尼安科技有限公司 | Grape moisture stress detection method based on image processing |
| CN106546567A (en) * | 2016-10-31 | 2017-03-29 | 浙江大学 | Plant drouhgt stress diagnostic method and device based on imaging-PAM technology |
| CN106556569A (en) * | 2016-11-11 | 2017-04-05 | 北京林业大学 | A kind of plant hydropenia condition diagnosing method based on infrared imaging |
| CN108469434A (en) * | 2018-04-11 | 2018-08-31 | 山东农业大学 | A kind of monitoring fruit tree whether the device and method of water shortage |
| CN112488230A (en) * | 2020-12-07 | 2021-03-12 | 中国农业大学 | Crop water stress degree judging method and device based on machine learning |
| CN112586347A (en) * | 2020-12-21 | 2021-04-02 | 扬州大学 | Screening method of drought-resistant rice variety |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001272373A (en) * | 2000-03-24 | 2001-10-05 | Shikoku Natl Agricultural Experiment Station Maff | Method for diagnosing water stress in plants |
| CN100573146C (en) * | 2003-04-11 | 2009-12-23 | 中国科学院地理科学与资源研究所 | A kind of determining crops water forcing index device and assay method thereof |
| JP2012137354A (en) * | 2010-12-27 | 2012-07-19 | Ehime Prefecture | Plant water stress evaluation device and method |
-
2013
- 2013-04-25 CN CN201310148957.6A patent/CN103278503B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001272373A (en) * | 2000-03-24 | 2001-10-05 | Shikoku Natl Agricultural Experiment Station Maff | Method for diagnosing water stress in plants |
| CN100573146C (en) * | 2003-04-11 | 2009-12-23 | 中国科学院地理科学与资源研究所 | A kind of determining crops water forcing index device and assay method thereof |
| JP2012137354A (en) * | 2010-12-27 | 2012-07-19 | Ehime Prefecture | Plant water stress evaluation device and method |
Non-Patent Citations (1)
| Title |
|---|
| 程麒等: "《基于红外热图像的棉花花铃期水分胁迫指数与光合参数的关系》", 《新疆农业科学》, vol. 49, no. 6, 30 June 2012 (2012-06-30), pages 999 - 1006 * |
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| CN104568750A (en) * | 2015-01-19 | 2015-04-29 | 无锡桑尼安科技有限公司 | Grape moisture stress detection method based on image processing |
| CN105181647A (en) * | 2015-01-19 | 2015-12-23 | 无锡桑尼安科技有限公司 | Image processing-based grape water stress detection method |
| CN105181695A (en) * | 2015-01-19 | 2015-12-23 | 无锡桑尼安科技有限公司 | Image processing-based grape water stress detection method |
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