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CN105651713B - A kind of green vegetables leaf chlorophyll quantitative detecting method based on computer image analysis - Google Patents

A kind of green vegetables leaf chlorophyll quantitative detecting method based on computer image analysis Download PDF

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CN105651713B
CN105651713B CN201511024044.9A CN201511024044A CN105651713B CN 105651713 B CN105651713 B CN 105651713B CN 201511024044 A CN201511024044 A CN 201511024044A CN 105651713 B CN105651713 B CN 105651713B
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王德海
孙宇露
朱国建
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Zhejiang University of Technology ZJUT
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Abstract

一种基于计算机图像分析的青菜叶片叶绿素定量检测方法，包括如下步骤：根据青菜叶片叶绿素含量变化引起叶片颜色变化，采用数码相机或扫描仪获取青菜叶片图像，用计算机图像技术获取图像中叶片的颜色参数值(L、a、b、ΔE)，并采用传统的分光光度计法测定对应叶片的叶绿素含量，通过采用不同的函数模型对叶片的颜色参数值与叶绿素含量之间的关系进行拟合，从而构建出基于颜色参数值的叶绿素含量预测模型，即只要获取叶片的颜色值，输入模型，即可实现对叶片叶绿素含量的测定。

A method for quantitatively detecting chlorophyll in green vegetables leaves based on computer image analysis, comprising the following steps: according to the change in leaf color caused by changes in the chlorophyll content of green vegetables leaves, using a digital camera or a scanner to obtain an image of the green vegetables leaves, and using a computer image technology to obtain the color of the leaves in the image The parameter values (L, a, b, ΔE) were determined, and the chlorophyll content of the corresponding leaves was measured by the traditional spectrophotometer method. Therefore, a prediction model of chlorophyll content based on the color parameter value is constructed, that is, as long as the color value of the leaf is obtained and the model is input, the determination of the chlorophyll content of the leaf can be realized.

Description

A kind of green vegetables leaf chlorophyll quantitative detecting method based on computer image analysis
Technical field
The present invention relates to a kind of green vegetables blade Determination of Chlorophyll content quantitative detection methods, belong to the test of crops physical signs Method.
Background technique
The common method of measuring chlorophyll content is spectrophotometer method at present, i.e., shreds blade weighing, with largely having Solvent such as acetone, ethyl alcohol extract leaf chlorophyll for a long time, then with spectrophotometric determination extracting solution in 645nm and Then the OD value of 663nm calculates chlorophyll content using Arnon formula.
Still an alternative is that directly reading the value of chlorophyll content in leaf blades with chlorophyll meter measurement blade.Most It is the SPAR-502 that Japanese Minolta company produces, it is that have absorption in visible light region specific wavelength position according to leaf chlorophyll The characteristics of paddy and reflection peak, the light that a branch of known strength is emitted by the instrument of production are radiated at the blade position that need to be measured, Tested blade specific wavelength absorptivity and reflectivity are detected to calculate chlorophyll content in leaf blades.Using traditional spectrophotometer Measurement chlorophyll content need to be extracted with organic solvent, not only break up sample, and process is cumbersome, relatively time-consuming.Chlorophyll It counts expensive, is not suitable for the research of some bases and measurement of the production unit to gardening product chlorophyll.
Since plant leaf color is the external manifestation of blade Determination of Chlorophyll content, pass through building number using the color parameter of plant It learns model and calculates chlorophyll content as a kind of new method.Reported document has following feature: a. uses colour difference meter Carry out color parameter measurement.Since colour difference meter has certain want to the size of sample, shape, color uniformity, surface smoothness Ask, and colour difference meter volume is larger, instrument price is expensive, suitable for scientific research but be not suitable for base research and production unit make With.B. current chlorophyll content detection model is based on the color systems such as RGB more, and precision of prediction is not high.C. utilization reported in the literature Computer picture establish chlorophyll content be based on more the crops such as detection model cotton foundation because green vegetables plant type, leaf morphology, There are larger differences with above-mentioned crop for surface texture etc., so the prediction model of existing chlorophyll content is not particularly suited for The measurement of green vegetables chlorophyll content in leaf blades.
Summary of the invention
The present invention will overcome problems of the prior art, provide a kind of based on the quick, lossless of color parameter value Green vegetables blade method for measuring chlorophyll content.
The purpose of the present invention is achieved through the following technical solutions: being caused according to the variation of green vegetables chlorophyll content in leaf blades Leaf color variation obtains green vegetables leaf image using digital camera or scanner, is obtained in image with computer image technology The color parameter value (L*, a*, b*, Δ E) of blade, CIE-LAB color specification system is international standard colour system.It uses space coordinate L ﹑ a ﹑ b value indicates.Origin is L=50, a=0, b=0.L* represent brightness (0-100) 0 be black, 100 be white.Coordinate A* indicates red (+) and green (-), and b* indicates yellow (+) and blue (-), Δ E=(Δ L*2+Δa*2+Δb*2)1/2, concentrate Colour system three elements are embodied, can more fully reflect the variation of color three-dimensional space.And use traditional spectrophotometric Meter method measures the chlorophyll content of corresponding blade, by using different function models to the color parameter value and chlorophyll of blade Relationship between content is fitted, so that the chlorophyll content prediction model based on color parameter value is constructed, as long as obtaining The color value of blade is taken, the measurement to chlorophyll content in leaf blades can be realized in input model.
The specific implementation step of the method for the present invention is described in further detail below:
1. leaf image obtains.Leaf digital image is obtained by scanner or digital camera, uses Canon PowerShotA610 digital camera shoot green vegetable leaf direct picture when, using smooth blank sheet of paper as background, camera lens vertically downward, Camera lens is 20cm or so with a distance from target object, and digital machine flash lamp is set to closed state, and pixel is 5,000,000, and phase is arranged Machine is under M-mode, acquisition parameters 1/400, F8.0, ISO100.White balance is automatic, and adjustment lens focus is 20cm.Using JPG Format stores image and incoming computer.
2. leaf image color parameter measures.Leaf digital image image processing software (such as Photoshop that will acquire CS6 etc.) color value that extracts entire blade, does using magic wand tool in Adobe Photoshop CS6 image processing software Green vegetables blade constituency (not comprising the stem arteries and veins part in dish leaf), setting pen tip size are 8 pixels.Then it executes in filter tool Fuzzy averaging order equalizes all pixels parameter value in blade constituency, is read in messagewindow using the Eyedropper tool L*, a*, b* value, and color difference Δ E=(Δ L*2+Δa*2+Δb*2)1/2。
3. the experimental determination of chlorophyll content in leaf blades.After obtaining green vegetables leaf image, sample is shredded immediately, point It does not weigh 0.20g on an electronic balance, is then placed in and fills 25ml mixed liquor (1:1 is prepared by volume for acetone, dehydrated alcohol) Tool plug test tube in, when being placed in that derect seething to leaf tissue bleaches completely under dark condition, with ultraviolet-visible spectrophotometric Meter is scanned different chlorophyll extracting solutions within the scope of 600-700nm, wavelength accuracy 0.2nm.In 645nm and 663nm Place measures and records its OD value, and makees Duplicate Samples, is averaged.
Chlorophyll content, which calculates, utilizes Arnon formula In formula: V is the final volume of leaching liquor；W is fresh weight, and D645, D663 are 645nm and 663nm OD value.
4. the building of chlorophyll content prediction model.Using different functions model y=Ax+B, y=A/x+B, y=Alnx+B With ln (lny)=Alnx+B, A, B be model of fit coefficient, respectively in step 2 measure each color parameter (x) of blade (a*, B*, Δ E) and step 3 in measure chlorophyll content (y) between relationship analysis is fitted by origin8.0 software, pass through Compare the coefficient of determination R of fit equation2, it is as follows to construct 2 prediction models:
Ln (lnC chl)=- 4.0547 ln b*+12.6085 (n=10, R2=0.9925**) --- --- --- (1)
C chl=-0.1412 Δ E+3.4516 (n=10, R2=0.9940) --- --- --- --- (2)
Wherein C chl is chlorophyll content (mg/g), and n is sample size, R2The coefficient of determination, * * indicate that model of fit has pole Significant correlation.
5. the measurement of sample to be tested chlorophyll content.Method measurement leaf image color ginseng is described according to above-mentioned steps 1-2 Color parameter b* or Δ E, are substituted into the chlorophyll content prediction model constructed in step 4, i.e., exportable leaf by number b*, Δ E respectively Chlorophyll contents.
The invention has the advantages that 1. is quick, easy, time saving, laborsaving, flexible operation is simple, does not need to destroy sample, to leaf Piece is not damaged, is not required to chemical reagent, save the cost.2. method proposed by the present invention need to only shoot one completely to a blade The image of blade both can get blade part chlorophyll content, also can get entire chlorophyll content in leaf blades, reduce because of a measurement The low problem of caused measurement accuracy；Evaluated error caused by due to operator's measuring point chooses difference is reduced, precision is higher.
Detailed description of the invention
Fig. 1 is the building schematic diagram of the invention based on b* chlorophyll content prediction model.
Fig. 2 is the building schematic diagram of the invention based on Δ E chlorophyll content prediction model.
Fig. 3 is the inspection schematic diagram of prediction model in embodiment 1
Fig. 4 is the inspection schematic diagram of prediction model in embodiment 2
Specific embodiment
Below by specific embodiment, the present invention is further illustrated, but protection scope of the present invention is not limited in This.
Embodiment 1
On October 10th, 2015,10:30, took 10 groups of blueness that the city Desheng road market of farm produce is bought under the city of Hangzhou, Zhejiang province Dish leaf piece sample, green vegetables kind are " May is slow (WYM, Shanghai local varieties) ", utilize Canon PowerShotA610 number phase When machine shoots green vegetable leaf direct picture, green vegetable leaf to be measured is placed in camera bellows using blank sheet of paper as background, digital camera is placed in camera bellows On the peephole of top, camera bellows standard lamp source (D65) to be opened by digital machine flash lamp and is set to closed state, pixel is 5,000,000, Camera is set under M-mode, acquisition parameters 1/400, F8.0, ISO100.White balance is automatic, and adjustment lens focus is 20cm. It is carried out under the weather condition of ceiling unlimited when taking pictures, camera is shot perpendicular to blade, and shadow-free on blade.Using JPG lattice Formula stores image and incoming computer, the leaf digital image Adobe Photoshop CS6 image processing software that will acquire Middle magic wand tool is done green vegetable leaf piece constituency (not comprising the stem arteries and veins part in dish leaf), and setting pen tip size is 8 pixels.Then it holds All pixels parameter value in blade constituency is equalized, is read using the Eyedropper tool by the fuzzy averaging order in row filter tool Take the b value in colouring information window.
Input model built:
Ln (lnC chl)=- 4.0547ln b*+12.6085 (n=10, R2=0.9925**)
Obtain respectively 10 groups of green vegetables blade sample chlorophyll content predicted values be 3.45,2.92,2.57,2.07,1.57, 3.49,2.77,2.16,1.78,1.63mg/g；It is real that 10 groups of green vegetables blade sample chlorophyll contents are obtained using spectrophotometer method Measured value is 3.48,2.89,2.54,2.11,1.59,3.6,2.66,2.19,1.78,1.63mg/g.
Embodiment 2
On October 15th, 2015,10:30, took 10 groups of green vegetables blade samples, and green vegetables kind is " short anti-blueness ", utilizes Canon When PowerShotA610 digital camera shoots green vegetable leaf direct picture, green vegetable leaf to be measured is placed in camera bellows using blank sheet of paper as back Scape, digital camera are placed on the peephole above camera bellows, open camera bellows standard lamp source (D65), digital machine flash lamp is set to Closed state, pixel are 5,000,000, and camera is arranged under M-mode, acquisition parameters 1/400, F8.0, ISO100.White balance is certainly Dynamic, adjustment lens focus is 20cm.It being carried out under the weather condition of ceiling unlimited when taking pictures, camera is shot perpendicular to blade, and Shadow-free on blade.Image and incoming computer, the leaf digital image Adobe that will acquire are stored using JPG format Magic wand tool does green vegetable leaf piece constituency (not comprising the stem arteries and veins part in dish leaf) in Photoshop CS6 image processing software, Setting pen tip size is 8 pixels.Then the fuzzy averaging order in filter tool is executed, all pixels in blade constituency are joined Digital average reads L*, a*, b* value in colouring information window, and color difference Δ E using the Eyedropper tool.
Input model built:
C chl=-0.1412 Δ E+3.4516 (n=10, R2=0.9940)
Obtain respectively 10 groups of green vegetables blade sample chlorophyll content predicted values be 2.34,2.78,2.36,3.56,1.34, 2.67,1.67,4.54,3.11,2.49mg/g；And 10 groups of green vegetables blade sample chlorophyll contents are obtained using spectrophotometer method Measured value is 2.45,2.72,2.5,3.47,1.36,2.70,1.81,4.6,3.15,2.61mg/g.

Claims (1)

1.一种基于计算机图像分析的青菜叶片叶绿素定量检测方法，包括如下步骤：1. a method for quantitative detection of chlorophyll in leaves of green vegetables based on computer image analysis, comprising the steps: (1).叶片图像获取；通过扫描仪或数码相机获取叶片数字图像，用CanonPowerShotA610数码相机拍摄青菜叶正面图像时，以平整的白纸作为背景，镜头垂直向下，镜头离目标物体距离为20cm，将数码机闪光灯设置于关闭状态，像素为500万，设置相机在M模式下，拍摄参数为1/400，F8.0，ISO100；白平衡自动，调整镜头焦距为20cm；采用JPG格式存储图像并传入计算机；(1) Leaf image acquisition; obtain the leaf digital image through a scanner or a digital camera. When taking a frontal image of a green cabbage leaf with a CanonPowerShotA610 digital camera, a flat white paper is used as the background, the lens is vertically downward, and the distance between the lens and the target object is 20cm , set the flash of the digital machine to off, the pixel is 5 million, set the camera in M mode, the shooting parameters are 1/400, F8.0, ISO100; the white balance is automatic, and the focal length of the lens is adjusted to 20cm; the image is stored in JPG format and transferred to the computer; (2).叶片图像颜色参数测定；将获取的叶片数字图像用图像处理软件提取整个叶片的颜色值L*、a*、b*，CIE-LAB表色系是国际标准制表色系统；它用空间坐标L﹑a﹑b值表示；原点坐标为L＝50,a＝0,b＝0；L*代表亮度，L*的取值范围是0-100，其中0为黑色、100为白色；坐标a*表示红色+和绿色-，b*表示黄色+和蓝色-，应用Adobe Photoshop CS6图像处理软件中魔术棒工具做青菜叶片选区，不包含菜叶上的茎脉部分，设置笔尖大小为8像素；然后执行滤镜工具中的模糊平均命令，将叶片选区内的所有像素参数值平均化，使用吸管工具读取信息窗口中的L*、a*、b*，并计算色差ΔE＝(ΔL*2+Δa*2+Δb*2)1/2；(2) Determination of leaf image color parameters; the obtained digital image of the leaf is used to extract the color values L*, a*, b* of the entire leaf with image processing software. The CIE-LAB color system is an international standard color system; it It is represented by the spatial coordinates L, a and b; the origin coordinates are L=50, a=0, b=0; L* represents the brightness, and the value range of L* is 0-100, where 0 is black and 100 is white. ;Coordinate a* means red+ and green-, b* means yellow+ and blue-, use the magic wand tool in Adobe Photoshop CS6 image processing software to make a selection of green leaves, excluding the stem veins on the leaves, set the size of the pen tip It is 8 pixels; then execute the blur average command in the filter tool to average all pixel parameter values in the leaf selection area, use the eyedropper tool to read L*, a*, b* in the information window, and calculate the color difference ΔE= (ΔL* 2 +Δa* 2 +Δb* 2 ) 1/2 ; (3).叶片叶绿素含量的实验室测定；在获取青菜叶片图像之后，立即将样品剪碎，分别在电子天平上称取0.20g，然后放入盛有25ml混合液的具塞试管中，所述的混合液由丙酮、无水乙醇按体积比1:1配制，置于黑暗条件下直接浸提至叶片组织完全变白时，用紫外可见光分光光度计在600—700nm范围内对不同叶绿素提取液进行扫描，波长精度为0.2nm；在645nm和663nm处测量并记录其光密度值，并作平行样，求其平均值；(3) Laboratory determination of chlorophyll content in leaves; after obtaining the image of green cabbage leaves, cut the samples into pieces immediately, weigh 0.20g on an electronic balance, and put them into a stoppered test tube containing 25ml of the mixture. The mixed solution is prepared by acetone and absolute ethanol in a volume ratio of 1:1, and is directly leached under dark conditions until the leaf tissue turns completely white, and different chlorophylls are extracted with a UV-Vis spectrophotometer in the range of 600-700 nm. The liquid is scanned, and the wavelength accuracy is 0.2 nm; the optical density values are measured and recorded at 645 nm and 663 nm, and parallel samples are made to obtain the average value; 叶绿素含量计算利用Arnon公式: 式中:V是浸提液的最终体积,W为叶片鲜重,D645、D663为645nm和663nm的光密度值，叶绿素总含量的单位是mg/g；The chlorophyll content was calculated using the Arnon formula: In the formula: V is the final volume of the extract, W is the fresh weight of the leaf, D645 and D663 are the optical density values at 645 nm and 663 nm, and the unit of the total chlorophyll content is mg/g; (4).叶绿素含量预测模型的构建；采用不同函数模型y＝Ax+B、y＝A/x+B、y＝Alnx+B和ln(lny)＝Alnx+B，A、B为拟合模型的系数，分别对步骤2中测定叶片各颜色参数(x)与步骤3中测定叶绿素含量(y)之间的关系通过origin8.0软件进行拟合分析，叶片各颜色参数(x)是a*、b*、ΔE，通过比较拟合方程的决定系数R2，构建2个预测模型如下:(4) Construction of chlorophyll content prediction model; different function models y=Ax+B, y=A/x+B, y=Alnx+B and ln(lny)=Alnx+B, A and B are fitting The coefficients of the model, the relationship between the color parameters (x) of the leaves determined in step 2 and the chlorophyll content (y) determined in step 3 were fitted and analyzed by the origin8.0 software, and the color parameters (x) of the leaves were a *, b*, ΔE, by comparing the coefficient of determination R 2 of the fitting equation, two prediction models are constructed as follows: ln(lnC chl)＝-4.0547ln b*+12.6085，n＝10，R2＝0.9925**---------------(1)ln(lnC chl)=-4.0547ln b*+12.6085, n=10, R 2 =0.9925**---------------(1) C chl＝-0.1412ΔE+3.4516，n＝10，R2＝0.9940——————————(2)C chl=-0.1412ΔE+3.4516, n=10, R 2 =0.9940———————————(2) 其中C chl为叶绿素含量，单位是mg/g，n为样本量，R2决定系数，**表示拟合模型具有极显著的相关性；Among them, C chl is the chlorophyll content, the unit is mg/ g , n is the sample size, R is the coefficient of determination, and ** indicates that the fitted model has a very significant correlation; (5).待测样品叶绿素含量的测定；按照上述步骤1-2描述方法测定叶片图像颜色参数b*、ΔE，将颜色参数b*或ΔE分别代入步骤4中构建的叶绿素含量预测模型，即可输出叶绿素含量。(5) Determination of the chlorophyll content of the sample to be tested; the leaf image color parameters b* and ΔE are determined according to the methods described in the above steps 1-2, and the color parameters b* or ΔE are respectively substituted into the chlorophyll content prediction model constructed in step 4, namely Chlorophyll content can be output.
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CN108719024A (en) * 2018-06-13 2018-11-02 苏州若依玫信息技术有限公司 A kind of automation implant system based on chlorophyll detection
CN109142235A (en) * 2018-09-10 2019-01-04 南京林业大学 Analysis method and device
CN109270069A (en) * 2018-11-05 2019-01-25 东北林业大学 A kind of cotyledon greening degree fast appraisement method based on color difference analysis
CN109655466A (en) * 2019-01-08 2019-04-19 中国矿业大学 A kind of spoil coal carrying rate online test method and device based on machine vision
CN110378890A (en) * 2019-07-23 2019-10-25 中国科学院东北地理与农业生态研究所 A kind of prediction technique of hayashishita resource plant above-ground organs N content
CN111426634A (en) * 2019-11-21 2020-07-17 四川省中医药科学院 Method for detecting the content of main chemical components of Salvia miltiorrhiza powder according to its color
CN111122579A (en) * 2020-01-17 2020-05-08 中国农业科学院都市农业研究所 Method for measuring total content of flavone in lettuce leaves
CN111896482A (en) * 2020-07-17 2020-11-06 上海应用技术大学 A kind of detection method of Chlorella chlorophyll content
CN113324921A (en) * 2021-05-28 2021-08-31 中国农业大学 Construction method and application of astragalus seed chlorophyll content determination model
CN115372373A (en) * 2022-08-11 2022-11-22 中国农业科学院农产品加工研究所 Method for rapidly determining appearance form of fruits and vegetables in drying process based on image analysis technology

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7110575B2 (en) * 2002-08-02 2006-09-19 Eastman Kodak Company Method for locating faces in digital color images
GB2418555A (en) * 2004-09-23 2006-03-29 Mitsubishi Electric Inf Tech Representing an image using descriptors based on colour information
CN101718683B (en) * 2009-11-10 2011-02-02 中国农业大学 Method for fast detection of chlorophyll content in leaf blades, modeling method and detection method
CN102072882B (en) * 2010-04-16 2013-07-24 姚孝明 Method for quantitatively analyzing plant nutrient content index based on leaf image characteristics
CN102519886A (en) * 2011-12-19 2012-06-27 浙江大学 Method for detecting contents of chlorophyll a and carotinoid in crop laminas

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