WO2021035857A1 - 黄瓜叶片叶绿素叶黄素比率分布快速检测系统及检测方法 - Google Patents

黄瓜叶片叶绿素叶黄素比率分布快速检测系统及检测方法 Download PDF

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WO2021035857A1
WO2021035857A1 PCT/CN2019/107982 CN2019107982W WO2021035857A1 WO 2021035857 A1 WO2021035857 A1 WO 2021035857A1 CN 2019107982 W CN2019107982 W CN 2019107982W WO 2021035857 A1 WO2021035857 A1 WO 2021035857A1
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light source
chlorophyll
leaf
lutein
characteristic wavelength
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PCT/CN2019/107982
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English (en)
French (fr)
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石吉勇
邹小波
郭志明
张文
李志华
黄晓玮
李文亭
胡雪桃
翟晓东
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江苏大学
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Priority to CH01568/20A priority Critical patent/CH716709B1/de
Priority to GB2018553.4A priority patent/GB2591577B/en
Publication of WO2021035857A1 publication Critical patent/WO2021035857A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G01N2021/0106—General arrangement of respective parts
    • G01N2021/0112—Apparatus in one mechanical, optical or electronic block
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N2021/8466—Investigation of vegetal material, e.g. leaves, plants, fruits
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture

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  • the invention belongs to the technical field of biological component detection, and relates to a rapid detection system and a detection method for cucumber leaf chlorophyll and xanthophyll ratio distribution.
  • Chlorophyll and lutein are important pigments in cucumber leaves.
  • the content and ratio of chlorophyll and lutein not only determine the appearance and color of the leaves, but also are closely related to the nutritional status of the plant.
  • Traditional physical and chemical analysis methods such as spectrophotometer and high performance liquid chromatography can simultaneously detect the content of chlorophyll and lutein in the sampling area of leaves, and then calculate the ratio of chlorophyll and lutein in the sampling area.
  • traditional physical and chemical analysis methods cannot achieve continuous spatial sampling of the same leaf, and cannot detect the corresponding chlorophyll and xanthophyll content at each point of the leaf, so that the leaf surface distribution detection of its content and ratio cannot be achieved.
  • Hyperspectral imaging technology contains not only the image information of the sample, but also the spectral information of each pixel in the sample image.
  • Related invention patents use the sensitive characteristics of pixel point spectrum to sample component content, and use hyperspectral images to realize the distribution detection of chlorophyll content in leaves and moisture content in vinegar.
  • the existing hyperspectral image acquisition system includes electronically controlled translation stage, hyperspectral camera and other components, the hardware structure is complicated and expensive; at the same time, due to the huge amount of hyperspectral image data, the data processing is too time-consuming, and the data processing steps are cumbersome, resulting in high Spectral image technology is difficult to achieve rapid detection of sample component distribution.
  • the present invention proposes a rapid detection system and detection method for the leaf surface distribution of cucumber leaf chlorophyll and lutein ratio.
  • the present invention first provides a cucumber leaf characteristic wavelength image acquisition system, which acquires a two-dimensional transmission image of the leaf at the characteristic wavelength under the condition of zero mechanical action and zero displacement of the sample.
  • the leaf characteristic wavelength image acquisition system includes a cucumber leaf characteristic wavelength image acquisition system, including a light source module for providing light; an imaging module for collecting two-dimensional transmission images; a control module for controlling the light source module And an imaging module, and calculate the proportion distribution of chlorophyll and lutein according to the collected images; wherein the light source module includes a light source cavity and a miniature LED linear light source unit arranged in the light source cavity; the inner wall of the light source cavity is provided with diffuse reflection coating Layer; The surface of the micro LED linear light source unit includes a diffuse reflection area.
  • the top of the light source cavity is provided with an opening.
  • the light source module further includes a light source cavity support column and a housing, and the light source cavity is fixed in the housing through the light source cavity support column.
  • micro LED linear light source units There are multiple and even number of the micro LED linear light source units, preferably six.
  • the micro LED linear array light source unit includes a plurality of LED lights uniformly arranged in a linear manner in the same plane, preferably six.
  • the light source module further includes a support rod and an arched reflector unit.
  • the micro LED linear array light source unit is supported by the support rod and is symmetrically distributed in the light source cavity around the axis of the light source cavity in the same plane; the LED linear array light source units are opposite to each other.
  • An arched reflective unit is installed on the inner wall of the light source cavity.
  • the supporting rods and the arch-shaped reflective unit are wrapped by diffuse reflection paint.
  • the foliar characteristic wavelength image acquisition system further includes an imaging module, the imaging module includes a camera, a lens, a sample stage, a support column, and an imaging window; the sample stage is fixed above the light source cavity through the support column, and the sample stage The part is provided with an imaging window; wherein the lens is arranged on the camera; the camera and the imaging window are both arranged on the light path of the light source outlet of the light source cavity.
  • the imaging module includes a camera, a lens, a sample stage, a support column, and an imaging window; the sample stage is fixed above the light source cavity through the support column, and the sample stage The part is provided with an imaging window; wherein the lens is arranged on the camera; the camera and the imaging window are both arranged on the light path of the light source outlet of the light source cavity.
  • the light emitted by the micro LED linear array light source unit is reflected by the arched reflecting unit, the diffuse reflection coating on the inner wall of the light source cavity, the micro LED linear array light source unit, and the LED lamp support rod, and then enters the lens through the imaging window.
  • the imaging window is filled with light-transmitting glass.
  • the foliar characteristic wavelength image acquisition system also includes a control module, and the control module includes a controller and a computer.
  • the controller is connected to the computer through a data line; the LED lights in the micro LED linear array light source unit is connected to the controller through a data line; the camera is connected to the controller through a data line.
  • the present invention also provides a rapid detection method for leaf surface distribution of cucumber leaf chlorophyll-to-xanthophyll ratio, which includes: establishing a chlorophyll content model and a lutein content model to establish a chlorophyll and lutein content detection model; and collecting leaf characteristics Two-dimensional transmission characteristic image at the wavelength; according to the collected two-dimensional transmission characteristic image and the established chlorophyll and lutein content detection model, the chlorophyll and lutein ratio distribution map is obtained.
  • Sample Chlorophyll and Xanthophyll content detection Use high performance liquid chromatography to detect the chlorophyll content Y_1_1, Y_1_2, whil, Y_1_n-1, Y_1_n and lutein content Y_2_1, Y_2_2, whil, Y_2_n-1, Y_2_n corresponding to n leaves ;
  • the two-dimensional transmission image of the leaf at the characteristic wavelengths ⁇ a , ⁇ b , ⁇ c , ⁇ d , ⁇ e , ⁇ f is collected under the condition of zero mechanical action of the system and zero displacement of the sample.
  • the controller controls the LED lights in the micro LED linear array light source unit to be turned off through the data line; the controller sequentially controls the LED lights in the micro LED linear array light source unit to turn on or off alternately, and control at the same time
  • the camera shoots the two-dimensional transmission characteristic images I_ ⁇ a, I_ ⁇ b, I_ ⁇ c, I_ ⁇ d, I_ ⁇ e, I_ ⁇ f at the characteristic wavelengths ⁇ a , ⁇ b , ⁇ c , ⁇ d , ⁇ e , and ⁇ f of the blade in the light-emitting state .
  • the controller stores the two-dimensional transmission characteristic images I_ ⁇ a , I_ ⁇ b , I_ ⁇ c , I_ ⁇ d , I_ ⁇ e , and I_ ⁇ f taken by the camera into the computer.
  • the blades can be collected at characteristic wavelengths ⁇ a , ⁇ b under the premise of zero mechanical action of the system and zero displacement of the sample.
  • the two-dimensional transmission image is used as the model input to quickly calculate the leaf chlorophyll content distribution map, the lutein content distribution map, and the chlorophyll-to-xanthophyll ratio distribution map, which overcomes the difficulty of the existing hyperspectral image technology to quickly detect the leaf composition and ratio distribution The insufficiency.
  • FIG. 1 Schematic diagram of the image acquisition system of the characteristic wavelength of the blade.
  • FIG. 2 is a chlorophyll chlorophyll distribution diagram, a lutein leaf surface distribution diagram, and a chlorophyll-to-xanthophyll ratio leaf surface distribution diagram of cucumber leaves obtained in an embodiment of the present invention; wherein (a) a cucumber leaf chlorophyll chlorophyll distribution diagram; b) Foliar distribution map of lutein of cucumber leaves; (c) Foliar distribution map of chlorophyll and lutein ratio of cucumber leaves.
  • Embodiment 1 A cucumber leaf feature wavelength image acquisition system
  • the invention provides a cucumber leaf characteristic wavelength image acquisition system, which collects a two-dimensional transmission image of the cucumber leaf at the characteristic wavelength under the condition of zero mechanical action and zero sample displacement.
  • the leaf characteristic wavelength image acquisition system includes a cucumber leaf characteristic wavelength image acquisition system, including a light source module for providing light; an imaging module for collecting two-dimensional transmission images; a control module for controlling the light source module And an imaging module, and calculate the proportion distribution of chlorophyll and lutein according to the collected images; wherein the light source module includes a light source cavity 1 and a micro LED linear array light source unit 4 arranged in the light source cavity; the inner wall of the light source cavity 1 is provided with Diffuse reflection coating 2; the surface of the micro LED linear array light source unit 4 includes a diffuse reflection area.
  • the top of the light source cavity 1 is provided with an opening.
  • the light source module further includes a light source cavity supporting column 3 and a housing 14, and the light source cavity 1 is fixed in the housing 14 through the light source cavity supporting column 3.
  • micro LED linear light source units 4 There are multiple and even number of the micro LED linear light source units 4, preferably six.
  • the micro LED linear light source unit 4 includes a plurality of LED lights uniformly arranged in a linear manner in the same plane, preferably six.
  • the light source module further includes a support rod 11 and an arched reflecting unit 13.
  • the micro LED linear array light source unit 4 is supported by the support rod 11 and is symmetrically distributed in the light source cavity 1 around the axis of the light source cavity 1 in the same plane;
  • An arc-shaped reflective unit 13 is installed on the inner wall of the light source cavity 1 opposite to the LED linear light source unit 4.
  • the support rod 11 and the arch-shaped reflective unit 13 are wrapped by diffuse reflection paint.
  • the foliar characteristic wavelength image acquisition system also includes an imaging module.
  • the imaging module includes a camera 16, a lens 17, a sample stage 18, a support column 19, and an imaging window 20; the sample stage 18 is fixed to the light source cavity through the support column 19 Above, an imaging window 20 is arranged between the sample stage 18; wherein the lens 17 is arranged on the camera 16; both the camera 16 and the imaging window 20 are arranged on the light path of the light source exit of the light source cavity 1.
  • the light emitted by the micro LED linear array light source unit 4 is reflected by the arched reflective unit 13, the inner wall of the light source cavity, the diffuse reflection coating 2, the micro LED linear array light source unit 4, and the LED lamp support rod 11, and then enters the lens 17 through the imaging window 20 .
  • the imaging window 20 is filled with light-transmitting glass.
  • the foliar characteristic wavelength image acquisition system also includes a control module, the control module includes a controller 15 and a computer 21; wherein the controller 15 is connected to the computer 21 through a data line 12; the miniature LED linear array light source unit 4 The LED lamp inside is connected to the controller 15 through the data line 12; the camera 16 is connected to the controller 15 through the data line 12.
  • Example 2 Non-destructive detection of leaf surface distribution of cucumber chlorophyll-to-xanthophyll ratio
  • the cucumber leaves are placed on the surface of the imaging window 20, and the controller 15 controls all the LED lights in the 6 miniature LED linear light source units 4 to be turned off through the data line 12;
  • the controller 15 sequentially controls the LED lights in the miniature LED linear light source unit 4 to emit or turn off alternately, and sequentially collect the two-dimensional projection characteristic images of chlorophyll and lutein at different characteristic wavelengths.
  • the specific operations are as follows:
  • the controller 15 controls all of the lights ⁇ 550 LED 5 light-emitting state, and controls the camera 16 photographing characteristic wavelength [lambda] cucumber leaves in a two-dimensional image transmission characteristics I_ ⁇ 550 at 550;
  • the controller 15 controls all of the lights ⁇ 550 LED 5 is turned off and the control of all the LED lamp 6 at 639 [lambda] emission state, and controls the camera 16 photographing cucumber leaves dimensional transmission wavelength characteristic image I_ ⁇ ⁇ 639 at feature 639;
  • the controller 15 controls all of the lights ⁇ 639 LED 6 in a closed state and all the control lights ⁇ 701 LED 7 light-emitting state, and controls the camera 16 photographing the wavelength [lambda] at 701 cucumber leaves the transmission characteristics of the two-dimensional image feature I_ ⁇ 701;
  • the controller 15 controls all of the lights ⁇ 701 LED 7 is turned off and the control of all the LED lamp 8 is 419 [lambda] light emission state, and controls the camera 16 photographing characteristic wavelength [lambda] cucumber leaves in a two-dimensional image transmission characteristics I_ ⁇ 419 at 419;
  • the controller 15 controls all of the lights ⁇ 419 LED 8 is turned off and the control of all the LED lamp 9 at 440 [lambda] emission state, and controls the camera 16 photographing characteristic wavelength [lambda] cucumber leaves in a two-dimensional image transmission characteristics I_ ⁇ 440 at 440;
  • the controller 15 controls all of the ⁇ 440 LED lamp 9 is turned off and the control of all the LED lamps 10 in 469 [lambda] emission state, and controls the camera 16 photographing characteristic wavelength [lambda] cucumber leaves in a two-dimensional image transmission characteristics I_ ⁇ 469 at 469;
  • the controller 15 controls all the ⁇ 469 LED lights 10 to be turned off and stores the two-dimensional transmission characteristic images I_ ⁇ 550 , I_ ⁇ 639 , I_ ⁇ 701 , I_ ⁇ 419 , I_ ⁇ 440 , and I_ ⁇ 469 taken by the camera 16 into the computer 21.
  • the characteristic wavelength image acquisition of the leaf to be measured Place the leaf to be measured in the sample area of the characteristic image acquisition system and keep the sample position unchanged, and use the characteristic image acquisition system to collect the leaf to be measured at the characteristic wavelength ⁇ 550 , ⁇ 639 , ⁇ 701, ⁇ 419, ⁇ 440, ⁇ transmission characteristics of the two-dimensional image I_ ⁇ 550 at 469, I_ ⁇ 639, I_ ⁇ 701, I_ ⁇ 419, I_ ⁇ 440, I_ ⁇ 469.
  • the chlorophyll-to-xanthophyll ratio distribution map I_Y_3 is shown in Figure 2(c).
  • the gray value of the pixel in Figure 2(c) represents the proportion of chlorophyll and lutein at the pixel, and the detection of the distribution map of the proportion of chlorophyll and lutein is realized.

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Abstract

一种叶片叶绿素叶黄素比率分布快速检测系统及检测方法；在系统在零机械动作及样品零位移情况下采集叶片在特征波长处的二维透射图像；叶面特征波长图像采集系统包括光源模块，光源模块包括光源腔（1）和设置于光源腔内的微型LED线阵光源单元（4）；光源腔（1）为球形且中空，内壁设有漫反射涂层（2）；微型LED线阵光源单元（4）有多个且为偶数设置，表面覆盖有漫反射涂料；该系统通过采集叶片在特征波长处二维透射图像，快速计算出叶片叶绿素含量分布图、叶黄素含量分布图以及叶绿素叶黄素比率分布图，克服了难以快速检测叶片组分及比率分布的不足。

Description

黄瓜叶片叶绿素叶黄素比率分布快速检测系统及检测方法 技术领域
本发明属于生物组分检测技术领域，涉及一种黄瓜叶片叶绿素叶黄素比率分布快速检测系统及检测方法。
背景技术
叶绿素和叶黄素是黄瓜叶片内的重要色素，叶绿素叶黄素含量及其比率不仅决定了叶片的外观颜色，而且与植株的营养状态密切相关。传统的理化分析法如分光光度计法、高效液相色谱法可以同时检测叶片采样区域的叶绿素及叶黄素含量，进而计算出采样区域叶绿素叶黄素的比率。然而，传统理化分析方法无法实现同一片叶片的空间连续采样，无法检测出叶片每一点处对应的叶绿素和叶黄素含量，从而无法实现其含量及比率的叶面分布检测。
高光谱图像技术既包含样品的图像信息，又包含样品图像中每个像素点的光谱信息。相关发明专利利用像素点光谱对样品组分含量的敏感特性，利用高光谱图像实现了叶片中叶绿素含量、醋醅中水分含量的分布检测。现有的高光谱图像采集系统包含电控平移台、高光谱相机等部件，硬件构成复杂且价格昂贵；同时，由于高光谱图像数据量庞大，数据处理过耗时、数据处理步骤繁琐，导致高光谱图像技术难以实现样品组分分布的快速检测。
发明内容
为了克服上述不足之一，本发明提出了一种黄瓜叶片叶绿素叶黄素比率的叶面分布快速检测系统及检测方法。
本发明首先提供一种黄瓜叶片的叶面特征波长图像采集系统，在所述系统在零机械动作及样品零位移情况下采集叶片在特征波长处的二维透射图像。
所述叶面特征波长图像采集系统包括一种黄瓜叶片叶面特征波长图像采集系统，包括光源模块，用于提供光线；成像模块，用于采集二维透射图像；控制模块，用于控制光源模块和成像模块，并根据采集的图像计算叶绿素叶黄素比例分布；其中所述光源模块包括光源腔和设置于光源腔内的微型LED线阵光源单元；所述光源腔的内壁设有漫反射涂层；所述微型LED线阵光源单元表面包括漫反射区域。
所述光源腔的顶部设有开口。
所述光源模块还包括光源腔支撑柱和外壳，所述光源腔通过光源腔支撑柱固定在外壳内。
所述微型LED线阵光源单元有多个且为偶数设置，优选为6个。
所述微型LED线阵光源单元包括在同一平面内以直线方式均匀排列的多个 LED灯，优选为6个。
所述光源模块还包括支撑杆、拱形反光单元，所述微型LED线阵光源单元通过支撑杆的支撑在同一平面内围绕光源腔轴心对称分布于光源腔的内部；LED线阵光源单元相对的光源腔内壁上安装有拱形反光单元。
所述支撑杆、拱形反光单元被漫反射涂料包裹。
所述叶面特征波长图像采集系统还包括成像模块，所述成像模块包括相机、镜头、样品台、支撑柱、成像窗口；所述样品台通过支撑柱固定于光源腔上方，所述样品台间部位设置有成像窗口；其中所述镜头设置于相机上；所述相机、成像窗口均设置于光源腔光源出口的光路上。
所述微型LED线阵光源单元发出的光被拱形反光单元、光源腔内壁漫反射涂层、微型LED线阵光源单元、LED灯支撑杆反射后经过成像窗口进入镜头。
所述成像窗口由透光玻璃填充而成。
所述叶面特征波长图像采集系统还包括控制模块，所述控制模块包括控制器和计算机。其中所述控制器通过数据线与计算机相连；所述微型LED线阵光源单元内的LED灯通过数据线与控制器相连；所述相机通过数据线与控制器相连。
本发明还提供一种黄瓜叶片叶绿素叶黄素比率的叶面分布快速检测方法，包括：建立叶绿素含量模型和叶黄素含量模型，以此建立叶绿素和叶黄素含量检测模型；采集叶片在特征波长处的二维透射特征图像；根据采集的二维透射特征图像和建立的叶绿素和叶黄素含量检测模型获得叶绿素叶黄素比例分布图。
具体操作如下：
建立叶绿素和叶黄素含量检测模型：选取叶片叶绿素的3个特征波长λ a,λ b,λ c和叶片叶黄素的3个特征波长λ d,λ e,λ f建立叶绿素含量模型Y_1＝F1(X_λ a,X_λ b,X_λ c)和叶黄素含量模型Y_2＝F2(X_λ d,X_λ e,X_λ f)，具体操作如下：
样本光谱信息采集：以波长H1nm为起点，以ΔH为波长采样间隔，利用光谱仪在m个波长下采集n片叶片对应的透射光谱信息X 1,X 2,……,X n-1,X n；其中第i(i＝1,2,……，n-1,n)片叶片对应的光谱信息X i中第j(j＝1,2,……，m-1,m)个波长对应的光谱响应值为X i_λ (H1+j* ΔH)；
样本叶绿素叶黄素含量检测：利用高效液相色谱检测n片叶片对应的叶绿素含量Y_1_1，Y_1_2，……，Y_1_n-1，Y_1_n以及叶黄素含量Y_2_1，Y_2_2，……，Y_2_n-1，Y_2_n；
叶绿素含量模型构建：将n片叶片对应的透射光谱信息X i包含的m个特征波长对应的光谱响应值X i_λ (H1+j*ΔH)作为自变量，将n片叶片对应的叶绿素含量Y_1_k(k＝1,2,……，n-1,n) 作为因变量，经遗传算法特征波长优选后结合线性回归方法建立包含3个特征波长λ a,λ b,λ c的叶绿素含量模型Y_1＝F1(X_λ a,X_λ b,X_λ c)＝k a*X_λ a+k b*X_λ b+k c*X_λ c+C 1,其中Y_1代表叶片叶绿素含量，X_λ a、X_λ b、X_λ c为叶片在特征波长λ a、λ b、λ c处的光谱响应值，且λ a、λ b、λ c∈{λ (H1+j*ΔH),j＝1,2,……,m-1,m}；k a、k b、k c分别为叶绿素含量模型中X_λ a、X_λ b、X_λ c对应的回归系数，C 1为叶绿素含量模型中的常数项；
叶黄素含量模型构建：将n片叶片对应的透射光谱信息X i包含的m个特征波长对应的光谱响应值X i_λ (H1+j*ΔH)作为自变量，将n片叶片对应的叶黄素含量Y_2_v(v＝1,2,……，n-1,n)作为因变量，经遗传算法特征波长优选后结合线性回归方法建立包含3个特征波长λ d,λ e,λ f的叶黄素含量模型Y_2＝F2(X_λ d,X_λ e,X_λ f)＝k d*X_λ d+k e*X_λ e+k f*X_λ f+C 2,其中Y_2代表叶片叶黄素含量，X_λ d、X_λ e、X_λ f为叶片在特征波长λ d、λ e、λ f处的光谱响应值，且λ d、λ e、λ f∈{λ (H1+j*ΔH),j＝0,1,2,……,m-1,m}；k d、k e、k f分别为叶绿素含量模型中X_λ d、X_λ e、X_λ f对应的回归系数，C 2为叶黄素含量模型中的常数项。
叶片特征波长图像采集：
利用叶片的叶面特征波长图像采集系统，在系统零机械动作及样品零位移情况下采集叶片在特征波长λ a、λ b、λ c、λ d、λ e、λ f处的二维透射图像，具体操作如下：
将叶片放置于成像窗口，控制器通过数据线控制微型LED线阵光源单元内的LED灯处于关闭状态；控制器依次控制微型LED线阵光源单元内的LED灯交替处于发光或关闭状态，同时控制相机拍摄发光状态下叶片在特征波长λ a、λ b、λ c、λ d、λ e、λ f处的二维透射特征图像I_λ a、I_λ b、I_λ c、I_λ d、I_λ e、I_λ f。
控制器将相机拍摄的二维透射特征图像I_λ a,I_λ b,I_λ c,I_λ d,I_λ e,I_λ f存入计算机。
待测叶片叶绿素叶黄素比率叶面分布图检测：
将得到的叶绿素和叶黄素的二维投射特征图像代入步骤S1得到的叶绿素和叶黄素含量模型中，得到叶绿素含量二维叶面分布图和叶黄素含量二维叶面分布图，将叶绿素含量二维叶面分布图除以叶黄素含量二维叶面分布图，得到叶绿素叶黄素比例分布图。
本发明的有益效果：
利用构建包含λ a、λ b、λ c、λ d、λ e、λ f波长光源的图像采集系统，可在系统零机械动作及样品零位移前提下，采集叶片在特征波长λ a、λ b、λ c、λ d、λ e、λ f处二维透射图像，借助叶绿素含量模型以及叶黄素含量模型，以特征波长λ a、λ b、λ c、λ d、λ e、λ f处二维透射图像作为模型输入，可快速计算出叶片叶绿素含量分布图、叶黄素含量分布图以及叶绿素叶黄素比率分布图，克服了现有高光谱图像技术难以快速检测叶片组分及比率分布的不足。
附图说明
图1叶片特征波长图像采集系统示意图。
图中，1-光源腔、2-漫反射涂层、3-支撑柱、4-微型LED线阵光源单元、5-λ a LED灯、6-λ b LED灯、7-λ c LED灯、8-λ d LED灯、9-λ e LED灯、10-λ f LED灯、11-支撑杆、12-数据线、13-拱形反光单元、14-矩形外壳、15-控制器、16-相机、17-镜头、18-样品台、19-支撑柱、20-成像窗口、21-计算机。
图2为本发明实施例中得到的黄瓜叶片的叶绿素叶绿分布图、叶黄素叶面分布图、以及叶绿素叶黄素比例叶面分布图；其中(a)黄瓜叶片的叶绿素叶绿分布图；(b)黄瓜叶片的叶黄素叶面分布图；(c)黄瓜叶片的叶绿素叶黄素比例叶面分布图。
具体实施方式
为了使本发明的目的、技术方案和优势更加清楚，使本领域技术人员更好的理解本发明的技术方案，下面将结合本发明的附图和具体实施例对本发明的技术方案更加清楚、完成的描述。显然，所描述的实施例是本发明的一部分实施方式，而不是全部，基于本发明的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施方式，都属于本发明保护的范围。
实施例1：一种黄瓜叶面特征波长图像采集系统
本发明提供的一种黄瓜叶片的叶面特征波长图像采集系统，在所述系统在零机械动作及样品零位移情况下采集黄瓜叶片在特征波长处的二维透射图像。
所述叶面特征波长图像采集系统包括一种黄瓜叶片叶面特征波长图像采集系统，包括光源模块，用于提供光线；成像模块，用于采集二维透射图像；控制模块，用于控制光源模块和成像模块，并根据采集的图像计算叶绿素叶黄素比例分布；其中所述光源模块包括光源腔1和设置于光源腔内的微型LED线阵光源单元4；所述光源腔1的内壁设有漫反射涂层2；所述微型LED线阵光源单元4表面包括漫反射区域。
所述光源腔1的顶部设有开口。
所述光源模块还包括光源腔支撑柱3和外壳14，所述光源腔1通过光源腔支撑柱3固定在外壳14内。
所述微型LED线阵光源单元4有多个且为偶数设置，优选为6个。
所述微型LED线阵光源单元4包括在同一平面内以直线方式均匀排列的多个LED灯，优选为6个。
所述光源模块还包括支撑杆11、拱形反光单元13，所述微型LED线阵光源单元4通过支撑杆11的支撑在同一平面内围绕光源腔1轴心对称分布于光源腔1的内部；LED线阵光源单元4相对的光源腔1内壁上安装有拱形反光单元13。
所述支撑杆11、拱形反光单元13被漫反射涂料包裹。
所述叶面特征波长图像采集系统还包括成像模块，所述成像模块包括相机16、镜头17、样品台18、支撑柱19、成像窗口20；所述样品台18通过支撑柱19固定于光源腔上方，所述样品台18间部位设置有成像窗口20；其中所述镜头17设置于相机16上；所述相机16、成像窗口20均设置于光源腔1光源出口的光路上。
所述微型LED线阵光源单元4发出的光被拱形反光单元13、光源腔内壁漫反射涂层2、微型LED线阵光源单元4、LED灯支撑杆11反射后经过成像窗口20进入镜头17。
所述成像窗口20由透光玻璃填充而成。
所述叶面特征波长图像采集系统还包括控制模块，所述控制模块包括控制器15和计算机21；其中所述控制器15通过数据线12与计算机21相连；所述微型LED线阵光源单元4内的LED灯通过数据线12与控制器15相连；所述相机16通过数据线12与控制器15相连。
实施例2：黄瓜叶绿素叶黄素比率的叶面分布无损检测
S1.建立叶绿素和叶黄素含量检测模型：
选取黄瓜叶片叶绿素的3个特征波长λ a,λ b,λ c，构建叶绿素含量模型Y_1＝F1(X_λ a,X_λ b,X_λ c)；同时选取黄瓜叶片叶黄素的3个特征波长λ d,λ e,λ f，构建叶黄素含量模型Y_2＝F2(X_λ d,X_λ e,X_λ f)，具体模型的构建过程如下：
(1)样本光谱信息采集：以波长H1＝400nm为起点，以ΔH＝1.928nm为波长采样间隔，利用光谱仪采集黄瓜叶片在778个波长λ (400+1*1.928),λ (400+2*1.928),……,λ (400+777*1.928),λ (400+778*1.928)下采集100片黄瓜叶片对应的透射光谱信息X 1,X 2,……,X 99,X 100；其中第i(i＝1,2,……，99,100)片黄瓜叶片对应的光谱信息X i中第j(j＝1,2,……，777,778)个波长对应的光谱响应值为X i_λ (400+j*1.928)；
(2)样本叶绿素和黄素含量检测：利用高效液相色谱(LC-20A，岛津，日本)检测步骤(1)中100片黄瓜叶片对应的叶绿素含量Y_1_1，Y_1_2，……，Y_1_99，Y_1_100以及叶黄素含量Y_2_1，Y_2_2，……，Y_2_99，Y_2_100；
(3)叶绿素含量模型构建：将100片黄瓜叶片对应的透射光谱信息X i包含的778个特征波长对应的光谱响应值X i_λ (400+j*1.928)作为自变量，将100片黄瓜叶片对应的叶绿素含量Y_1_k(k＝1,2,……，99,100)作为因变量，经遗传算法特征波长优选后结合线性回归方法建立包含3个特征波长λ a＝λ 550＝550nm,λ b＝λ 639＝639nm,λ c＝λ 701＝701nm的叶绿素含量模型Y_1＝F1(X_λ 550,X_λ 639,X_λ 701)＝0.4186*X_λ 550+0.0173*X_λ 639-0.3824*X_λ 701+1.4921,其中Y_1代表黄瓜叶片叶绿素含量，X_λ 550,X_λ 639,X_λ 701为黄瓜叶片在特征波长λ a＝550nm,λ b＝639nm,λ c＝701nm处的光谱响应值；
(4)叶黄素含量模型构建：将100片黄瓜叶片对应的透射光谱信息X i包含的778个特征波长对应的光谱响应值X i_λ (400+j*1.928)作为自变量，将100片黄瓜叶片对应的叶黄素含量Y_2_v(v＝1,2,……，99,100)作为因变量，经遗传算法特征波长优选后结合线性回归方法建立包含3个特征波长λ d＝λ 419＝419nm,λ e＝λ 440＝440nm,λ f＝λ 469＝469nm的叶黄素含量模型Y_2＝F2(X_419,X_440,X_469)＝1.7391*X_λ 419+0.0024*X_λ 440-0.6953*X_λ 469+0.9736,其中Y_2代表黄瓜叶片叶黄素含量，X_λ 419,X_λ 440,X_λ 469为黄瓜叶片在特征波长λ d＝419nm,λ e＝440nm,λ f＝469nm处的光谱响应值；
S2.黄瓜叶片特征波长图像采集：
(1)将黄瓜叶片放置于成像窗口20的表面，控制器15通过数据线12控制6个微型LED线阵光源单元4内全部的LED灯处于关闭状态；
(2)控制器15依次控制微型LED线阵光源单元4内的LED灯交替处于发光或关闭状态，依次采集不同特征波长下的叶绿素和叶黄素的二维投射特征图像，具体的操作如下：
控制器15控制全部λ 550LED灯5处于发光状态，并控制相机16拍摄黄瓜叶片在特征波长λ 550处的二维透射特征图像I_λ 550；
控制器15控制全部λ 550LED灯5处于关闭状态并控制全部λ 639LED灯6处于发光状态，并控制相机16拍摄黄瓜叶片在特征波长λ 639处的二维透射特征图像I_λ 639；
控制器15控制全部λ 639LED灯6处于关闭状态并控制全部λ 701LED灯7处于发光状态，并控制相机16拍摄黄瓜叶片在特征波长λ 701处的二维透射特征图像I_λ 701；
控制器15控制全部λ 701LED灯7处于关闭状态并控制全部λ 419LED灯8处于发光状态，并控制相机16拍摄黄瓜叶片在特征波长λ 419处的二维透射特征图像I_λ 419；
控制器15控制全部λ 419LED灯8处于关闭状态并控制全部λ 440LED灯9处于发光状态，并控制相机16拍摄黄瓜叶片在特征波长λ 440处的二维透射特征图像I_λ 440；
控制器15控制全部λ 440LED灯9处于关闭状态并控制全部λ 469LED灯10处于发光状态，并控制相机16拍摄黄瓜叶片在特征波长λ 469处的二维透射特征图像I_λ 469；
(3)控制器15控制全部λ 469LED灯10处于关闭状态并将相机16拍摄的二维透射特征图像I_λ 550,I_λ 639,I_λ 701,I_λ 419,I_λ 440,I_λ 469存入计算机21。
S3.待测叶片叶绿素叶黄素比率叶面分布图检测：
将得到的叶绿素和叶黄素的二维投射特征图像代入步骤S1得到的叶绿素和叶黄素含量模型中，得到叶绿素含量二维叶面分布图和叶黄素含量二维叶面分布图，将叶绿素含量二维叶面分布图除以叶黄素含量二维叶面分布图，得到叶绿素叶黄素比例分布图。
实施例3.待测叶片叶绿素叶黄素比率叶面分布图检测：
(1)待测叶片特征波长图像采集：将待测叶片放置于特征图像采集系统的样本区域并保持样本位置不变，利用特征图像采集系统采集待测叶片在特征波长λ 550,λ 639,λ 701,λ 419,λ 440,λ 469处的二维透射特征图像I_λ 550,I_λ 639,I_λ 701,I_λ 419,I_λ 440,I_λ 469。
(2)叶绿素含量二维叶面分布图检测：将待测叶片在特征波长λ 550,λ 639,λ 701,处的特征图像I_λ 550,I_λ 639,I_λ 701代入叶绿素含量模型Y_1＝F1(X_λ 550,X_λ 639,X_λ 701)，得到叶绿素含量二维叶面分布图I_Y_1＝F1(I_λ 550,I_λ 639,I_λ 701)＝0.4186*I_λ 550+0.0173*I_λ 639-0.3824*I_λ 701+1.4921，其中叶绿素含量二维叶面分布图I_Y_1如图2(a)所示。图2(a)中像素点的灰度值代表了像素点处叶片的叶绿素含量，实现了叶绿素含量二维叶面分布图的检测。
(3)叶黄素含量二维叶面分布图检测：将待测叶片在特征波长λ 419,λ 440,λ 469处的特征图像I_λ 419,I_λ 440,I_λ 469代入叶黄素含量模型Y_2＝F2(X_λ 419,X_λ 440,X_λ 469)，得到叶黄素含量二维叶面分布图I_Y_2＝F2(I_λ 419,I_λ 440,I_λ 469)＝1.7391*I_λ 419+0.0024*I_λ 440-0.6953*I_λ 469+0.9736，其中叶黄素含量二维叶面分布图I_Y_2如图2(b)所示。图2(b)中像素点的灰度值代表了像素点处叶片的叶黄素含量，实现了叶黄素含量二维叶面分布图的检测。
(4)叶绿素叶黄素比例分布图检测：将叶绿素含量二维叶面分布图I_Y_1除以叶黄素含量二维叶面分布图I_Y_2，得到叶绿素叶黄素比例分布图I_Y_3＝I_Y_1/I_Y_2，其中叶绿素叶黄素比例分布图I_Y_3如图2(c)所示。图2(c)中像素点的灰度值代表了像素点处叶绿素叶黄素比例，实现了叶绿素叶黄素比例分布图的检测。

Claims (15)

  1. 一种黄瓜叶片叶面特征波长图像采集系统，其特征在于，包括
    光源模块，用于提供光线；
    成像模块，用于采集二维透射图像；
    控制模块，用于控制光源模块和成像模块，并根据采集的图像计算叶绿素叶黄素比例分布；
    其中所述光源模块包括光源腔(1)和设置于光源腔内的微型LED线阵光源单元(4)；所述光源腔(1)的内壁设有漫反射涂层(2)；所述微型LED线阵光源单元(4)表面包括漫反射区域。
  2. 根据权利要求1所述的叶面特征波长图像采集系统，其特征在于，所述光源腔(1)的顶部设有开口。
  3. 根据权利要求1所述的叶面特征波长图像采集系统，其特征在于，所述光源模块还包括光源腔支撑柱(3)和外壳(14)，所述光源腔(1)通过光源腔支撑柱(3)固定在外壳(14)内。
  4. 根据权利要求1所述的叶面特征波长图像采集系统，其特征在于，所述微型LED线阵光源单元(4)有多个且为偶数设置。
  5. 根据权利要求1所述的叶面特征波长图像采集系统，其特征在于，所述微型LED线阵光源单元(4)包括在同一平面内以直线方式均匀排列的多个LED灯，优选为6个。
  6. 根据权利要求1所述的叶面特征波长图像采集系统，其特征在于，所述光源模块还包括支撑杆(11)、拱形反光单元(13)，所述微型LED线阵光源单元(4)通过支撑杆(11)的支撑在同一平面内围绕光源腔(1)轴心对称分布于光源腔(1)的内部；LED线阵光源单元(4)相对的光源腔(1)内壁上安装有拱形反光单元(13)。
  7. 根据权利要求6所述的叶面特征波长图像采集系统，其特征在于，所述支撑杆(11)、拱形反光单元(13)被漫反射涂料包裹。
  8. 根据权利要求6所述的叶面特征波长图像采集系统，其特征在于，所述叶面特征波长图像采集系统还包括成像模块，所述成像模块包括相机(16)、镜头(17)、样品台(18)、支撑柱(19)、成像窗口(20)；所述样品台(18)通过支撑柱(19)固定于光源腔上方，所述样品台(18)间部位设置有成像窗口(20)；其中所述镜头(17)设置于相机(16)上；所述相机(16)、成像窗口(20)均设置于光源腔(1)光源出口的光路上。
  9. 根据权利要求8所述的叶面特征波长图像采集系统，其特征在于，所述微型LED线阵光源单元(4)发出的光被拱形反光单元(13)、光源腔内壁漫反射涂层(2)、微型LED线阵光源单元(4)、LED灯支撑杆(11)反射后经过成像窗口(20)进入镜头(17)。
  10. 根据权利要求8所述的叶面特征波长图像采集系统，其特征在于，所述成像窗口(20)由透光玻璃填充而成。
  11. 根据权利要求8所述的叶面特征波长图像采集系统，其特征在于，所述叶面特征波长图像采集系统还包括控制模块，所述控制模块包括控制器(15)和计算机(21)；其中所述控制器(15)通过数据线(12)与计算机(21)相连；所述微型LED线阵光源单元(4)内的LED灯通过数据线(12)与控制器(15)相连；所述相机(16)通过数据线(12)与控制器(15)相连。
  12. 一种黄瓜叶片叶绿素叶黄素比率的叶面分布快速检测方法，其特征在于，所述方法包括如下步骤：
    建立叶绿素含量模型和叶黄素含量模型，以此建立叶绿素和叶黄素含量检测模型；
    采集叶片在特征波长处的二维透射特征图像；
    根据采集的二维透射特征图像和建立的叶绿素和叶黄素含量检测模型获得叶绿素叶黄素比例分布图。
  13. 根据权利要求12所述的一种黄瓜叶片叶绿素叶黄素比率的叶面分布快速检测方法，其特征在于，所述建立叶绿素和叶黄素含量检测模型的方法为：
    根据叶片叶绿素含量与叶片在叶绿素特征波长的光谱响应值建立的叶绿素含量模型，以及根据叶片叶黄素含量与叶片在叶黄素特征波长的光谱响应值建立的叶黄素含量模型，建立叶绿素和叶黄素含量检测模型。
  14. 根据权利要求12所述的一种黄瓜叶片叶绿素叶黄素比率的叶面分布快速检测方法，其特征在于，所述采集叶片在特征波长处的二维透射特征图像具体操作为：
    在预设条件下采集叶片在不同特征波长处的二维透射图像，将叶片放置于成像窗口，控制器通过数据线控制微型LED线阵光源单元内的LED灯处于关闭状态；控制器依次控制微型LED线阵光源单元内的LED灯交替处于发光或关闭状态，同时控制相机拍摄发光状态下叶片在特征波长处的二维透射特征图像。
  15. 根据权利要求12所述的一种黄瓜叶片叶绿素叶黄素比率的叶面分布快速检测方法，其特征在于，所述叶绿素叶黄素比例分布图的获取方法为：
    将得到的叶绿素和叶黄素的二维投射特征图像代入得到的叶绿素和叶黄素含量模型中，得到叶绿素含量二维叶面分布图和叶黄素含量二维叶面分布图，将叶绿素含量二维叶面分布图除以叶黄素含量二维叶面分布图，得到叶绿素叶黄素比例分布图。
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