WO2013133171A1 - Method for sorting seeds and seed-sorting apparatus - Google Patents

Method for sorting seeds and seed-sorting apparatus Download PDF

Info

Publication number
WO2013133171A1
WO2013133171A1 PCT/JP2013/055704 JP2013055704W WO2013133171A1 WO 2013133171 A1 WO2013133171 A1 WO 2013133171A1 JP 2013055704 W JP2013055704 W JP 2013055704W WO 2013133171 A1 WO2013133171 A1 WO 2013133171A1
Authority
WO
WIPO (PCT)
Prior art keywords
seed
spectrum
seeds
selection target
selection
Prior art date
Application number
PCT/JP2013/055704
Other languages
French (fr)
Japanese (ja)
Inventor
菅沼　寛
美代子 藤本
陽子 五十嵐
康貴 副田
美香 池田
Original Assignee
住友電気工業株式会社
住化農業資材株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 住友電気工業株式会社, 住化農業資材株式会社 filed Critical 住友電気工業株式会社
Publication of WO2013133171A1 publication Critical patent/WO2013133171A1/en

Links

Images

Classifications

    • 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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47—Scattering, i.e. diffuse reflection
    • G01N21/4738—Diffuse reflection, e.g. also for testing fluids, fibrous materials
    • 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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
    • 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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • 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
    • 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
    • G01N21/85—Investigating moving fluids or granular solids
    • G01N2021/8592—Grain or other flowing solid samples
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/129—Using chemometrical methods

Definitions

  • the present invention relates to a seed selection method and a seed selection apparatus.
  • an apparatus for classifying seeds according to appearance (color of seeds), an apparatus for classifying seeds according to specific gravity of seeds, and the like are known. Furthermore, in recent years, methods for measuring the amount of a specific chemical substance contained in seeds by irradiating the seeds with near infrared light and estimating the characteristics of the seeds from the results have been studied (for example, patents). Reference 1).
  • the present invention has been made in view of the above, and an object of the present invention is to provide a seed selection method and a seed selection apparatus capable of easily and non-destructively selecting crops and tree seeds according to practical characteristics. To do.
  • a seed selection method is a seed selection method for performing selection according to practical characteristics for each seed, and a wavelength of 1200 nm to 2400 nm with respect to a selection target seed that is a selection target.
  • the selection target seeds are selected into two or more groups based on the spectral shape of the diffuse reflection spectrum obtained by irradiating near-infrared light in a band of at least 10 nm among the bands.
  • seed selection method since the selection is performed based on the spectral shape of the diffuse reflection spectrum obtained by irradiating near-infrared light in the wavelength band of at least 10 nm, the seeds are not destroyed and simple. Seeds can be selected by various methods.
  • a difference spectrum is calculated from the reference spectrum which is a diffuse reflection spectrum of the seed of the same variety as the selection target seed. It can be set as the aspect which classify
  • a group in which the seed is selected based on the spectral shape of the diffuse reflection spectrum of the seed having a predetermined practical characteristic is examined in advance, and the seed to be selected is selected into the same group as the seed having the predetermined practical characteristic. It can also be set as the aspect which determines the practical characteristic of the seed for selection according to whether it is.
  • the seed selection device diffuses the selection target seed, which is a selection target, with a light source unit that irradiates near infrared light in a band of at least 10 nm out of a wavelength band of 1200 nm to 2400 nm, and the seed to be selected.
  • An acquisition unit that acquires a diffuse reflection spectrum by reflected near-infrared light, and an analysis unit that selects a selection target seed into one of two or more groups based on the spectrum shape of the diffuse reflection spectrum acquired by the acquisition unit; It is characterized by providing.
  • the seed selection method and seed selection apparatus which can perform the selection of the crop and the seed of a tree according to a practical characteristic nondestructively easily are provided.
  • the seed sorting device 1 performs nondestructive inspection on the practical characteristics of seeds 3 (separation target seeds: FIG. 1 shows the placement positions of the seeds 3) dispersedly placed on the belt conveyor 2, and sorts based on the characteristics. It is a device for doing.
  • the selection factors of the seed selection device 1 according to the present embodiment include seed germination vigor and seedling rate, seed origin (wild species, cultivated species, production area, etc.), presence or absence of processing by chemicals, etc. In the embodiment, these are referred to as practical characteristics. Practical characteristics are considered to be affected by the amount and alteration of the components contained in the seed 3, but it is difficult to judge only by the appearance.
  • the seed sorting apparatus 1 includes a light source unit 10 (light source unit), a detection unit 20 (acquisition unit), and an analysis unit 30 (analysis unit).
  • the light source unit 10 irradiates measurement light having a certain wavelength band toward a predetermined irradiation area A1 on the belt conveyor 2.
  • the wavelength range of the measurement light emitted by the light source unit 10 is a near-infrared region depending on the type of the seed 3 and the like, and light having a wavelength range of 1200 to 2400 nm is preferably used. Light in a band of 10 nm or more is used for analysis.
  • the light source unit 10 including the light source 11 (SC light source) that generates super continuum (SC) light will be described.
  • the irradiation region A1 is a partial region of the surface (mounting surface 2b) of the belt conveyor 2 on which the seed 3 is placed.
  • the irradiation area A1 extends in the width direction (x-axis direction) perpendicular to the traveling direction 2a (y-axis direction in FIG. 1) of the placement surface 2b, and covers from one end to the other end of the placement surface 2b. This is an area extending in a line.
  • region A1 shall be 10 mm or less.
  • the light source unit 10 includes a light source 11 that emits SC light, an irradiation unit 12, and an optical fiber 13 that connects the light source 11 and the irradiation unit 12.
  • the light source 11 generates SC light as near infrared light. More specifically, the light source 11 that is an SC light source includes a seed light source and a nonlinear medium, inputs light emitted from the seed light source to the nonlinear medium, and broadens the spectrum by a nonlinear optical effect in the nonlinear medium. SC light is output.
  • N-infrared light generated by the light source 11 is incident on one end face of the optical fiber 13. This near-infrared light is guided through the core region of the optical fiber 13 and is emitted from the other end face to the irradiation unit 12.
  • the irradiation unit 12 irradiates near-infrared light (SC light) emitted from the end face of the optical fiber 13 to the irradiation area A1 on which the inspection object 3 is placed. Since the irradiation unit 12 receives near infrared light emitted from the optical fiber 13 and emits it in a one-dimensional line corresponding to the irradiation region A1, a cylindrical lens is preferably used as the irradiation unit 12.
  • the near-infrared light L1 shaped in a line shape in the irradiation unit 12 in this way is irradiated from the irradiation unit 12 to the irradiation region A1.
  • the near-infrared light L1 output from the light source unit 10 is diffusely reflected by the seed 3 placed on the irradiation area A1. A part of the light enters the detection unit 20 as diffusely reflected light L2.
  • the detection unit 20 has a function as a hyperspectral sensor that acquires a hyperspectral image.
  • the hyperspectral image in this embodiment is demonstrated using FIG.
  • FIG. 2 is a diagram for explaining the outline of the hyperspectral image.
  • the hyperspectral image H is an image composed of N pixels P 1 to P N, and one of the pixels P n includes a plurality of intensity data.
  • the following spectral information Sn is included.
  • the intensity data is data indicating a spectral intensity at a particular wavelength (or wavelength band), indicating that in FIG. 2, 15 intensity data are held as the spectral information S n.
  • the hyperspectral image H is characterized by having a plurality of intensity data for each pixel constituting the image, so that a three-dimensional image having both a two-dimensional element as an image and an element as spectral data. Configuration data.
  • the detection unit 20 includes a slit 21, a spectroscope 22, and a light receiving unit 23.
  • the detection unit 20 has a visual field region 20 s extending in a direction (x-axis direction) perpendicular to the traveling direction 2 a of the belt conveyor 2.
  • the visual field region 20s of the detection unit 20 is a linear region included in the irradiation region A1 of the placement surface 2b, and is a region in which the diffusely reflected light L2 that has passed through the slit 21 forms an image on the light receiving unit 23.
  • the slit 21 is provided with an opening in a direction parallel to the extending direction (x-axis direction) of the irradiation region A1.
  • the diffuse reflected light L2 that has entered the slit 21 of the detection unit 20 enters the spectroscope 22.
  • the spectroscope 22 splits the diffusely reflected light L2 in the longitudinal direction of the slit 21, that is, the direction perpendicular to the extending direction of the irradiation area A1 (y-axis direction).
  • the light split by the spectroscope 22 is received by the light receiving unit 23.
  • the light receiving unit 23 includes a light receiving surface in which a plurality of light receiving elements are two-dimensionally arranged, and each light receiving element receives light. As a result, the light receiving unit 23 receives light of each wavelength of the diffusely reflected light L2 reflected at each position along the width direction (x-axis direction) on the belt conveyor 2. Each light receiving element outputs a signal corresponding to the intensity of received light as information on a two-dimensional planar point composed of a position and a wavelength. A signal output from the light receiving element of the light receiving unit 23 is sent from the detection unit 20 to the analysis unit 30 as image data related to the hyperspectral image.
  • the analysis unit 30 obtains the spectrum of the diffuse reflected light L2 from the input signal, and performs an inspection based on the obtained spectrum.
  • the inspection is performed according to the following principle.
  • the practical characteristics of the seed 3 are considered to vary depending on the components contained in the seed 3, and the practical characteristics are considered to be determined by the amounts and ratios of a plurality of components. Therefore, the practical characteristics of the components contained in the seed 3 are evaluated by analyzing the spectrum of the diffuse reflected light L2 in the near-infrared region in which these pieces of information are reflected. And the result of the analysis by the analysis unit 30 is notified to the operator of the seed analyzer 1 by outputting to the monitor connected to the analysis unit 30, a printer, etc., for example.
  • FIG. 3 is a diagram for explaining the flow of analysis using the spectrum of diffusely reflected light.
  • seed analysis by the seed analyzer 1 is performed by creating a reference spectrum (S01), measuring diffuse reflection spectra of various children to be measured (S02), calculating a difference spectrum / second-order differential spectrum. This is performed through the steps of calculation (S03) and pattern determination (S04).
  • a reference spectrum that is a reference spectrum used for evaluating the practical characteristics of seeds is created.
  • the seeds used for creating the reference spectrum may be the same item as the variety to be measured, and may be the same variety.
  • a method for creating a reference spectrum a part of seeds (for example, 10 seeds, 10% of the total number of seeds) to be measured is sampled, and some seeds are measured according to the spectrum measuring method. And a method of converting the acquired spectrum into an absorption spectrum and calculating an average value (reference spectrum).
  • the conversion to the absorption spectrum is performed using Kubelka-Munk (KM conversion).
  • the KM conversion is to calculate K / S shown in the following (1), where R is the diffuse reflectance at each wavelength obtained when measuring the diffuse reflectance spectrum of FIG.
  • K is an absorption coefficient
  • S is a scattering coefficient.
  • K / S (1-R) 2 / 2R (1)
  • the diffuse reflected light having a long optical path length is almost absorbed and only the diffuse reflected light having a short optical path length is emitted.
  • the absorption peak possessed by the inspection object may not be clear. Therefore, by clarifying the shape of the absorption peak using the K / S calculated by performing the above KM conversion, the characteristics of the inspection object can be clarified. It can be carried out.
  • the diffuse reflection spectrum and the KM absorbance spectrum can be used properly depending on the size of the diffuse reflectance (R) in the diffuse reflection spectrum.
  • the diffuse reflectance (R) is 0.1 or more, that is, when the intensity of the downward peak (absorption peak) in the diffuse reflection spectrum is greater than 0.1, the peak wavelength of the absorption peak in the diffuse reflection spectrum and Since the shape can be sufficiently confirmed, the evaluation in the diffuse reflection spectrum can be used.
  • the diffuse reflectance (R) is smaller than 0.1, it is difficult to distinguish between peaks. Therefore, by calculating the KM absorbance spectrum and evaluating each peak more clearly, it is possible to obtain more accurate results. High evaluation can be performed.
  • KM conversion is not essential, and analysis may be performed using the diffuse reflection spectrum as it is.
  • seed analysis is performed using an absorption spectrum obtained using the above-described KM conversion will be described.
  • a diffuse reflection spectrum is measured for each child that is a measurement target.
  • the diffuse reflection spectrum is acquired by imaging the diffuse reflected light L2 obtained by irradiating the seed 3 with the near infrared light L1 from the light source 10 with the camera 20.
  • the absorption spectrum is calculated by performing KM conversion on the diffuse reflection spectrum.
  • FIG. 4 An example of this difference spectrum is shown in FIG.
  • the absorption spectrum is calculated from the diffuse reflection spectrum for each of the four seeds # 1 to # 4, and the difference between each absorption spectrum and the reference spectrum is calculated.
  • seed # 1 is obtained by applying chemical processing to seeds of the same variety as the other seeds # 2 to # 4.
  • the spectrum shape of the seed # 1 has a peak shape that is different from the other seeds # 2 to # 4.
  • the peak shape is greatly different in the vicinity of the wavelength of 1730 nm. ing.
  • FIG. 5 shows a second-order differential difference spectrum obtained by second-order differentiation of the difference spectra of the four seeds # 1 to # 4 shown in FIG.
  • the difference between the peak shape at the wavelength of 1730 nm in the spectrum of seed # 1 and the peak shape in the spectra of other seeds # 2 to # 4 becomes more prominent.
  • pattern division is performed using the difference spectrum or the second-order differential difference spectrum.
  • Examples of pattern divisions include “good / bad germination rate”, “good / bad seedling rate”, “wild species / cultivated species”, “production area”, “presence / absence of processing such as drugs”. These may be combined and classified into three or more patterns.
  • a characteristic spectrum shape is shown in a differential spectrum or a second-order differential difference spectrum of seeds whose practical characteristics are known in advance, for example, seeds considered to have good germination vigor.
  • principal component analysis can be used as an analysis method for performing pattern identification.
  • a diffuse reflection spectrum obtained by irradiating near infrared light in a wavelength band of at least 10 nm. Since the sorting is performed based on the spectrum shape, seeds can be sorted by a simple method without destroying the seeds.
  • FIG. 6 is a spectrum obtained by second-order differentiation of the diffuse reflection spectrum from each of wild seeds and cultivated seeds with respect to the seeds of the vegetables of the Apiaceae family.
  • Wild seeds and cultivated seeds included in the seeds of vegetative vegetables based on the spectral shape of the diffuse reflectance spectrum obtained by irradiating near-infrared light in the band of at least 10 nm out of the wavelength band of 1200 nm to 2400 nm Can be distinguished. For example, by comparing the peak shape at a wavelength of 1400 nm to 1500 nm with the peak shape at a wavelength of 2100 nm to 2200 nm, it is possible to distinguish between wild seeds and cultivated seeds included in the seeds of the sorghum vegetable.
  • the light source unit 10 emits SC light
  • a light source different from the SC light source may be used.
  • the said embodiment demonstrated the case where the detection unit 20 had a function as a hyperspectral sensor which acquires a hyperspectral image, it is not limited to this structure. Moreover, although the said embodiment demonstrated the structure which irradiates the seed 3 mounted on the belt conveyor with the fixed light source unit 10 and detects with the detection unit 20, the light source unit 10 and the detection unit are movable. It may be an expression.
  • the present invention relates to a seed selection method and a seed selection apparatus that can easily and non-destructively select crops and tree seeds according to practical characteristics.
  • SYMBOLS 1 Seed sorting apparatus, 2 ... Belt conveyor, 3 ... Seed (seed object seed), 10 ... Light source unit, 11, 11A ... Light source, 12 ... Irradiation part, 13 ... Optical fiber, 20 ... Detection unit, 21 ... Slit, 22 ... Spectroscope, 23 ... Light receiving unit, 30 ... Analysis unit, 40 ... Mirror scanner, 50 ... Variable wavelength filter.

Abstract

[Problem] To provide a seed-sorting method capable of easily and non-destructively sorting seeds of agricultural crops and trees according to practical characteristics. [Solution] A method for sorting seeds that sorts according to the practical characteristics of each seed, the method characterized in that seeds for sorting are sorted into any of at least two groups on the basis of the profile of the diffuse reflection spectrum obtained by photography performed using a camera (20), by irradiating seeds for sorting (3) with near-infrared light in a band of at least 10 nm within the wavelength band of 1200 nm to 2400 nm from a light source (10).

Description

種子選別方法及び種子選別装置Seed sorting method and seed sorting device
本発明は、種子選別方法及び種子選別装置に関する。 The present invention relates to a seed selection method and a seed selection apparatus.
農作物の種子の品質判定等を目的として、例えば外観（種子の色調）に応じて種子を分類する装置や、種子の比重に応じて種子を分類する装置等が知られている。さらに、近年では、近赤外光を種子に照射することで、種子に含まれる特定の化学物質の量を計測し、その結果から種子の特性を推定する方法が検討されている（例えば、特許文献１参照）。 For the purpose of determining the quality of seeds of agricultural products, for example, an apparatus for classifying seeds according to appearance (color of seeds), an apparatus for classifying seeds according to specific gravity of seeds, and the like are known. Furthermore, in recent years, methods for measuring the amount of a specific chemical substance contained in seeds by irradiating the seeds with near infrared light and estimating the characteristics of the seeds from the results have been studied (for example, patents). Reference 1).
特表２００４－５１５７７８号公報JP-T-2004-515778
しかしながら、種子の発芽勢や成苗率等の実用特性については、種子に含まれる特定の化学物質の量だけで判断できるものではなく、いろいろな化学物質が影響を与えていると考えられる。このように複数種類の化学物質が種子の特性に影響を与える場合には、特定の化学物質の測定のみでは特性を正確に評価することが困難であると考えられる。また、複数の化学物質の含有量を測定することは作業量が非常に増大するという問題がある。 However, practical characteristics such as seed germination vigor and seedling rate cannot be judged only by the amount of a specific chemical substance contained in seeds, and various chemical substances are considered to have an influence. Thus, when multiple types of chemical substances affect seed characteristics, it is considered difficult to accurately evaluate the characteristics only by measuring specific chemical substances. In addition, measuring the contents of a plurality of chemical substances has a problem that the amount of work increases greatly.
本発明は上記を鑑みてなされたものであり、実用特性に応じた農作物や樹木の種子の選別を非破壊で容易に行うことが可能な種子選別方法及び種子選別装置を提供することを目的とする。 The present invention has been made in view of the above, and an object of the present invention is to provide a seed selection method and a seed selection apparatus capable of easily and non-destructively selecting crops and tree seeds according to practical characteristics. To do.
上記目的を達成するため、本発明に係る種子選別方法は、種子毎の実用特性に応じて選別を行う種子選別方法であって、選別対象である選別対象種子に対して、１２００ｎｍ～２４００ｎｍの波長帯域のうち少なくとも１０ｎｍの帯域の近赤外光を照射することにより得られる拡散反射スペクトルのスペクトル形状に基づいて選別対象種子を２以上のグループのうちにいずれかに選別することを特徴とする。 In order to achieve the above object, a seed selection method according to the present invention is a seed selection method for performing selection according to practical characteristics for each seed, and a wavelength of 1200 nm to 2400 nm with respect to a selection target seed that is a selection target. The selection target seeds are selected into two or more groups based on the spectral shape of the diffuse reflection spectrum obtained by irradiating near-infrared light in a band of at least 10 nm among the bands.
上記の種子選別方法によれば、少なくとも１０ｎｍの波長帯域の近赤外光を照射することにより得られる拡散反射スペクトルのスペクトル形状に基づいて、選別を行うため、種子を破壊することなく、且つ簡易な方法で種子を選別することができる。 According to the seed selection method described above, since the selection is performed based on the spectral shape of the diffuse reflection spectrum obtained by irradiating near-infrared light in the wavelength band of at least 10 nm, the seeds are not destroyed and simple. Seeds can be selected by various methods.
ここで、選別対象種子に対して近赤外光を照射することにより得られる拡散反射スペクトルについて、選別対象種子と同一品種の種子の拡散反射スペクトルである基準スペクトルとの差分スペクトルを算出し、差分スペクトルのスペクトル形状を用いて選別対象種子を選別する態様とすることができる。 Here, with respect to the diffuse reflection spectrum obtained by irradiating the selection target seed with near infrared light, a difference spectrum is calculated from the reference spectrum which is a diffuse reflection spectrum of the seed of the same variety as the selection target seed. It can be set as the aspect which classify | selects the selection object seed using the spectrum shape of a spectrum.
また、差分スペクトルの２階微分である２階微分差分スペクトルを算出し、２階微分差分スペクトルのスペクトル形状を用いて選別対象種子を選別する態様としてもよい。 Moreover, it is good also as an aspect which calculates the 2nd-order differential difference spectrum which is the 2nd-order differential of a difference spectrum, and selects the selection object seed using the spectrum shape of a 2nd-order differential difference spectrum.
また、所定の実用特性を有する種子の拡散反射スペクトルのスペクトル形状に基づいて当該種子が選別されるグループを予め調べておき、所定の実用特性を有する種子と同一のグループに選別対象種子が選別されるか否かにより、選別対象種子の実用特性を判定する態様とすることもできる。 In addition, a group in which the seed is selected based on the spectral shape of the diffuse reflection spectrum of the seed having a predetermined practical characteristic is examined in advance, and the seed to be selected is selected into the same group as the seed having the predetermined practical characteristic. It can also be set as the aspect which determines the practical characteristic of the seed for selection according to whether it is.
また、本発明に種子選別装置は、選別対象である選別対象種子に対して、１２００ｎｍ～２４００ｎｍの波長帯域のうち少なくとも１０ｎｍの帯域の近赤外光を照射する光源部と、選別対象種子において拡散反射された近赤外光による拡散反射スペクトルを取得する取得部と、取得部により取得された拡散反射スペクトルのスペクトル形状に基づいて選別対象種子を２以上のグループのいずれかに選別する分析部と、を備えることを特徴とする。 In addition, the seed selection device according to the present invention diffuses the selection target seed, which is a selection target, with a light source unit that irradiates near infrared light in a band of at least 10 nm out of a wavelength band of 1200 nm to 2400 nm, and the seed to be selected. An acquisition unit that acquires a diffuse reflection spectrum by reflected near-infrared light, and an analysis unit that selects a selection target seed into one of two or more groups based on the spectrum shape of the diffuse reflection spectrum acquired by the acquisition unit; It is characterized by providing.
本発明によれば、実用特性に応じた農作物や樹木の種子の選別を非破壊で容易に行うことが可能な種子選別方法及び種子選別装置が提供される。 ADVANTAGE OF THE INVENTION According to this invention, the seed selection method and seed selection apparatus which can perform the selection of the crop and the seed of a tree according to a practical characteristic nondestructively easily are provided.
本実施形態に係る種子選別装置の構成を説明する図である。It is a figure explaining the composition of the seed sorter concerning this embodiment. ハイパースペクトル画像について説明する図である。It is a figure explaining a hyperspectral image. 本実施形態に係る種子選別方法の流れを説明する図である。It is a figure explaining the flow of the seed selection method concerning this embodiment. 差分スペクトルについて説明する図である。It is a figure explaining a difference spectrum. ２階微分差分スペクトルについて説明する図である。It is a figure explaining a 2nd-order differential difference spectrum. ２階微分スペクトルの一例を示す図である。It is a figure which shows an example of a 2nd-order differential spectrum.
以下、添付図面を参照して、本発明を実施するための形態を詳細に説明する。なお、図面の説明においては同一要素には同一符号を付し、重複する説明を省略する。 DESCRIPTION OF EMBODIMENTS Hereinafter, embodiments for carrying out the present invention will be described in detail with reference to the accompanying drawings. In the description of the drawings, the same elements are denoted by the same reference numerals, and redundant description is omitted.
本実施形態に係る種子選別装置１について図１を用いて説明する。種子選別装置１は、ベルトコンベア２上に分散載置された種子３（選別対象種子：図１では、種子３の載置位置を示す）の実用特性を非破壊検査し、特性に基づいて選別するための装置である。本実施形態に係る種子選別装置１の選別要因として、種子の発芽勢や成苗率、種子の由来（野生種、栽培種や産地など）、薬剤などによる加工の有無等が挙げられるが、本実施形態では、これらを含めて実用特性という。実用特性は、種子３に含まれる成分の量や変質等の影響を受けると考えられるが、外観のみでは判断することが困難である。よって、本実施形態に係る種子選別装置１では、測定光を種子３に対して照射することにより得られる拡散反射光のスペクトルを測定し、このスペクトルを解析することにより種子３の実用特性を検出する。この種子選別装置１では、光源ユニット１０（光源部）、検出ユニット２０（取得部）、及び分析ユニット３０（分析部）を備える。 A seed sorting apparatus 1 according to this embodiment will be described with reference to FIG. The seed sorting device 1 performs nondestructive inspection on the practical characteristics of seeds 3 (separation target seeds: FIG. 1 shows the placement positions of the seeds 3) dispersedly placed on the belt conveyor 2, and sorts based on the characteristics. It is a device for doing. The selection factors of the seed selection device 1 according to the present embodiment include seed germination vigor and seedling rate, seed origin (wild species, cultivated species, production area, etc.), presence or absence of processing by chemicals, etc. In the embodiment, these are referred to as practical characteristics. Practical characteristics are considered to be affected by the amount and alteration of the components contained in the seed 3, but it is difficult to judge only by the appearance. Therefore, in the seed selection apparatus 1 according to the present embodiment, the spectrum of diffuse reflected light obtained by irradiating the seed 3 with the measurement light is measured, and the practical characteristics of the seed 3 are detected by analyzing the spectrum. To do. The seed sorting apparatus 1 includes a light source unit 10 (light source unit), a detection unit 20 (acquisition unit), and an analysis unit 30 (analysis unit).
光源ユニット１０は、一定の波長帯域を有する測定光を、ベルトコンベア２上における所定の照射領域Ａ１へ向けて照射する。光源ユニット１０が照射する測定光の波長範囲は、種子３の種類等に応じて近赤外領域であって、波長範囲が１２００～２４００ｎｍの光が好適に用いられ、この波長範囲のうちの少なくとも１０ｎｍ以上の帯域の光が分析に用いられる。 The light source unit 10 irradiates measurement light having a certain wavelength band toward a predetermined irradiation area A1 on the belt conveyor 2. The wavelength range of the measurement light emitted by the light source unit 10 is a near-infrared region depending on the type of the seed 3 and the like, and light having a wavelength range of 1200 to 2400 nm is preferably used. Light in a band of 10 nm or more is used for analysis.
なお、本実施形態では、スーパーコンティニューム（ＳＣ）光を発生する光源１１（ＳＣ光源）を含む光源ユニット１０について説明する。 In the present embodiment, the light source unit 10 including the light source 11 (SC light source) that generates super continuum (SC) light will be described.
照射領域Ａ１とは、種子３を載置するベルトコンベア２の表面（載置面２ｂ）の一部の領域である。この照射領域Ａ１は、載置面２ｂの進行方向２ａ（図１のｙ軸方向）と垂直な幅方向（ｘ軸方向）に広がり、載置面２ｂの一方の端から他方の端までを覆うライン状に延びる領域である。そして、照射領域Ａ１の延在方向に垂直な方向（ｙ軸方向）における照射領域Ａ１の幅は１０ｍｍ以下とされる。 The irradiation region A1 is a partial region of the surface (mounting surface 2b) of the belt conveyor 2 on which the seed 3 is placed. The irradiation area A1 extends in the width direction (x-axis direction) perpendicular to the traveling direction 2a (y-axis direction in FIG. 1) of the placement surface 2b, and covers from one end to the other end of the placement surface 2b. This is an area extending in a line. And the width | variety of irradiation area | region A1 in a direction (y-axis direction) perpendicular | vertical to the extension direction of irradiation area | region A1 shall be 10 mm or less.
光源ユニット１０は、ＳＣ光を出射する光源１１と、照射部１２と、光源１１と照射部１２とを接続する光ファイバ１３と、を備える。光源１１は、近赤外光としてＳＣ光を発生させる。さらに具体的には、ＳＣ光源である光源１１は、種光源及び非線形媒質を備え、種光源から出射される光を非線形媒質に入力し、非線形媒質中における非線形光学効果によりスペクトルを広帯域に広げてＳＣ光を出力する。 The light source unit 10 includes a light source 11 that emits SC light, an irradiation unit 12, and an optical fiber 13 that connects the light source 11 and the irradiation unit 12. The light source 11 generates SC light as near infrared light. More specifically, the light source 11 that is an SC light source includes a seed light source and a nonlinear medium, inputs light emitted from the seed light source to the nonlinear medium, and broadens the spectrum by a nonlinear optical effect in the nonlinear medium. SC light is output.
光源１１により発生された近赤外光（ＳＣ光）は、光ファイバ１３の一方の端面へ入射される。この近赤外光は、光ファイバ１３のコア領域を導波し、もう一方の端面から照射部１２に対して出射される。 Near-infrared light (SC light) generated by the light source 11 is incident on one end face of the optical fiber 13. This near-infrared light is guided through the core region of the optical fiber 13 and is emitted from the other end face to the irradiation unit 12.
照射部１２は、光ファイバ１３の端面から出射される近赤外光（ＳＣ光）を検査対象物３が載置される照射領域Ａ１に対して照射する。照射部１２は、光ファイバ１３から出射される近赤外光を入射して、照射領域Ａ１に対応した１次元のライン状に出射するため、照射部１２としてシリンドリカルレンズが好適に用いられる。このように照射部１２においてライン状に整形された近赤外光Ｌ１が、照射部１２から照射領域Ａ１に対して照射される。 The irradiation unit 12 irradiates near-infrared light (SC light) emitted from the end face of the optical fiber 13 to the irradiation area A1 on which the inspection object 3 is placed. Since the irradiation unit 12 receives near infrared light emitted from the optical fiber 13 and emits it in a one-dimensional line corresponding to the irradiation region A1, a cylindrical lens is preferably used as the irradiation unit 12. The near-infrared light L1 shaped in a line shape in the irradiation unit 12 in this way is irradiated from the irradiation unit 12 to the irradiation region A1.
光源ユニット１０から出力された近赤外光Ｌ１は、照射領域Ａ１上に載置された種子３により拡散反射される。そして、その一部が、拡散反射光Ｌ２として検出ユニット２０に入射する。 The near-infrared light L1 output from the light source unit 10 is diffusely reflected by the seed 3 placed on the irradiation area A1. A part of the light enters the detection unit 20 as diffusely reflected light L2.
検出ユニット２０は、ハイパースペクトル画像を取得するハイパースペクトルセンサとしての機能を有する。ここで、本実施形態におけるハイパースペクトル画像について図２を用いて説明する。図２は、ハイパースペクトル画像についてその概略を説明する図である。図２に示すように、ハイパースペクトル画像Ｈとは、Ｎ個の画素Ｐ１～ＰＮにより構成されている画像であるが、このうちの１個の画素Ｐｎには、複数の強度データからなるスペクトル情報Ｓｎが含まれている。この強度データとは、特定の波長（又は波長帯域）におけるスペクトル強度を示すデータであり、図２では、１５個の強度データがスペクトル情報Ｓｎとして保持されていることを示す。このように、ハイパースペクトル画像Ｈは、画像を構成する画素毎に、それぞれ複数の強度データを持つという特徴から、画像としての二次元的要素と、スペクトルデータとしての要素をあわせ持った三次元的構成のデータである。 The detection unit 20 has a function as a hyperspectral sensor that acquires a hyperspectral image. Here, the hyperspectral image in this embodiment is demonstrated using FIG. FIG. 2 is a diagram for explaining the outline of the hyperspectral image. As shown in FIG. 2, the hyperspectral image H is an image composed of N pixels P 1 to P N, and one of the pixels P n includes a plurality of intensity data. The following spectral information Sn is included. And the intensity data is data indicating a spectral intensity at a particular wavelength (or wavelength band), indicating that in FIG. 2, 15 intensity data are held as the spectral information S n. As described above, the hyperspectral image H is characterized by having a plurality of intensity data for each pixel constituting the image, so that a three-dimensional image having both a two-dimensional element as an image and an element as spectral data. Configuration data.
図１に戻り、本実施形態に係る検出ユニット２０は、スリット２１と、分光器２２と、受光部２３と、を備える。この検出ユニット２０は、その視野領域２０ｓがベルトコンベア２の進行方向２ａと垂直な方向（ｘ軸方向）に延びている。検出ユニット２０の視野領域２０ｓは、載置面２ｂの照射領域Ａ１に含まれるライン状の領域であって、スリット２１を通過した拡散反射光Ｌ２が受光部２３上に像を結ぶ領域である。 Returning to FIG. 1, the detection unit 20 according to the present embodiment includes a slit 21, a spectroscope 22, and a light receiving unit 23. The detection unit 20 has a visual field region 20 s extending in a direction (x-axis direction) perpendicular to the traveling direction 2 a of the belt conveyor 2. The visual field region 20s of the detection unit 20 is a linear region included in the irradiation region A1 of the placement surface 2b, and is a region in which the diffusely reflected light L2 that has passed through the slit 21 forms an image on the light receiving unit 23.
スリット２１は、照射領域Ａ１の延在方向（ｘ軸方向）と平行な方向に開口が設けられる。検出ユニット２０のスリット２１に入射した拡散反射光Ｌ２は、分光器２２へ入射する。 The slit 21 is provided with an opening in a direction parallel to the extending direction (x-axis direction) of the irradiation region A1. The diffuse reflected light L2 that has entered the slit 21 of the detection unit 20 enters the spectroscope 22.
分光器２２は、スリット２１の長手方向、すなわち照射領域Ａ１の延在方向に垂直な方向（ｙ軸方向）に拡散反射光Ｌ２を分光する。分光器２２により分光された光は、受光部２３によって受光される。 The spectroscope 22 splits the diffusely reflected light L2 in the longitudinal direction of the slit 21, that is, the direction perpendicular to the extending direction of the irradiation area A1 (y-axis direction). The light split by the spectroscope 22 is received by the light receiving unit 23.
受光部２３は、複数の受光素子が２次元に配列された受光面を備え、各受光素子が光を受光する。これにより、受光部２３がベルトコンベア２上の幅方向（ｘ軸方向）に沿った各位置で反射した拡散反射光Ｌ２の各波長の光をそれぞれ受光することとなる。各受光素子は、受光した光の強度に応じた信号を位置と波長とからなる二次元平面状の一点に関する情報として出力する。この受光部２３の受光素子から出力される信号が、ハイパースペクトル画像に係る画像データとして、検出ユニット２０から分析ユニット３０に送られる。 The light receiving unit 23 includes a light receiving surface in which a plurality of light receiving elements are two-dimensionally arranged, and each light receiving element receives light. As a result, the light receiving unit 23 receives light of each wavelength of the diffusely reflected light L2 reflected at each position along the width direction (x-axis direction) on the belt conveyor 2. Each light receiving element outputs a signal corresponding to the intensity of received light as information on a two-dimensional planar point composed of a position and a wavelength. A signal output from the light receiving element of the light receiving unit 23 is sent from the detection unit 20 to the analysis unit 30 as image data related to the hyperspectral image.
分析ユニット３０は、入力された信号により拡散反射光Ｌ２のスペクトルを得て、この得られたスペクトルに基づいて検査を行う。種子３の実用特性を検出する場合は、次のような原理で検査を行う。種子３の実用特性は、種子３に含まれる成分によって変動すると考えられ、複数の成分の量やその割合等により実用特性が決まると考えられる。したがって、これらの情報が反映された近赤外領域の拡散反射光Ｌ２のスペクトルを分析することで、種子３に含まれる成分の実用特性を評価する。そして、分析ユニット３０による分析の結果は、例えば分析ユニット３０に接続されるモニタや、プリンタ等に出力することによって、種子分析装置１のオペレータに通知される。 The analysis unit 30 obtains the spectrum of the diffuse reflected light L2 from the input signal, and performs an inspection based on the obtained spectrum. When the practical characteristics of the seed 3 are detected, the inspection is performed according to the following principle. The practical characteristics of the seed 3 are considered to vary depending on the components contained in the seed 3, and the practical characteristics are considered to be determined by the amounts and ratios of a plurality of components. Therefore, the practical characteristics of the components contained in the seed 3 are evaluated by analyzing the spectrum of the diffuse reflected light L2 in the near-infrared region in which these pieces of information are reflected. And the result of the analysis by the analysis unit 30 is notified to the operator of the seed analyzer 1 by outputting to the monitor connected to the analysis unit 30, a printer, etc., for example.
ここで、種子３の近赤外光の拡散反射スペクトルを用いた分析について説明する。図３は、拡散反射光のスペクトルを用いた分析の流れを説明する図である。図３に示すように、種子分析装置１による種子分析は、基準スペクトルの作成（Ｓ０１）、測定対象となる各種子の拡散反射スペクトルの測定（Ｓ０２）、差分スペクトルの算出／２階微分スペクトルの算出（Ｓ０３）、パターン判定（Ｓ０４）のステップを経て行われる。 Here, the analysis using the near-infrared diffuse reflection spectrum of the seed 3 will be described. FIG. 3 is a diagram for explaining the flow of analysis using the spectrum of diffusely reflected light. As shown in FIG. 3, seed analysis by the seed analyzer 1 is performed by creating a reference spectrum (S01), measuring diffuse reflection spectra of various children to be measured (S02), calculating a difference spectrum / second-order differential spectrum. This is performed through the steps of calculation (S03) and pattern determination (S04).
まず、種子の実用特性を評価するために用いる基準となるスペクトルとなる基準スペクトルが作成される。基準スペクトルの作成に用いられる種子は、測定対象物となる品種と同一の品目であればよく、更に、同一品種であることができる。基準スペクトルの作成方法の一例としては、測定対象物となる種子の一部（例えば、１０個、種子全数の１０％等）をサンプリングし、その一部の種子をスペクトルの測定方法にしたがって測定し、取得したスペクトルを吸収スペクトルに変換し、その平均値（基準スペクトル）を算出する方法が挙げられる。 First, a reference spectrum that is a reference spectrum used for evaluating the practical characteristics of seeds is created. The seeds used for creating the reference spectrum may be the same item as the variety to be measured, and may be the same variety. As an example of a method for creating a reference spectrum, a part of seeds (for example, 10 seeds, 10% of the total number of seeds) to be measured is sampled, and some seeds are measured according to the spectrum measuring method. And a method of converting the acquired spectrum into an absorption spectrum and calculating an average value (reference spectrum).
吸収スペクトルへの変換は、クベルカ－ムンク変換（Kubelka-Munk：ＫＭ変換）を用いて行われる。ＫＭ変換とは、図２の拡散反射スペクトルを測定する際に得られた各波長における拡散反射率をＲとしたとき、下記（１）に示すＫ／Ｓを算出することである。なお、Ｋは吸収係数、Ｓは散乱係数である。
Ｋ／Ｓ＝（１－Ｒ）２／２Ｒ　　…（１） The conversion to the absorption spectrum is performed using Kubelka-Munk (KM conversion). The KM conversion is to calculate K / S shown in the following (1), where R is the diffuse reflectance at each wavelength obtained when measuring the diffuse reflectance spectrum of FIG. Here, K is an absorption coefficient and S is a scattering coefficient.
K / S = (1-R) 2 / 2R (1)
拡散反射スペクトルを測定する際、検査対象物による吸収が強い波長域（吸収ピーク）においては、長い光路長を有する拡散反射光はほとんど吸収され、短い光路長を有する拡散反射光のみが放射される。一方、検査対象物による吸収が弱い波長域（吸収ピーク）においては、長い光路長を有する拡散反射光についても一部は吸収されず放射される。したがって、拡散反射スペクトルにおけるピーク間の相対強度は、元来検査対象物が有する吸収ピーク間の相対強度よりも小さくなっているため、検査対象物の有する吸収ピークが明確とならない場合がある。そこで、上記のＫＭ変換を行って算出したＫ／Ｓを用いて吸収ピークの形状を明確にすることにより、より検査対象物の特徴を明確にすることができるため、より高精度の品質評価を行うことができる。 When measuring the diffuse reflection spectrum, in the wavelength region (absorption peak) where the absorption by the inspection object is strong, the diffuse reflected light having a long optical path length is almost absorbed and only the diffuse reflected light having a short optical path length is emitted. . On the other hand, in the wavelength region (absorption peak) where the absorption by the inspection object is weak, part of the diffusely reflected light having a long optical path length is not absorbed but is emitted. Therefore, the relative intensity between the peaks in the diffuse reflection spectrum is smaller than the relative intensity between the absorption peaks originally possessed by the inspection object, and therefore the absorption peak possessed by the inspection object may not be clear. Therefore, by clarifying the shape of the absorption peak using the K / S calculated by performing the above KM conversion, the characteristics of the inspection object can be clarified. It can be carried out.
なお、拡散反射スペクトルとＫＭ吸光度スペクトル（ＫＭ変換による吸収スペクトル）とは、拡散反射スペクトルにおける拡散反射率（Ｒ）の大小により使い分けることができる。例えば、拡散反射率（Ｒ）が０．１以上の場合、すなわち拡散反射スペクトルにおいて下向きのピーク（吸収ピーク）の強度が０．１より大きい場合は、拡散反射スペクトルにおいても吸収ピークのピーク波長及びその形状が十分に確認できることから、拡散反射スペクトルにおける評価を用いることができる。しかし、拡散反射率（Ｒ）が０．１より小さい場合は、ピークの区別がつきづらいことから、ＫＭ吸光度スペクトルを算出して各ピークをより明確に区別した後に評価することにより、より精度の高い評価を行うことができる。 The diffuse reflection spectrum and the KM absorbance spectrum (absorption spectrum by KM conversion) can be used properly depending on the size of the diffuse reflectance (R) in the diffuse reflection spectrum. For example, when the diffuse reflectance (R) is 0.1 or more, that is, when the intensity of the downward peak (absorption peak) in the diffuse reflection spectrum is greater than 0.1, the peak wavelength of the absorption peak in the diffuse reflection spectrum and Since the shape can be sufficiently confirmed, the evaluation in the diffuse reflection spectrum can be used. However, when the diffuse reflectance (R) is smaller than 0.1, it is difficult to distinguish between peaks. Therefore, by calculating the KM absorbance spectrum and evaluating each peak more clearly, it is possible to obtain more accurate results. High evaluation can be performed.
なお、ＫＭ変換は必須ではなく、拡散反射スペクトルをそのまま利用して分析を行ってもよい。本実施形態では、上記のＫＭ変換を用いて得られた吸収スペクトルを利用して種子の分析を行う場合について説明を行う。 Note that KM conversion is not essential, and analysis may be performed using the diffuse reflection spectrum as it is. In the present embodiment, a case where seed analysis is performed using an absorption spectrum obtained using the above-described KM conversion will be described.
次に、測定対象である各種子について拡散反射スペクトルの測定を行う。光源１０から種子３に対して近赤外光Ｌ１を照射して得られる拡散反射光Ｌ２をカメラ２０で撮像することにより、拡散反射スペクトルを取得する。本実施形態では、この拡散反射スペクトルをＫＭ変換することにより、吸収スペクトルを算出する。 Next, a diffuse reflection spectrum is measured for each child that is a measurement target. The diffuse reflection spectrum is acquired by imaging the diffuse reflected light L2 obtained by irradiating the seed 3 with the near infrared light L1 from the light source 10 with the camera 20. In the present embodiment, the absorption spectrum is calculated by performing KM conversion on the diffuse reflection spectrum.
次に、先に作成した基準スペクトルと吸収スペクトルとを用いて差分スペクトルを取得する。この差分スペクトルの例を図４に示す。図４では、４つの種子＃１～＃４についてそれぞれ拡散反射スペクトルから吸収スペクトルを算出し、それぞれの吸収スペクトルと基準スペクトルとの差分を算出したものである。４つの種子＃１～＃４のうち、種子＃１は、他の種子＃２～４と同一品種の種子に対して薬剤加工を施したものである。図４の差分スペクトルの例では、種子＃１のスペクトル形状のみが他の種子＃２～＃４とは正負が異なるピーク形状となっていて、例えば、波長１７３０ｎｍ付近においては、ピーク形状が大きく異なっている。 Next, a difference spectrum is acquired using the reference spectrum and absorption spectrum created previously. An example of this difference spectrum is shown in FIG. In FIG. 4, the absorption spectrum is calculated from the diffuse reflection spectrum for each of the four seeds # 1 to # 4, and the difference between each absorption spectrum and the reference spectrum is calculated. Of the four seeds # 1 to # 4, seed # 1 is obtained by applying chemical processing to seeds of the same variety as the other seeds # 2 to # 4. In the example of the difference spectrum in FIG. 4, only the spectrum shape of the seed # 1 has a peak shape that is different from the other seeds # 2 to # 4. For example, the peak shape is greatly different in the vicinity of the wavelength of 1730 nm. ing.
また、図５では、図４に示した４つの種子＃１～＃４の差分スペクトルを２階微分した２階微分差分スペクトルを算出したものを示す。図５の２階微分差分スペクトル場合、種子＃１のスペクトルでの波長１７３０ｎｍにおけるピーク形状について、他の種子＃２～＃４のスペクトルにおけるピーク形状との差異がより顕著となる。 FIG. 5 shows a second-order differential difference spectrum obtained by second-order differentiation of the difference spectra of the four seeds # 1 to # 4 shown in FIG. In the second-order differential difference spectrum of FIG. 5, the difference between the peak shape at the wavelength of 1730 nm in the spectrum of seed # 1 and the peak shape in the spectra of other seeds # 2 to # 4 becomes more prominent.
次に、この差分スペクトルまたは２階微分差分スペクトルを用いて、パターン分けを行う。パターン分けの例を挙げると、「発芽勢が良／不良」「成苗率が良／不良」「野生種／栽培種」「産地」「薬剤などの加工の有／無」が考えられる。また、これらを組み合わせて、３つ以上のパターンに分類してもよい。このような実用特性に応じてパターン分けをする場合、予め実用特性が分かっている種子、例えば、発芽勢が良いと思われる種子の差分スペクトルまたは２階微分差分スペクトルにおいて特徴的なスペクトル形状を示す波長帯域を特定し、その波長帯域におけるスペクトル形状に基づいてパターン識別を行う方法が挙げられる。パターン識別を行うための解析方法としては、例えば主成分分析を用いることができる。 Next, pattern division is performed using the difference spectrum or the second-order differential difference spectrum. Examples of pattern divisions include “good / bad germination rate”, “good / bad seedling rate”, “wild species / cultivated species”, “production area”, “presence / absence of processing such as drugs”. These may be combined and classified into three or more patterns. When patterning is performed according to such practical characteristics, a characteristic spectrum shape is shown in a differential spectrum or a second-order differential difference spectrum of seeds whose practical characteristics are known in advance, for example, seeds considered to have good germination vigor. There is a method of identifying a wavelength band and performing pattern identification based on a spectrum shape in the wavelength band. As an analysis method for performing pattern identification, for example, principal component analysis can be used.
以上のように、本実施形態に係る種子選別装置１及びこの種子選別装置１を用いた種子選別方法によれば、少なくとも１０ｎｍの波長帯域の近赤外光を照射することにより得られる拡散反射スペクトルのスペクトル形状に基づいて、選別を行うため、種子を破壊することなく、且つ簡易な方法で種子を選別することができる。 As described above, according to the seed sorting device 1 and the seed sorting method using the seed sorting device 1 according to the present embodiment, a diffuse reflection spectrum obtained by irradiating near infrared light in a wavelength band of at least 10 nm. Since the sorting is performed based on the spectrum shape, seeds can be sorted by a simple method without destroying the seeds.
図６は、セリ科の野菜の種子に関して野生種種子、栽培種種子各々からの拡散反射スペクトルを２階微分したスペクトルである。１２００ｎｍ～２４００ｎｍの波長帯域のうちの少なくとも１０ｎｍの帯域の近赤外光を照射することにより得られる拡散反射スペクトルのスペクトル形状に基づいてセリ科の野菜の種子に含まれる野生種種子と栽培種種子とを区別することが可能である。例えば、波長１４００ｎｍ～１５００ｎｍにおけるピーク形状と、波長２１００ｎｍ～２２００ｎｍにおけるピーク形状とを比較すれば、セリ科の野菜の種子に含まれる野生種種子と栽培種種子とを区別することが可能となる。 FIG. 6 is a spectrum obtained by second-order differentiation of the diffuse reflection spectrum from each of wild seeds and cultivated seeds with respect to the seeds of the vegetables of the Apiaceae family. Wild seeds and cultivated seeds included in the seeds of vegetative vegetables based on the spectral shape of the diffuse reflectance spectrum obtained by irradiating near-infrared light in the band of at least 10 nm out of the wavelength band of 1200 nm to 2400 nm Can be distinguished. For example, by comparing the peak shape at a wavelength of 1400 nm to 1500 nm with the peak shape at a wavelength of 2100 nm to 2200 nm, it is possible to distinguish between wild seeds and cultivated seeds included in the seeds of the sorghum vegetable.
以上、本発明の実施形態について説明したが、本発明は上記の実施形態に限定されず、種々の変更を行うことができる。 As mentioned above, although embodiment of this invention was described, this invention is not limited to said embodiment, A various change can be made.
例えば、上記実施形態では、光源ユニット１０が、ＳＣ光を出射する場合について説明したが、ＳＣ光源とは異なる光源を用いてもよい。 For example, in the above embodiment, the case where the light source unit 10 emits SC light has been described. However, a light source different from the SC light source may be used.
また、上記実施形態では、検出ユニット２０が、ハイパースペクトル画像を取得するハイパースペクトルセンサとしての機能を有する場合について説明したが、この構成に限定されない。また、上記実施形態では、ベルトコンベアに載置されて移動する種子３を固定された光源ユニット１０で照射し、検出ユニット２０にて検出する構成について説明したが、光源ユニット１０及び検出ユニットが可動式となっていてもよい。 Moreover, although the said embodiment demonstrated the case where the detection unit 20 had a function as a hyperspectral sensor which acquires a hyperspectral image, it is not limited to this structure. Moreover, although the said embodiment demonstrated the structure which irradiates the seed 3 mounted on the belt conveyor with the fixed light source unit 10 and detects with the detection unit 20, the light source unit 10 and the detection unit are movable. It may be an expression.
実用特性に応じた農作物や樹木の種子の選別を非破壊で容易に行うことが可能な種子選別方法及び種子選別装置に関する。 The present invention relates to a seed selection method and a seed selection apparatus that can easily and non-destructively select crops and tree seeds according to practical characteristics.
１…種子選別装置、２…ベルトコンベア、３…種子（選別対象種子）、１０…光源ユニット、１１，１１Ａ…光源、１２…照射部、１３…光ファイバ、２０…検出ユニット、２１…スリット、２２…分光器、２３…受光部、３０…分析ユニット、４０…ミラースキャナ、５０…波長可変フィルタ。 DESCRIPTION OF SYMBOLS 1 ... Seed sorting apparatus, 2 ... Belt conveyor, 3 ... Seed (seed object seed), 10 ... Light source unit, 11, 11A ... Light source, 12 ... Irradiation part, 13 ... Optical fiber, 20 ... Detection unit, 21 ... Slit, 22 ... Spectroscope, 23 ... Light receiving unit, 30 ... Analysis unit, 40 ... Mirror scanner, 50 ... Variable wavelength filter.

Claims (5)

  1. 種子毎の実用特性に応じて選別を行う種子選別方法であって、
    選別対象である選別対象種子に対して、１２００ｎｍ～２４００ｎｍの波長帯域のうち少なくとも１０ｎｍの帯域の近赤外光を照射することにより得られる拡散反射スペクトルのスペクトル形状に基づいて前記選別対象種子を２以上のグループのいずれかに選別する方法。     A seed selection method for selecting according to the practical characteristics of each seed,
    Based on the spectral shape of the diffuse reflection spectrum obtained by irradiating the selection target seed, which is the selection target, with near-infrared light of at least 10 nm out of the wavelength band of 1200 nm to 2400 nm, A method of sorting into any of the above groups.
  2. 前記選別対象種子に対して前記近赤外光を照射することにより得られる前記拡散反射スペクトルについて、前記選別対象種子と同一品種の種子の拡散反射スペクトルである基準スペクトルとの差分スペクトルを算出し、
    前記差分スペクトルのスペクトル形状を用いて前記選別対象種子を選別する
    ことを特徴とする請求項１記載の種子選別方法。     For the diffuse reflectance spectrum obtained by irradiating the selection target seed with the near-infrared light, a difference spectrum from a reference spectrum that is a diffuse reflectance spectrum of seeds of the same variety as the selection target seed is calculated,
    The seed selection method according to claim 1, wherein the selection target seed is selected using a spectrum shape of the difference spectrum.
  3. 前記差分スペクトルの２階微分である２階微分差分スペクトルを算出し、
    前記２階微分差分スペクトルのスペクトル形状を用いて前記選別対象種子を選別する
    ことを特徴とする請求項２記載の種子選別方法。     Calculating a second-order differential difference spectrum that is a second-order derivative of the difference spectrum;
    The seed selection method according to claim 2, wherein the selection target seed is selected using a spectrum shape of the second-order differential difference spectrum.
  4. 所定の実用特性を有する種子の拡散反射スペクトルのスペクトル形状に基づいて当該種子が選別されるグループを予め調べておき、
    前記所定の実用特性を有する種子と同一のグループに前記選別対象種子が選別されるか否かにより、前記選別対象種子の実用特性を判定する
    ことを特徴とする請求項１～３のいずれか一項に記載の種子選別方法。     Check in advance the group in which the seed is selected based on the spectral shape of the diffuse reflection spectrum of the seed having the predetermined practical characteristics,
    4. The practical characteristics of the selection target seed are determined based on whether or not the selection target seed is selected in the same group as the seed having the predetermined practical characteristic. The seed selection method according to Item.
  5. 選別対象である選別対象種子に対して、１２００ｎｍ～２４００ｎｍの波長帯域のうち少なくとも１０ｎｍの帯域の近赤外光を照射する光源部と、
    前記選別対象種子において拡散反射された前記近赤外光による拡散反射スペクトルを取得する取得部と、
    前記取得部により取得された前記拡散反射スペクトルのスペクトル形状に基づいて前記選別対象種子を２以上のグループのいずれかに選別する分析部と、
    を備えることを特徴とする種子選別装置。     A light source unit that irradiates the selection target seeds to be selected with near infrared light in a band of at least 10 nm out of a wavelength band of 1200 nm to 2400 nm;
    An acquisition unit that acquires a diffuse reflection spectrum by the near-infrared light diffusely reflected in the selection target seed;
    An analyzing unit that sorts the selection target seed into one of two or more groups based on a spectrum shape of the diffuse reflection spectrum acquired by the acquiring unit;
    A seed sorting apparatus comprising:
PCT/JP2013/055704 2012-03-05 2013-03-01 Method for sorting seeds and seed-sorting apparatus WO2013133171A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2012048484 2012-03-05
JP2012-048484 2012-03-05

Publications (1)

Publication Number Publication Date
WO2013133171A1 true WO2013133171A1 (en) 2013-09-12

Family

ID=49116651

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2013/055704 WO2013133171A1 (en) 2012-03-05 2013-03-01 Method for sorting seeds and seed-sorting apparatus

Country Status (2)

Country Link
JP (1) JPWO2013133171A1 (en)
WO (1) WO2013133171A1 (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101619031B1 (en) * 2015-12-31 2016-05-18 충남대학교산학협력단 The short wave infrared reflectance system for detection bacterium (Acidovorax avenae subsp. citrulli) infected watermelon seed
KR101683404B1 (en) * 2015-12-31 2016-12-06 충남대학교산학협력단 Development of detection method for virus-infected (cucumber green mottle mosaic virus) watermelon seed using near-infrared reflectance spectrum and detection apparatus
ES2684855A1 (en) * 2017-03-31 2018-10-04 Arboreto S.A.T., Ltda INSPECTION EQUIPMENT FOR THE AUTOMATED CLASSIFICATION OR DISCRIMINATION OF ALMONDS BASED ON THE CONCENTRATION OF AMIGDALINE AND INSPECTION PROCEDURE (Machine-translation by Google Translate, not legally binding)
CN110174357A (en) * 2019-04-12 2019-08-27 浙江大学 A kind of hybridization gumbo seed classification identification device and method

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004150984A (en) * 2002-10-31 2004-05-27 National Institute Of Advanced Industrial & Technology Method and apparatus for measuring concentration in dissolved substance by near-infrared spectrum
JP2004515778A (en) * 2000-10-30 2004-05-27 モンサント テクノロジー エルエルシー Method and apparatus for analyzing crops

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004515778A (en) * 2000-10-30 2004-05-27 モンサント テクノロジー エルエルシー Method and apparatus for analyzing crops
JP2004150984A (en) * 2002-10-31 2004-05-27 National Institute Of Advanced Industrial & Technology Method and apparatus for measuring concentration in dissolved substance by near-infrared spectrum

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101619031B1 (en) * 2015-12-31 2016-05-18 충남대학교산학협력단 The short wave infrared reflectance system for detection bacterium (Acidovorax avenae subsp. citrulli) infected watermelon seed
KR101683404B1 (en) * 2015-12-31 2016-12-06 충남대학교산학협력단 Development of detection method for virus-infected (cucumber green mottle mosaic virus) watermelon seed using near-infrared reflectance spectrum and detection apparatus
ES2684855A1 (en) * 2017-03-31 2018-10-04 Arboreto S.A.T., Ltda INSPECTION EQUIPMENT FOR THE AUTOMATED CLASSIFICATION OR DISCRIMINATION OF ALMONDS BASED ON THE CONCENTRATION OF AMIGDALINE AND INSPECTION PROCEDURE (Machine-translation by Google Translate, not legally binding)
CN110174357A (en) * 2019-04-12 2019-08-27 浙江大学 A kind of hybridization gumbo seed classification identification device and method

Also Published As

Publication number Publication date
JPWO2013133171A1 (en) 2015-07-30

Similar Documents

Publication Publication Date Title
Walsh et al. Visible-NIR ‘point’spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use
US9625376B2 (en) System for and method of combined LIBS and IR absorption spectroscopy investigations
US9910024B2 (en) Method, sensor unit and machine for detecting “sugar top” defects in potatoes
Magwaza et al. Evaluation of Fourier transform-NIR spectroscopy for integrated external and internal quality assessment of Valencia oranges
WO2016059946A1 (en) Spectroscopic measurement method and spectroscopic measurement device
US9164029B2 (en) Method of classifying and discerning wooden materials
Lohumi et al. Raman hyperspectral imaging and spectral similarity analysis for quantitative detection of multiple adulterants in wheat flour
JP2011141809A (en) Equipment and method for analyzing image data
JP7265908B2 (en) Tablet inspection method and tablet inspection device
JP2014215177A (en) Inspection device and inspection method
Lohumi et al. Calibration and testing of a Raman hyperspectral imaging system to reveal powdered food adulteration
Saranwong et al. A feasibility study using simplified near infrared imaging to detect fruit fly larvae in intact fruit
JP2013044729A (en) Coating state measuring method
JP2012098181A (en) Device and method for detection
WO2013133171A1 (en) Method for sorting seeds and seed-sorting apparatus
Mignani et al. Dispersive raman spectroscopy for the nondestructive and rapid assessment of the quality of southern Italian honey types
JP2013164338A (en) Method for detecting foreign matter of plant or plant product
WO2016080442A1 (en) Quality evaluation method and quality evaluation device
Valero et al. Selection models for the internal quality of fruit, based on time domain laser reflectance spectroscopy
JP2009168747A (en) Method of inspecting food and inspection apparatus implementing the same
JP2017203658A (en) Inspection method and optical measurement device
US20090185164A1 (en) Method of inspecting food and inspection apparatus implementing the same
JP6832947B2 (en) Methods and equipment for detecting the presence of mycotoxins in cereals
JP2015040818A (en) Method and apparatus for grain classification
JP2882824B2 (en) Fruit and vegetable ingredient measuring device

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 13758066

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2014503817

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 13758066

Country of ref document: EP

Kind code of ref document: A1