JP4993558B2 - Crop identification method and crop identification device - Google Patents

Description

The present invention relates to a method and an apparatus for identifying them by components contained in agricultural products such as vegetables and fruits such as green onions, radishes, carrots, and apples.

  In recent years, for example, deep green onions called white onions are being branded by local producers such as Honjo green onions and white stars, which have a special taste. However, these branded agriculture, forestry and fishery products have little difference in appearance when the original varieties are the same. For this reason, when a brand is fake and shipped, a consumer cannot determine whether it is genuine. Therefore, these fake brand products have caused a problem that the brand image that has finally been established through efforts has been reduced.

In response to the above problems, an apparatus has been proposed in which the content of these agricultural, forestry and fishery products is measured and analyzed by quality judging means to judge the quality (see Patent Document 1).
The apparatus for measuring the content of the component moves the fruits and vegetables placed between the adjacent fruits and vegetables placement units in the transport direction, and sends the measurement light projected from the irradiation unit to the fruits and vegetables placement units. Irradiate the same peripheral surface of the rotating fruits and vegetables by the rotational force. And the measurement light which permeate | transmitted the fruit and vegetables from the clearance gap between each fruit and vegetables mounting part is received by a light-receiving means, The spectrum of the received measurement light is analyzed by a quality determination means, and quality is determined. However, the device for measuring this component content can only receive the measurement light that passes through the gaps between the fruit and vegetable placement parts, and can only determine the quality of the central part of the fruit and vegetables through which the measurement light passes, The accuracy of quality judgment was not high.

  In addition, rotate the axis of the cylindrical cell as the axis of rotation, move the near infrared irradiation device in the length direction or height direction of the cell, and irradiate the entire distributed raw tea leaves in a spiral Thus, a tea leaf component analysis method and apparatus for measuring it have been proposed (see Patent Document 2).

  However, this tea leaf component analysis method and apparatus cannot specify to which part of the raw tea leaves the near infrared rays are irradiated, and the results differ depending on each measurement. Therefore, the accuracy of specifying the quality was not high.

Japanese Patent Laid-Open No. 2005-9943 Japanese Patent Laid-Open No. 11-230902

The present invention, crop specific method of identifying the crops, and aims to provide a crop specific apparatus used for the crop specific method.

The present invention is irradiated with near infrared rays with respect to agricultural crops, a measuring step of measuring the sugar contained in the predetermined direction to the crop by measuring the transmitted light the near infrared radiation is transmitted through the crop was obtained by the measurement A linear function connecting two predetermined points is calculated from the sugar content distribution, a conversion distribution obtained by converting the sugar content distribution so that the linear function becomes a reference axis is obtained, and an approximate expression of the conversion distribution is obtained by Fourier transform. calculates, on the basis of the coefficients of the approximate expression, a crop specific method characterized by having a crop specifying step of specifying said crop, a crop specific apparatus used for the crop specific method.

The crops include, for example leeks, radish, that the vegetables and fruits, such as carrots or apples.
Thus, it is possible by the distribution of the content of the sugar containing crops, identifying the crop. It should be noted, and to identify the crops, includes identifying the brand and, crops of freshness that is based on the quality of the crops.

  The near-infrared rays refer to infrared rays having a wavelength of 700 to 1500 nm.

According to the present invention, an approximate expression with a high degree of approximation (approximating performance is good) can be easily obtained, and the crop is specified by the coefficient of the approximate expression. Therefore, compared with the case where the crop is specified by all measured values. Thus, the process for specifying becomes easy.

In one embodiment of the present invention, the predetermined direction, the axial direction of the crop, it may be at least one of the rotation direction around the axis of the crop.

The axis is the central longitudinal axis if crop elongated center direction if cored crop apple etc., or that the central axis in the height direction or width direction when the crop mucin Including.

Thereby, the measurement direction can be selected according to the form of the target crop , the crop can be specified by the distribution of the selected measurement direction of the content, and therefore, the variation in the specific accuracy due to the shape of the target crop is eliminated, It is possible to specify with high accuracy.

In addition, as an aspect of the present invention, it is possible to provide a type determination step and a type determination unit for comparing the threshold value serving as a quality standard registered in advance with the coefficient to determine the type of the crop .

  The type includes a brand type differentiated according to the content of the component.

The threshold includes a coefficient of a distribution indicating a boundary of an allowable range of a type set in advance based on a component distribution of a predetermined component of a crop serving as a reference for the type.

Thereby, even if it is a crop with an unknown brand, a brand can be specified and a consumer's convenience can be raised, for example. In addition, since a standard for brand certification can be set, the quality of the brand can be easily maintained, and user satisfaction can be obtained.

In addition, as an aspect of the present invention, a freshness determination step and a freshness determination means for determining the freshness of the crop based on freshness change data corresponding to the freshness of the crop registered in advance can be provided.

The freshness refers to the degree of freshness of crops with the passage of time from a predetermined reference time such as at the time of harvesting, shipping, shipping from cold storage or frozen storage.
The freshness change data is data indicating the change over time of the freshness for each type expressed by a coefficient of a linear function connecting two points determined in advance from the content distribution or a coefficient of an approximate expression of the content distribution. Yes, it includes data for each freshness and a function indicating changes over time.
Thereby, since a consumer can confirm the freshness of the farm products to purchase, he can raise a consumer's satisfaction. In addition, it is possible to prevent counterfeiting of the crop freshness display.

According to this invention, the said crop can be specified by distribution of content of the sugar to contain.

An embodiment of the present invention will be described below with reference to the drawings.
FIG. 1 shows a configuration diagram of the crop identification device 1, FIG. 2 shows a perspective view of the transmitted near-infrared spectrum measuring device 2, and FIG. 3 shows a longitudinal sectional direction in the vicinity of the center in the width direction of the transmitted near-infrared spectrum measuring device 2. FIG. 4 is a perspective view of the digital saccharimeter 4.
The crop identification device 1 includes a transmission near-infrared spectrum measurement device 2 and a digital sugar content meter 4, and each is connected to a control device 3.

  The transmitted near-infrared spectrum measuring apparatus 2 includes a light projecting unit 21 that is provided at an upper portion and includes a light emitting device 21a that emits near-infrared light L1 downward from the bottom surface, and a deep green onion 100 that is a subject. A sliding base 22 that slides in the left-right direction, which is the axial direction (longitudinal direction), and a light receiving unit 24 that receives the transmitted light L2 emitted from the light projecting unit 21 and transmitted through the deep root onion 100, In this order, the transmission spectrum data received by the above configuration is output to the control device 3.

In addition, although the light-emitting device 21a is comprised with the halogen lamp, it can also be comprised with the illuminating device which irradiates near infrared light L1, such as a light emitting diode.
Further, a fixed projection 23 is provided in the center of the upper surface of the slide type pedestal 22 so as to sandwich and hold the mounted deep green onion 100. The fixed projection 23 emits light from the light projecting unit 21 and passes through the deep green onion 100. A passage hole 23a that allows the transmitted light L2 to pass therethrough, and gripping means (not shown) that is gripped and appropriately rotated in the direction of arrow a (FIG. 3) about the axis of the root deep green onion 100.

  Further, in the present embodiment, the slide base 22 is configured such that the slide base 22 itself slides in the left-right direction (longitudinal direction). However, the slide base 22 is fixed, and the fixed protrusion 23 is the slide base. You may comprise so that 22 can be slid to the left-right direction.

The light receiving unit 24 includes a photographing camera (CCD camera) 24a inside, and a light receiving window 25 that allows the transmitted light L2 to pass therethrough.
Furthermore, a first condenser lens (not shown) is provided between the light emitting device 21a and the deep root leek 100. Further, a second condenser lens and a diffraction grating (not shown) are arranged in this order between the deep root leek 100 and the CCD camera 24a. Therefore, the near-infrared light L1 emitted from the light emitting device 21a passes through the first condenser lens and is irradiated to the deep root onion 100, and the transmitted light L2 transmitted through the deep root onion 100 is transmitted to the second condenser lens and the diffraction grating. And reaches the CCD camera 24a. In this embodiment, near infrared rays L1 having wavelengths of 750, 768, 825, and 907 nm determined based on experimental results are used, but the present invention is not limited to this.

  In FIG. 2, the transmission near-infrared spectrum measuring apparatus 2 is omitted for clarity, but the upper end portion is made of rubber across the front surface of the light projecting unit 21 and the front surface of the slide base 22. A warming lamp 26 fixed below the front surface of the light unit 21 is provided to block the outside light from affecting the near-infrared light L1 irradiated to the deep root leek 100.

  With the above configuration, the transmitted near-infrared spectrum measuring apparatus 2 controls transmission spectrum data at a plurality of points in the length direction (axial direction) of the deep root green onion 100 and the rotational direction around the axial center. Can be output.

  As shown in FIG. 4, the digital saccharimeter 4 is a measuring instrument for measuring the Brix value indicating the saccharification, and switches between the liquid crystal display unit 43 that displays the measured value and the power ON / OFF of the digital saccharimeter 4. It consists of a power switch, an operation unit 42 for instructing and setting the start of measurement, and a measurement lens unit 41 having a circular shape in plan view for measuring by injecting a liquid subject. This is a configuration for outputting to the control device 3.

The deep root leek 100, which is the subject, grinds the measurement piece, which is the part to be measured, dissolves in purified water to form a liquid test solution, and injects it into the measurement lens unit 41.
With the above configuration, the digital sugar content meter 4 can measure the Brix value indicating the sugar content of the root deep green onion 100 and output it to the control device 3.

  The control device 3 is a computer, a control device composed of a CPU, ROM, and RAM, a storage device such as a hard disk, and a storage medium reading device that reads a storage medium such as a CD-ROM drive or FD drive (CD-ROM, FD, etc.) And a storage medium read / write device, an input device such as a mouse and a keyboard, and a display device 31 constituted by a CRT, a liquid crystal screen, or the like.

It should be noted that the storage device (for example, hard disk) has absorbance described later from a control program for controlling each device of the crop identification device 1, a registration program for registering each data, transmission spectrum data described later, and reference data described later. Absorbance calculation program to be calculated, conversion formula calculation program for calculating a conversion formula for converting to converted Brix value data from correspondence between absorbance and Brix value, and a conversion program for calculating converted Brix value data from transmission spectrum data by the conversion formula A distribution calculation program for calculating the component distribution of the converted converted Brix value data, an approximate expression calculation program for calculating an approximate expression of the component distribution, or a comparison program for comparing a coefficient of the approximate expression with a previously registered coefficient And an output program for outputting the results to the display device 31. Stores and the like.

The transmission spectrum data is the absorption afterglow intensity of the light intensity of a predetermined wavelength in the near infrared ray L1 emitted from the light emitting device 21a.
The reference data is a light reception spectrum (light reception intensity) in a state where only air exists between the light projecting unit 21 and the light receiving unit 24.
The absorbance is a ratio between the received light intensity as reference data and the afterglow intensity of transmission spectrum data.

  In addition, the control device 3 uses reference data, quality standard converted Brix value data (hereinafter referred to as quality standard data) for each brand type, coefficients of approximate formulas thereof, or freshness standard converted Brix value data serving as the freshness standard ( A database (DB) 32 for storing the coefficients of the freshness reference data) and the approximate expression thereof is connected.

  With the above configuration, the control device 3 registers the data output from the transmission near-infrared spectrum measurement device 2 and the digital saccharimeter 4 in the DB 32, and based on the transmission spectrum data and the reference data by the absorbance calculation program. The absorbance at one measuring point of the root deep green onion 100 can be calculated, and a conversion formula for converting the absorbance into converted Brix value data can be calculated by the conversion formula calculation program.

  In this embodiment, as shown in FIG. 5, the deep root leek 100 has the root X1 as a measurement reference cross section, and presets measurement cross sections x2 to 16 at predetermined equal intervals (for example, 2 cm intervals). As shown, in each measurement section, measurement points y1 to 6 are set at equal intervals (30 degrees in the present embodiment) around the axis of the root deep green onion 100, and the measurement cross section X1 is measured by the transmission near infrared spectrum measurement apparatus 2. Measure each of measurement points y1-6 of -16.

Further, in the digital sugar content meter 4, the root deep green onion 100 is cut at the midpoint of each measurement cross section X1-16, a measurement piece centering on the measurement cross section X1-16 is created, and the test piece is ground and tested. A liquid is formed and measured. However, the measurement cross section may be set at a ratio to the full length H such as dividing the full length H into 15 equal parts with reference to the full length H of the root deep green onion 100 without setting the measurement cross sections at predetermined equal intervals, The length H2 of the white portion of the deep root leek 100 may be set as a reference. In this case, the same number of measurement cross sections can be ensured for any length of deep green onion 100, and the comparison becomes easy. Further, the measurement reference cross section may be set after securing a predetermined length from the end portion without setting the measurement reference cross section at the lower end root portion 100a of the deep root leek 100.
Further, the number of measurement sections and measurement points to be set is not limited to the above set number, and may be set as appropriate depending on the shape such as the length of the deep root leek 100 that is the subject.

  Thus, by setting the measurement reference cross section, the measurement cross section, and the measurement points at equal intervals between the measurement cross sections, it is possible to detect measurement data that can be compared with the root depth leek 100 and that can be easily compared. In addition, for example, since the error due to the rotation direction of the measurement point can be eliminated as compared with the measurement in which the measurement point is not set in the rotation direction, even if the same root green onion 100 is measured again, it is reproduced. High-quality measurement data can be detected.

  In the present embodiment, the root deep spring onion 100 is rotated in the direction of arrow a (FIG. 3), and all the measurement points on the measurement cross section on the lower end root portion 100a side of the root deep spring onion 100 are measured, and then the upper end side Although it is configured in the measurement order to move to the next measurement section toward the leaf, for example, the root deep spring onion 100 is moved in the axial direction (longitudinal direction), and only the measurement points with each measurement section are measured. Then, a measurement order in which the root deep green onion 100 is rotated and sequential measurement points are measured may be used.

Next, the crop specifying method using the crop specifying apparatus 1 having such a configuration will be described with reference to FIGS. 7 to 11 showing flow charts of the respective measurements.
As shown in FIG. 7, this crop specifying method includes a calibration mode, a reference data registration mode, a brand determination mode, and a freshness measurement mode.

The calibration mode uses transmission near-infrared spectrum measurement device 2 and digital saccharimeter 4 of crop identification device 1 to collect transmission spectrum data and Brix values for a plurality of real crops in advance before this measurement. In this mode, the conversion formula for converting the transmission spectrum data into the converted Brix value data is calculated and registered by the absorbance calculation program or the conversion formula calculation program.

  In the reference data registration mode, before the actual measurement using the transmission near-infrared spectrum measuring apparatus 2, as the freshness reference data serving as the freshness reference and the brand reference data serving as the brand quality reference, the respective converted Brix values are used. This is a mode for calculating and registering an approximate expression of the component distribution and its coefficients. Further, in this mode, an allowable quality range is set based on the brand reference data, and an allowable upper / lower distribution coefficient indicating the allowable range is calculated and registered.

The brand determination mode is a mode in which the transmitted near-infrared spectrum measuring apparatus 2 is used to determine whether or not the measured component distribution of the deep green onion 100 is included in an allowable range based on pre-registered brand reference data.
The freshness measurement mode is a mode in which the transmitted near-infrared spectrum measuring apparatus 2 is used to determine the freshness of the root depth leek 100 based on freshness reference data in which the measured component distribution of the root depth leek 100 is registered in advance.

When this crop specifying method is started, it is first determined whether or not the calibration mode is set. If the crop mode is set (step s1: Yes), a calibration process is performed (step s2). If it is not the calibration mode (step s1: No), it is next determined whether or not it is the reference data registration mode. If it is the reference data registration mode (step s3: Yes), a reference data registration process is performed (step s3: Yes). Step s4). If it is not the reference data registration mode (step s3: No), it is next determined whether or not it is the brand determination mode. If it is the brand determination mode (step s5: Yes), brand determination processing is performed (step s6). ) If not in the brand determination mode (step s5: No), the freshness measurement process is performed (step s7), and the crop identification method is completed.

Each mode will be described below.
First, the calibration mode will be described with reference to FIG.
In the calibration mode, the crop identification device 1 performs a calibration process. In this calibration process, first, using the transmission near-infrared spectrum measuring apparatus 2, six measurement points y1 to 6 in the radial direction are measured with respect to measurement sections x1 to 16 (FIG. 5) arranged in the longitudinal direction of the root deep green onion 100. The transmission spectrum data of (FIG. 6) is detected (step t1). Further, after the transmission near-infrared spectrum measurement step t1, the root deep green onion 100 is cut into 16 measurement pieces as described above, and the pretreatment for forming the above-described test solution is performed (step t2). 4 is used to measure the Brix value indicating sugar content (step t3). Each measurement data is output to the control device 3, and the control device 3 registers the measurement data in the DB 32 (step t4). The control device 3 determines whether or not the registered number of data has reached the number of data that can be calibrated, that is, the conversion formula can be calculated. When the number of data is sufficient (step t5: Yes), 16 pieces of the transmission spectrum data, which are the minimum values for each measurement section, of 96 pieces of the transmission spectrum data in total, and a measurement piece corresponding to the measurement section Based on the Brix value, a conversion formula for converting transmission spectrum data into converted Brix value data is calculated by the absorbance calculation program or the conversion formula calculation program (step t6) and registered (step t7). If it is determined that the number of data is not sufficient (step t5: No), the process returns to the transmission near infrared spectrum measurement step t1 again, and the transmission spectrum data and the Brix value are collected based on the flow described above. .
Since the conversion formula can be calculated even with a small number of data, the conversion formula based on the small number of data may be calculated without determining t5.
Through this calibration process, it is possible to convert transmission spectrum data detected by the transmission near-infrared spectrum measuring apparatus 2 into converted Brix value data corresponding to the sugar content detected by the digital sugar content meter 4.

  In the present embodiment, a conversion formula for converting into converted Brix value data is calculated based on the minimum value of transmission spectrum data at each measurement point in each measurement section and the Brix value of the measurement piece corresponding to the measurement section. However, the present invention is not limited to this, and for example, the total or average value or maximum value of converted Brix values at each measurement point in each measurement section may be used.

Next, the reference data registration mode will be described with reference to FIG.
In the reference data registration mode, the crop identification device 1 performs reference data registration processing. In this reference data registration process, it is first determined whether or not the brand reference data is measured by operating an appropriate input means. If it is brand reference data measurement (step u1: Yes), the reference product that is the brand quality reference 96 transmission spectrum data of the measurement points y1 to 6 (FIG. 6) of the respective measurement sections x1 to 16 (FIG. 5) are detected by the transmitted near-infrared spectrum measuring apparatus 2 (step u2). ), And output the detected transmission spectrum data to the control device 3. The control device 3 calculates converted Brix value data from the 16 transmission spectrum data, which is the minimum value for each measurement section, among the 96 transmission spectrum data (Step u3), and calculates a distribution calculation program. Thus, as shown in FIG. 13A, the component distribution W1 of the converted Brix value data is calculated with the horizontal axis as the length of the deep root leek 100 and the vertical axis as the Brix value.

  Further, the approximate expression calculation program calculates an approximate expression of the calculated component distribution W1 by a method described later (step u4), and registers the coefficient of the approximate expression in the DB 32 as brand determination reference data (step u5). . Furthermore, based on the component distribution of the root deep green onion 100 which is the reference product, an allowable upper limit distribution W3 and an allowable lower limit distribution W4 indicating the upper and lower limits of the allowable range of the brand quality component distribution are set, and the allowable upper and lower limit distribution is set. The coefficients of the approximate expressions of W3 and W4 (FIG. 13) are registered in the DB 32 as brand determination allowable value data (step u6), and the process is completed.

  Here, a method of calculating the approximate expression of the calculated component distribution W will be described in detail. As shown in FIG. 13B, for example, when obtaining an approximate expression of the component distribution W1, a linear function S1 connecting the measurement point X1 and the measurement point X16 of the component distribution W1 of the converted Brix value data.

を求め、上記成分分布Ｗ１を、一時関数Ｓ１に基づいて変換し、一次関数Ｓ１を基準軸とする変換分布Ｗ２を求め、この変換分布Ｗ２をフーリエ変換することで、前記成分分布Ｗ１を示す近似式

The component distribution W1 is converted based on the temporary function S1, a conversion distribution W2 having the primary function S1 as a reference axis is obtained, and the conversion distribution W2 is Fourier transformed to approximate the component distribution W1. formula

を容易に求めることができる。この数式２のａ_０、ｂ、ａ_１・ｅ^ｉωｔは、成分分布Ｗ１の分布形状を表す係数である。この算出方法により、品質基準である基準品の根深ネギ１００の成分分布Ｗ１、許容上限分布Ｗ３、あるいは許容下限分布Ｗ４等の成分分布の近似式および近似式の係数を算出することができる。なお、本明細書中において、上記近似式を求める方法を近似式算出方法という。

Can be easily obtained. In Equation 2, a ₀ , b, a ₁ · e ^iωt are coefficients representing the distribution shape of the component distribution W1. By this calculation method, it is possible to calculate the approximate expression and the coefficient of the approximate expression of the component distribution such as the component distribution W1, the allowable upper limit distribution W3, or the allowable lower limit distribution W4 of the root deep green onion 100 as the quality standard. In this specification, the method for obtaining the approximate expression is referred to as an approximate expression calculation method.

  When it is not brand standard data measurement (step u1: No), the root deep green onion 100 which is various freshness states is prepared, and the measurement cross sections x1-16 of each root deep green onion 100 with the transmission near infrared spectrum measuring apparatus 2 (FIG. 5). ) 96 transmission spectrum data are detected in total at the measurement points y1 to 6 (FIG. 6) (step u7), and the detected transmission spectrum data is output to the control device 3. The control device 3 calculates converted Brix value data from the 16 transmission spectrum data, which is the minimum value for each measurement section, among the 96 transmission spectrum data, based on the conversion formula by a conversion program (step u8). ), The freshness reference component distribution of the converted Brix value data is calculated by the distribution calculation program. For example, when the root depth leek 100 is measured on the day of harvest, 3 days after harvest, 5 days after harvest, 7 days after harvest, and 10 days after harvest, the freshness reference component distributions W5, W6, W7, W8, W9 can be calculated.

  The approximate expression calculation program calculates the approximate expression of the calculated freshness component distributions W5, 6, 7, 8, and 9 by the above-described approximate expression calculation method (step u9), and uses the coefficient of the approximate expression as freshness reference data. Register in the DB 32 (step u10). It is determined whether or not the registered freshness reference data has reached a sufficient number of data for performing the freshness determination process. If it is sufficient, the process is completed (step u11: Yes). (Step u11: No), it returns to freshness reference data transmission near infrared spectrum measurement u7 again, and freshness reference data is collected.

  By this reference data registration mode processing, the root deep green onion 100 having various freshness states and the root deep green onion 100 which is a standard product that is a brand quality standard, which is detected in advance by the transmission near infrared spectrum measuring apparatus 2 before the main measurement, is obtained. In the transmission spectrum data, the minimum value in each measurement section is converted into a converted Brix value, and a component distribution in which the converted Brix value in each measurement section is associated with the position of the measurement section is calculated. Furthermore, an approximate expression of the component distribution can be calculated, and the coefficient of the approximate expression can be registered. In addition, based on the component distribution of the standard product of the brand, the allowable range of the component distribution certified as a brand is set, and the allowable upper limit distribution indicating the upper and lower limits of the allowable range and the allowable value coefficient of the allowable lower limit distribution are set as the brand judgment allowable value. It can be registered as data.

  In the present embodiment, the component distribution based on the converted Brix value obtained by converting the minimum value among the transmission spectrum data at each measurement point in each measurement cross section is calculated. However, the present invention is not limited to this. The total or average value or the maximum value of the converted Brix values at each measurement point may be used.

Next, the brand determination mode will be described with reference to FIG.
In the brand determination mode, the crop identification device 1 performs brand determination processing. This brand determination process uses the transmission near-infrared spectrum measuring apparatus 2 to measure all the measurement points y1 to 6 (FIG. 6) of the measurement cross sections x1 to 16 (FIG. 5) of the root deep green onion 100 whose component distribution is unknown. The 96 pieces of transmission spectrum data are detected (step v1), and the detected transmission spectrum data is output to the control device 3. The control device 3 calculates converted Brix value data based on the conversion formula from the 16 transmission spectrum data, which is the minimum value for each measurement section, among the 96 transmission spectrum data (step v2). ), The component distribution of the converted Brix value data is calculated by the distribution calculation program.

  The approximate expression calculation program calculates the approximate expression of the calculated component distribution by the above-described approximate expression calculation method (step v3), and registers the coefficient of the approximate expression in the DB 32 in the reference data registration mode by the comparison program. Compared with the determined brand determination allowable value data (step v4), and if the allowable value coefficient range of the allowable range distribution registered as the brand determination allowable value data is not exceeded (step v5: Yes), the root deep green onion 100 Is recognized as being the deep green onion 100 that satisfies the brand quality, and is output to the display device 31 (step v6). If the coefficient of the approximate expression of the root deep green onion 100 exceeds the range of the allowable value coefficient (step v5: No), the root deep green onion 100 is rejected as a root deep green onion 100 that does not satisfy the brand quality. And complete the process.

  For example, as shown in FIG. 15, when the component distribution of the root green onion 100 in a plurality of production areas is detected, an approximate expression for each component distribution is obtained by the approximate expression calculation program, and each approximate expression is calculated by the comparison program. The coefficient and the brand judgment allowable value data are compared, and the root deep green onion 100 of the production area C having the component distribution determined to be within the brand judgment allowable value data range (W3, W4) is recognized as a brand product, etc. Nepuka Leek 100 is certified as a branded product.

  Therefore, as described above, the brand determination mode process determines whether the quality of the brand is satisfied by the component distribution of the root deep green onion 100 even if the root of the green onion 100 is unknown. If you are satisfied, you can certify that it is a branded product.

  In addition, with this brand determination mode processing, for example, even the root deep green onion 100 cultivated as a brand product can be rejected for the deep root green onion 100 that does not satisfy the quality due to the component distribution, so it is easy to maintain the quality of the brand. It can be carried out.

  In the present embodiment, the component distribution based on the converted Brix value obtained by converting the minimum value among the transmission spectrum data at each measurement point in each measurement cross section is calculated. However, the present invention is not limited to this. The total or average value or the maximum value of the converted Brix values at each measurement point may be used.

Next, the freshness determination mode will be described with reference to FIG.
In the freshness determination mode, the crop identification device 1 performs a freshness determination process. This freshness determination process uses the transmitted near-infrared spectrum measuring apparatus 2 to transmit the transmission spectrum data of the measurement points y1 to 6 (FIG. 6) of the measurement sections x1 to 16 (FIG. 5) of the root deep green onion 100 whose freshness is unknown. (Step w1), and the detected transmission spectrum data is output to the control device 3. The control device 3 calculates converted Brix value data from the 16 transmission spectrum data, which is the minimum value for each measurement section, among the 96 transmission spectrum data, based on the conversion formula by a conversion program (step w2). ), The component distribution of the converted Brix value data is calculated by the distribution calculation program.

  The approximate expression calculation program calculates the approximate expression of the calculated component distribution by the above-described approximate expression calculation method (step w3), and the coefficient of the approximate expression is registered in the freshness reference data DB 32 as the reference data registration mode. If the coefficient of the freshness standard data corresponding to the coefficient of the approximate expression exists (step w5: Yes), the freshness of the root deep green onion 100 is certified based on the freshness standard data. And output to the display device 31 (step w6). If the coefficient of the freshness reference data corresponding to the coefficient of the approximate expression of the root deep green onion 100 does not exist (step w5: No), an indication that the freshness of the root deep green onion 100 cannot be determined is output to the display device 31. , Complete the process.

  By this freshness determination mode process, even the root depth leek 100 whose freshness is unknown can be determined based on freshness reference data registered in advance by the component distribution of the root depth leek 100, and the freshness of the root depth leek 100 can be recognized. .

  Accordingly, the consumer can confirm the freshness of the root deep green onion 100 without confirming the quality indication such as the harvest date described in the package of the root deep green onion 100, and can increase the satisfaction of the consumer.

  In the present embodiment, the component distribution based on the converted Brix value obtained by converting the minimum value among the transmission spectrum data at each measurement point in each measurement cross section is calculated. However, the present invention is not limited to this. The total or average value or the maximum value of the converted Brix values at each measurement point may be used.

  Further, in this embodiment, the freshness of the root deep green onion 100 is determined based on the coefficient of the freshness reference data for each freshness, but the change in freshness with time is shown based on the coefficient of the freshness reference data for each of a plurality of freshnesses. The structure which calculates a function and determines the freshness of the deep root leek 100 by the function may be sufficient. Thereby, the freshness of the deep green onion 100 whose freshness is unknown can be determined without previously registering freshness reference data for each of the many freshnesses.

  Furthermore, without determining the freshness of the root deep green onion 100 based on the coefficient of the approximate expression of the component distribution for each freshness, the root deep green onion 100 based on the coefficient of the linear function connecting two predetermined points of the component distribution for each freshness. The structure which determines freshness may be sufficient. Thereby, since it can compare by the coefficient of a linear function in order to obtain | require the freshness of the root deep green onion 100 whose freshness is undetermined, the calculation burden of the control apparatus 3 can be reduced.

  Note that the component distribution is not only the component distribution in the axial center direction (length direction) of the deep root leek 100 as shown in FIG. 13 or the like, but when the subject is a substantially spherical body such as an apple or an onion, for example, FIG. As shown in FIG. 5, the component distribution in the rotation direction about the axis may be detected.

In addition, by storing in advance a conversion formula or conversion table for converting transmission spectrum data output from the transmission near-infrared spectrum measurement device 2 into a converted Brix value, the crop identification device 1 is transmitted to the transmission near-infrared spectrum measurement device 2. And the control device 3, and the crop identification method may be configured in a reference data registration mode, a brand determination mode, and a freshness measurement mode. Further, the crop identification method is configured only in the measurement mode, the component distribution of the root deep green onion 100 is calculated, and for each part of the root deep green onion 100, for example, for cooking or seasoning, based on the calculated component distribution You may use the crop identification apparatus 1 in order to determine a utilization method.

In the correspondence between the configuration of the present invention and the above-described embodiment,
The crop of the present invention corresponds to the root deep green onion 100,
Similarly,
The crop identification process corresponds to the approximate expression calculation processes u4, u9, v3, w3,
The measurement process corresponds to the transmission near infrared spectrum measurement process t1, v1, w1, the freshness reference data transmission near infrared spectrum measurement process u2, and the brand reference data transmission near infrared spectrum measurement process w1,
The distribution corresponds to the component distribution W1,
The transmitted light corresponds to the transmitted light L2,
The linear function corresponds to the linear function S1,
The conversion distribution corresponds to the conversion distribution W2,
The approximate expression corresponds to approximate expression S2,
The threshold corresponds to the allowable range distributions W3 and W4,
The freshness change data corresponds to freshness component distributions W5 to 9,
The type determination step corresponds to the coefficient comparison step v4,
The freshness determination step corresponds to the coefficient comparison step w4,
The measuring means corresponds to the transmission near infrared spectrum measuring apparatus 2,
The crop identification means corresponds to the crop identification device 1,
The crop identification means corresponds to an absorbance calculation program, a conversion formula calculation program, a conversion program, a distribution calculation program, and an approximate expression calculation program, and the type determination means corresponds to a comparison program,
The freshness determination means corresponds to the comparison program,
Although the predetermined component corresponds to the sugar content, the present invention is not limited only to the configuration of the above-described embodiment, and can be applied based on the technical idea shown in the claims, and many embodiments can be applied. Obtainable.

The block diagram of a crop identification apparatus. The perspective view of a transmission near-infrared spectrum measuring apparatus. Explanatory drawing of the longitudinal cross-section direction near the center of the width direction of a transmission near-infrared spectrum measuring apparatus. The perspective view of a digital sugar content meter. Explanatory drawing of the measurement cross section of a deep-rooted onion. Explanatory drawing of the measurement point of Nebuka onion. The flowchart of the crop identification method. The flowchart of a calibration mode. The flowchart of a reference data registration mode. The flow chart of brand judgment mode. The flowchart of freshness determination mode. The component distribution graph of conversion Brix value data. Tolerance distribution graph showing tolerance boundaries. Freshness component distribution graph. Component distribution graph of measurement results. Other forms of component distribution graph.

DESCRIPTION OF SYMBOLS 1 ... Agricultural product identification apparatus 2 ... Transmission near-infrared spectrum measuring apparatus 100 ... Nep deep leek t1, v1, w1 ... Transmission near-infrared spectrum measurement process u2 ... Brand reference data transmission near-infrared spectrum measurement process u4, u9, v3, w3 ... Approximation formula calculation step u7 ... freshness reference data transmission near infrared spectrum measurement step v4, w4 ... coefficient comparison step L2 ... transmitted light S1 ... linear function W1 ... component distribution W2 ... conversion distribution W3, W4 ... allowable range distribution W5-9 … Freshness component distribution

Claims (8)

A measuring step of irradiating the near infrared, near-infrared to measure the glucose contained in the crop by measuring the light transmitted through the crop in a predetermined direction relative to the crops,
A linear function connecting two predetermined points is calculated from the sugar content distribution obtained by the measurement, a conversion distribution obtained by converting the sugar content distribution so that the linear function becomes a reference axis is obtained, and by Fourier transform, calculating the approximate expression of the transformation distribution, based on the coefficients of the approximate expression, and a crop specifying step of specifying the crop
Crop identification method. The certain direction,
The axial direction of the crop, at least one and then crop specific method of claim 1 with a rotation direction around the axis of the crop. Compared to threshold a previously registered quality standards and the coefficient, crop specific method according to claim 1 or 2 having a type determination step determines the type of the crop. The crop identification method according to any one of claims 1 to 3, further comprising a freshness determination step of determining freshness of the crop based on freshness change data corresponding to freshness of the crop registered in advance. Measuring means for irradiating the crop with near infrared rays, measuring the transmitted light transmitted by the near infrared rays through the crop , and measuring sugar contained in the crop in a certain direction;
Calculate a linear function connecting two predetermined points from the distribution of sugar content obtained by the measurement, obtain a conversion distribution obtained by converting the content distribution so that the linear function is a reference axis, by Fourier transform, calculating the approximate expression of the transformation distribution, based on the coefficients of the approximate expression, and a crop specifying means for specifying the crop
A crop identification device. The certain direction,
The axial direction of the crop, at least one and then crop determining device according to claim 5 in the rotational direction around the axis of the crop. The crop identification device according to claim 5 or 6, further comprising a type determination unit that determines a type of the crop by comparing a threshold serving as a quality standard registered in advance with the coefficient. The crop identification device according to any one of claims 5 to 7, further comprising freshness determination means for determining freshness of the crop based on freshness change data corresponding to the freshness of the crop registered in advance for each type.

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