JP7471215B2 - Crop sorting equipment - Google Patents

Description

The present invention relates to a crop sorting device that evaluates crops such as carrots and radishes using photographed images of the crops being transported and sorts the crops based on the evaluation results.

Patent Document 1 discloses an automatic crop sorting device that removes crops one by one from a storage area, weighs and measures them, and sorts and collects them by weight class.

Patent Document 2 discloses an inspection device that uses multiple cameras to capture images of an inspection object transported by a transport means such as a conveyor, and performs an appearance inspection of the fruit or vegetable that is the inspection object based on the multiple captured images. This inspection device includes an input section into which the fruit or vegetable is input, a transport section that transports the input fruit or vegetable, an imaging section that photographs the transported fruit or vegetable, an identification section that uses the multiple captured images of the fruit or vegetable to identify the size, shape, and damage of the fruit or vegetable, and a sorting section that sorts the fruit or vegetable based on the identification results. The size determination by the identification section is performed by comparing the number of pixels in the fruit or vegetable area obtained by image processing of the captured image as the area with a threshold value. The shape determination by the identification section is also performed using the ratio of the long side to the short side of a rectangle that borders the fruit or vegetable area. The damage determination by the identification section is performed by comparing the area of the connected area of the damaged image obtained by image processing with a standard value.

Japanese Patent Application Laid-Open No. 6-156654 Japanese Patent No. 4649628

In the automatic crop sorting device described in Patent Document 1, it is necessary to provide a balance arm for each weight class for weighing and sorting, so as the number of weight classes increases, the number of balance arms also increases, and the device becomes larger.

In the inspection device according to Patent Document 2, in order to determine the size, shape and damage of fruits and vegetables, multiple captured images from an imaging unit having multiple cameras are required, which complicates the imaging unit and image processing. Furthermore, the size of fruits and vegetables is determined by comparing the number of pixels in the fruit or vegetable area recognized in the captured image as the area with a threshold value, so even if the fruit or vegetable is deformed, it may be determined to be large as long as its area is large.

Therefore, the object of the present invention is to improve a crop sorting device that sorts crops based on the size grade and surface abnormalities of the crops evaluated using captured images.

The crop sorting device of the present invention includes a transport unit that transports crops individually, a photographing unit that outputs photographed images of the crops that are transported individually, a size calculation unit that calculates a size grade of the crop from the photographed images as evaluation information of the crop, a surface abnormality calculation unit that calculates surface abnormalities of the crop from the photographed images as the evaluation information, and a sorting unit that sorts the crop based on the evaluation information, and the size calculation unit calculates the size grade based on the outline of the crop and the area of the internal region of the outline calculated from the photographed images.

In this configuration, the size calculation unit and the surface anomaly calculation unit can use a common captured image, simplifying the capture unit. Furthermore, the size calculation unit determines the size grade of the crop based on the contour of the crop and the area of the internal region of this contour, which can prevent the inconvenience of determining the size grade based on the total area, including the area of parts with little commercial value that arise due to irregular shapes.

Since many agricultural products are sold at a price determined by weight, the weight of the agricultural product is important as a criterion for sorting the agricultural products. An accurate weight cannot be calculated from a photographed image. However, by taking into account the inherent characteristics of the agricultural product, such as its density and shape, it is possible to create a correlation that enables the weight of the agricultural product to be accurately derived from the projected area of the agricultural product, which can be easily calculated from the photographed image. For this reason, in a preferred embodiment, the size calculation unit can calculate the weight of the agricultural product based on the contour and the area of the internal region calculated from the photographed image, and calculate the size grade based on the weight.

Surface abnormalities of agricultural crops have been visually inspected for a long time, and the evaluation criteria vary depending on conditions such as the type of agricultural crop, its variety, and growing season (early or late). For this reason, surface abnormalities must be evaluated using evaluation criteria that are set in consideration of such conditions. It is preferable that the evaluation criteria be set based on a photographed image of the agricultural crop having a surface abnormality to be judged at the appropriate time. For this reason, in a preferred embodiment, the surface abnormality calculation unit has a comparison calculation function that calculates the surface abnormality by comparing the agricultural crop area in the photographed image with a preselected reference image for abnormality judgment. The reference image for abnormality judgment here may be a photographed image of the agricultural crop that is actually considered to have a surface abnormality, or it may be a pseudo-created image of the agricultural crop that is actually considered to have a surface abnormality.

Surface abnormalities include deformations in the overall contour of a crop, such as bending of carrots, cucumbers, and eggplants. However, evaluating the overall contour of a crop all at once places a high burden on image analysis. This image analysis can be simplified by dividing the crop into tip, middle, and base areas according to the shape characteristics, and performing appropriate image analysis for each divided area. For this reason, in a preferred embodiment, the surface abnormality calculation unit calculates the surface abnormality based on the shape characteristics of the areas divided according to the overall geometric characteristics of the contour.

For surface abnormalities occurring in the tip shape of a crop, image analysis of the tip region of the crop is sufficient, and for surface abnormalities occurring at the base, image analysis of the base region of the crop is sufficient. For surface abnormalities occurring in the main region of the crop excluding the tip and base regions, image analysis of the main region of the crop is sufficient. For this reason, in a preferred embodiment, the surface abnormality calculation unit calculates the surface abnormality based on the shape characteristics of partial contours separated from the contour by shape characteristic geometric features.

The size and number of surface abnormalities caused by the occurrence of singular points such as spots on the surface of agricultural crops are important. Even a single large spot can be problematic, and even multiple small spots can be problematic. For this reason, in a preferred embodiment, the surface abnormality calculation unit calculates the surface abnormality based on the area and number of brightness singular points within the contour.

The importance of problematic size and surface abnormalities in agricultural crops may vary depending on the type of crop, its variety, and even the growing season, and as a result, the sorting conditions and sorting destinations also change. Therefore, it is desirable to be able to freely change the sorting conditions and perform various sorting operations. For this reason, in a preferred embodiment, the sorting unit is configured to sort the agricultural crops based on a combination of the size class and the surface abnormality, and distribute them to each of the pre-set sorting destinations.

FIG. 2 is a schematic perspective view of the crop sorting device. FIG. 2 is a functional block diagram showing a control system of the crop sorting device. FIG. 13 is a schematic diagram showing a screen for setting evaluation criteria for size determination. FIG. 13 is a schematic diagram showing a screen for setting an evaluation criterion for determining a contour shape. FIG. 13 is a schematic diagram showing a sorting destination setting screen. FIG. 4 is a schematic diagram showing a driving status display screen. FIG. 1 is an explanatory diagram for explaining a method for evaluating lumps. FIG. 13 is an explanatory diagram for explaining a method for evaluating a curved root. FIG. 1 is an explanatory diagram for explaining a method for evaluating fork roots.

Below, as an embodiment of the agricultural product sorting device, a carrot sorting device that sorts carrots based on size and surface abnormalities will be described. FIG. 1 is a schematic perspective view of the carrot sorting device. The carrot sorting device is composed of a supply unit 11, an alignment unit 12, a conveying unit 13, and a sorting unit 14. The supply unit 11 is formed as a storage hopper. The alignment unit 12 aligns the carrots conveyed upward from the supply unit 11 and sends them out one by one. The conveying unit 13 conveys the carrots sent out from the supply unit 11 to the sorting unit 14. The sorting unit 14 has eight sorting destinations formed along the conveying direction of the carrots. The sorting unit 14 distributes the conveyed carrots to appropriate sorting destinations based on evaluation information generated by the agricultural product evaluation process described later.

The carrot sorting device is provided with an operation panel 15 to set the speed and other settings of the device. Furthermore, since photographed images of carrots are used to calculate the size grade and surface abnormality level of carrots as evaluation information for carrots, an image capturing unit 2 including a camera for capturing images of the carrots being transported is provided above the transport unit 13.

Figure 2 shows a functional block diagram of the control system of the agricultural product sorting device. This control system includes a sorting machine control device 10. The sorting machine control device 10 receives control parameters from an operation panel 15 and a sorting control unit 100, and controls the carrot sorting device. The sorting control unit 100 acquires photographed images of the carrots from the photographing unit 2, calculates the size grade and surface abnormality state of the carrots, and sends the calculation results to the sorting unit 14 as evaluation information. The sorting control unit 100 includes a agricultural product evaluation unit 3, an evaluation criterion setting unit 4, and a reference image management unit 6. It also includes a touch panel unit 5 that functions as a display unit that allows the operator to input information and displays information to the operator.

The crop evaluation unit 3 has a size calculation unit 31 and a surface abnormality calculation unit 32. The evaluation criteria used by the size calculation unit 31 and the surface abnormality calculation unit 32 are set by the evaluation criteria setting unit 4. The captured image of the carrot from the photographing unit 2 is provided to the size calculation unit 31 and the surface abnormality calculation unit 32. The size calculation unit 31 calculates the size grade of the carrot using the evaluation criteria and the captured image, and the surface abnormality calculation unit 32 calculates the surface abnormality of the carrot using the evaluation criteria and the captured image. The crop evaluation unit 3 generates evaluation information as an evaluation result of the carrot based on the calculation results of the size calculation unit 31 and the surface abnormality calculation unit 32, and outputs it to the sorting unit 14.

The size calculation unit 31 calculates a size grade based on the outline of the carrot calculated from the photographed image of the carrot and the area of the internal region of this outline. The size grades are "SSS", "SS", "S", "M", "L", "LL", and "LLL", in order from smallest to largest. A grade selected from these is actually assigned to the sorting destination by the sorting unit 14. Note that a carrot that is much longer and thinner than a standard carrot is not necessarily considered to be of the same size grade even if it has the same area as a standard carrot. Also, depending on the type of vegetable or fruit, the size of some is evaluated by weight rather than by apparent size. For this reason, the size calculation unit 31 also has a function as an option to estimate and calculate the weight based on the outline calculated from the photographed image and the area within this outline, and to calculate the size grade from the estimated calculation result.

In this embodiment, the surface abnormality calculation unit 32 has three main image processing functions to calculate the surface abnormality of the carrot. The first function is a function to calculate a specific surface abnormality based on the shape characteristics of the outline of a specific part obtained by dividing the outline of the carrot region extracted from the photographed image into multiple parts. The second function is a function to calculate a specific surface abnormality based on the area and number of image singular points in the carrot region extracted from the photographed image. The third function is a function to calculate a specific surface abnormality based on the distribution degree of a specific color in a specific part of the carrot region extracted from the photographed image.

The size calculation unit 31 and surface abnormality calculation unit 32 that make up the agricultural crop evaluation unit 3 evaluate the carrots based on the evaluation criteria set by the evaluation criteria setting unit 4. The evaluation criteria are generated by the evaluation criteria setting unit 4 based on instruction information from the touch panel unit 5. The touch panel unit 5 generates instruction information based on operational input by the worker and provides it to the evaluation criteria setting unit 4.

The touch panel unit 5 has a display control unit 51, a flat panel display 50 abbreviated as FPD, and an operation unit 52. The flat panel display 50 functions as a touch panel. The operation unit 52 has software switches displayed on the flat panel display 50 and hardware switches provided around the flat panel display 50. The display control unit 51 generates various information to be displayed on the flat panel display 50. Furthermore, the display control unit 51 outputs processed information obtained by processing operation input by the operation unit 52 to other functional units. In other words, the touch panel unit 5 forms a graphic user interface for the sorting control unit 100.

The reference image management unit 6 manages the reference images described below. The reference images captured from the reference image management unit 6 are displayed on the flat panel display 50. One example of a reference image is an actual reference image that can be visually identified as a carrot, and is a photographed image showing various sizes of carrots or an approximate outline of a reference image representing a carrot. Instead of a photographed image, an illustrated image may be used as the actual reference image. Another example of a reference image is a photographed image of a carrot showing various surface abnormalities (an abnormal reference image). For this abnormal reference image, an illustrated image may also be used instead of a photographed image.

Next, we will explain the evaluation criteria generation process, which generates evaluation criteria through cooperation between the reference image management unit 6, the touch panel unit 5, and the evaluation criteria setting unit 4.

(Evaluation criteria for size evaluation)
In the process of generating an evaluation standard for size evaluation, a screen of a flat panel display 50 as shown in FIG. 3 is used. In the center of the screen, an actual reference image showing a carrot of a standard size is displayed, and a plurality of (four in FIG. 3) approximate outlines of the carrot that are simplified in outline are displayed superimposed on the actual reference image. These approximate outlines are also managed by the reference image management unit 6. In addition, in the lower region of the screen, a first horizontal bar for setting the grade of the area (projected area) of the carrot and a second horizontal bar for setting the grade of the thickness (width) of the carrot are displayed. A plurality of (three in FIG. 3) area cursors are arranged on the first horizontal bar so as to be movable along the first horizontal bar by the operator's operation. A plurality of (three in FIG. 3) thickness cursors are also arranged on the second horizontal bar so as to be movable along the second horizontal bar by the operator's operation. The plurality of area cursors are for setting the area grades. The position of each area cursor on the first horizontal bar is linked to the area of the approximate outline assigned to each area cursor, and the area grades such as S, M, L, and LL are classified according to the position of each area cursor. Similarly, the thickness cursors set the thickness grade. The position of each thickness cursor on the second horizontal bar is linked to the width of the approximate outline assigned to each area cursor, and the thickness grades such as S, M, L, and LL are classified according to the position of each thickness cursor. Depending on the position of this thickness cursor, the size grade can be limited by the thickness, rather than setting the size grade only by the area. This makes it possible to lower the grade of a carrot that is too thin even if it has the same area.

Each approximate outline is enlarged or reduced by operating (operation input) the area cursor and thickness cursor. The final adjusted approximate outlines, which are the multiple adjusted outlines, are sent to the evaluation criterion setting unit 4. The evaluation criterion setting unit 4 processes data related to the adjusted outlines as instruction information, generates an evaluation criterion related to size, and sends it to the agricultural crop evaluation unit 3. This evaluation criterion related to size is set in the size calculation unit 31. The size calculation unit 31 compares the image processing results of the captured image sent from the photography unit 2 with the evaluation criterion, and calculates the size grade of the carrots being transported.

Specifically, the adjusted outline includes one or more enlarged outlines larger than an actual reference image (an example of a reference image) showing a carrot of a standard size, one or more reduced outlines smaller than the actual reference image, and a standard outline having an area between the enlarged outline and the reduced outline. The enlarged outline serves as an evaluation criterion for large sizes (LL or L) in terms of the area and thickness of the carrot, the reduced outline serves as an evaluation criterion for small sizes (S or SS) in terms of the area and thickness of the carrot, and the standard outline serves as an evaluation criterion for medium sizes (M) in terms of the area and thickness of the carrot. Note that instead of various outlines such as the reduced outline, standard outline, and enlarged outline, data of a reference image corresponding to each outline may be sent to the evaluation criterion setting unit 4 as instruction information. In this case, the evaluation criterion setting unit 4 generates an evaluation criterion based on the data of the reference image sent as instruction information and sets it in the agricultural product evaluation unit 3.

(Evaluation criteria for evaluating surface abnormalities)
In the process of generating an evaluation standard for evaluating surface abnormalities, for example, a screen of a flat panel display 50 as shown in FIG. 4 is used. In the example shown in FIG. 4, a group of abnormal reference images (including non-abnormal reference images for comparison) showing various forms of carrots are arranged in a matrix (5 rows x 6 columns in FIG. 4), and a horizontal judgment bar is arranged in the area below the group. A judgment cursor is arranged on the horizontal judgment bar so as to be movable along the horizontal judgment bar by an operator's operation. The judgment cursor can be moved to the boundary position of each column of carrots arranged in the matrix. This screen is used to judge the state of a bump as a surface abnormality of a carrot. Therefore, in the central area, carrots with bump states ranging from normal to abnormal are arranged in stages. In the rightmost column in the central area, carrots considered to be the most normal grade (OK) are arranged, and carrots considered to be the most abnormal grade (NG) are arranged in the leftmost column. In each column, carrots of the same grade but different normal or abnormal states are arranged. That is, they are displayed in order according to the magnitude of the abnormality. This allows the operator to easily visually recognize the level of abnormality of various types of carrots. The abnormal reference images are managed in the reference image management unit 6 with attribute values that indicate the level of abnormality.

The operator moves the judgment cursor to a position that is the boundary between OK and NG. Based on the final position of the judgment cursor, the touch panel unit 5 generates instruction information for evaluating the lumps and sends it to the evaluation criterion setting unit 4. Based on the instruction information, the evaluation criterion setting unit 4 generates evaluation criteria for lumps, which are an example of a surface abnormality, and sends it to the agricultural product evaluation unit 3. This evaluation criterion for lumps is set in the surface abnormality calculation unit 32. The surface abnormality calculation unit 32 processes the captured image sent from the imaging unit 2, and compares the image processing results with the set evaluation criteria to calculate the surface abnormality state of the photographed carrot.

Surface abnormalities on carrots include dents and curved roots in addition to lumps, but even if these are used as selection criteria, the operator can generate and set the desired evaluation criteria using the abnormal reference images and judgment cursor, as described above.

Based on the size class calculated by the size calculation unit 31 and the surface abnormality state calculated by the surface abnormality calculation unit 32, evaluation information specifying the sorting destination is created and provided to the sorting unit 14.

To set the sorting destinations in the sorting unit 14, for example, a sorting destination setting screen as shown in FIG. 4 is used. In this example, there are eight sorting destinations, and the sorting conditions are size class and type of surface abnormality. Size classes ranging from SSS to LLL can be selected as size classes, and surface abnormality types such as no abnormality, curved root, forked root, stain, scratch, poor coloring, and blue neck can be selected. For example, depending on the settings, carrots with size class M and no surface abnormalities are sorted to sorting destination "1," and carrots with size class L and abnormal lumps are sorted to sorting destination "5."

The operating status of the carrot sorting device can be confirmed and changed through the operating screen shown in FIG. 6. In this embodiment, the central area of the operating screen displays the captured image from the image capture unit 2. The lower area displays a group of buttons for adjusting the processing speed of the device. The group of buttons includes a stop button, a pause button, a start button, a speed down button, and a speed up button.

(Image Processing Algorithm)
An example of an image processing algorithm implemented in the crop assessment unit 3 for detecting surface anomalies will now be described.
(1) Bumps and dents (peripheral area)
In detecting lumps and dents as surface abnormalities of a carrot as shown in FIG. 7, the tip and root regions of the carrot are excluded. The lengths of the excluded tip and root regions are each p% of the total length. This p can be set arbitrarily by the operator. The remaining intermediate region excluding the tip and root regions is the detection target region. In an actual photographed image, the tip and root regions may be reversed, so the tip and root regions do not need to be distinguished. Alternatively, since a carrot is cone-shaped, the image may be rotated so that the tip region is automatically on one side. The evaluation of the lumps in the outer peripheral region shown in FIG. 7 is performed by fragmenting the inner region of the contour of the photographed image of the carrot, which corresponds to the cross section of the carrot, in the transverse direction with a width of a predetermined number of pixels, and based on the length from the virtual center line P1 of the carrot to the apex of each fragment. Specifically, the maximum value in the envelope of the apex of each fragment within a predetermined range is found. Furthermore, a judgment circle of a predetermined radius (indicated by r in FIG. 7) is set with the apex of the fragment having this maximum value as its center. The ratio of the carrot area to the background area in this judgment circle is calculated, and if the ratio of the carrot area is equal to or less than the bump threshold, it is judged to be a bump. If the number of bumps extracted in this way exceeds the bump judgment value, the carrot is judged to have a surface abnormality (bump) and is selected. Note that the evaluation of dents is similar to that of bumps, and instead of the maximum value in the envelope of the vertices of each fragment within a specified range, the minimum value is calculated, and the dents are evaluated in the same manner.

(2) Bumps and dents (inner area)
To detect bumps and dents in the internal region of a carrot (the internal region in a photographed image of a carrot), edge detection is used, utilizing the brightness change that occurs at the boundary line of the bump or dent. If the area of the edge-detected region is equal to or greater than a predetermined value, it is deemed to be a bump or dent, and if the number of bumps or dents exceeds a bump (dent) judgment value, the carrot in the photographed image is evaluated as having a surface abnormality (bump, dent) and is selected out. Note that blemishes on the surface of the carrot are also evaluated in a similar manner.

(3) Curved Roots In detecting curved roots as a surface abnormality of a carrot as shown in FIG. 8, the tip and root regions of the carrot are also excluded. An approximation line of the outer contour of the middle region, which is the region to be evaluated, is calculated. Here, the approximation line is a line connecting the boundary point between the root region and the middle region and the boundary point between the middle region and the tip region. Next, the deviation of the extracted outer contour from the approximation line is calculated. In calculating this deviation, the length of each fragment (the length from the virtual center line P1 to the apex) obtained by fragmenting the inner region of the contour of the photographed image of the carrot with a width of a predetermined number of pixels is used. The area between the approximation line and the apex of the fragment (the bulging portion is classified as a positive area and the depressed portion is classified as a negative area) is used as the deviation area and is integrated over all the fragments. Note that the length of the fragments in the region close to the root region or the tip region contains a large error, so the averaged length is used. If the integrated deviation area exceeds the curved root judgment value, the carrot is evaluated as having a surface abnormality (curved root) and is selected.

(4) Forks As shown in Fig. 9, forks are detected as surface abnormalities of carrots by setting transverse lines (pixel lines shown by dotted lines in Fig. 9) at intervals of a predetermined number of pixels along the longitudinal direction of the carrot, and if a carrot part and a background part are repeated on the transverse line, the transverse line is regarded as a fork crossing line. If the number of these fork crossing lines exceeds the fork judgment value, the carrot is evaluated as having a surface abnormality (forks) and is selected. The number of these fork crossing lines represents the length (number of lines x predetermined number of pixels), so in Fig. 8, the fork judgment value is represented by the length q, and the actually calculated length (number of fork crossing lines) is represented by r.

(5) Scratches Edge detection is used to detect forks as surface abnormalities of carrots, in the same way as detecting lumps and dents in internal regions. Closed regions obtained by edge detection are regarded as scratch regions, and the lengths and number of scratch regions are calculated. If the number of scratch regions having lengths exceeding the first scratch judgment value exceeds the second scratch judgment value, the carrot is evaluated as having a surface abnormality (scratches) and is selected.

(6) Coloring Defects As with the detection of blemishes and scratches, edge detection is also used to detect coloring defects as a surface abnormality of carrots. Threshold values for hue, saturation, and brightness that indicate coloring defects are set as edge thresholds for edge detection. If the area of a coloring defect region obtained by edge detection exceeds the coloring defect judgment value, the carrot is evaluated as having a surface abnormality (coloring defect) and is selected.

(7) Green Neck When detecting a green neck as a surface abnormality of a carrot, the carrot is divided into a tip region, a middle region, and a base region. If the tip region and the base region are not distinguished in the captured image, both the tip region and the base region become the detection target region, and if the tip region and the base region are distinguished, the base region becomes the detection target region. In the detection target region, the captured image is ternarized with the background color, a bluish color, and an orange color. If the proportion of blue-colored pixels in the ternarized image data exceeds the blue neck judgment value, the carrot is evaluated as having a surface abnormality (green neck) and is selected.

In the image processing algorithm described above, various judgment values for detecting surface abnormalities may be set as default values. Alternatively, an operator may select the boundaries of surface abnormalities from a group of abnormal reference images in which surface abnormalities are arranged by abnormality level as shown in FIG. 5, and the surface abnormality judgment values and various thresholds may be set based on the selection result. For example, in the case of detecting coloring defects, data for the threshold value of coloring defects is created from a photographed image that is considered to have a coloring defect and is selected by the operator from a group of photographed images showing actual coloring defects in stages as shown in FIG. 5.

[Another embodiment]
(1) In the above-described embodiment, carrots are used as the agricultural product to be sorted. However, other fruits and vegetables may also be used.
(2) The various functional blocks shown in FIG. 2 are for illustrative purposes only, and these functional blocks may be combined with other functional blocks or divided into multiple sub-blocks.

The configurations disclosed in the above embodiment (including other embodiments, the same applies below) can be applied in combination with configurations disclosed in other embodiments, so long as no contradiction arises. Furthermore, the embodiments disclosed in this specification are merely examples, and the present invention is not limited to these embodiments. They can be modified as appropriate within the scope of the purpose of the present invention.

The present invention can be applied to a crop sorting device that uses photographed images of crops to evaluate the crops and sort them based on the evaluation results.

2: Photographing section 3: Crop evaluation unit 4: Evaluation standard setting section 5: Touch panel unit 6: Reference image management section 10: Sorting machine control device 11: Supply section 12: Alignment section 13: Transport section 14: Sorting section 31: Calculation section 32: Surface abnormality calculation section 50: Flat panel display 51: Display control section 52: Operation section 100: Sorting control unit

Claims (6)

A crop sorting device, comprising:
A conveying unit that conveys the agricultural products individually;
an image capturing unit that captures and outputs images of the agricultural products that are individually transported;
a size calculation unit that calculates a size class of the agricultural crop as evaluation information of the agricultural crop from the captured image;
a surface abnormality calculation unit that calculates a surface abnormality of the crop as the evaluation information from the captured image;
A sorting unit that sorts the agricultural products based on the evaluation information;
Equipped with
The size calculation unit calculates the size class based on an outline of the crop and an area of an internal region of the outline calculated from the captured image ,
The size calculation unit calculates the weight of the crop based on the contour and the area of the internal region calculated from the captured image, and calculates the size class based on the weight. A crop sorting device, comprising:
A conveying unit that conveys the agricultural products individually;
an image capturing unit that captures and outputs images of the agricultural products that are individually transported;
a size calculation unit that calculates a size class of the agricultural crop as evaluation information of the agricultural crop from the captured image;
a surface abnormality calculation unit that calculates a surface abnormality of the crop as the evaluation information from the captured image;
A sorting unit that sorts the agricultural products based on the evaluation information;
Equipped with
The size calculation unit calculates the size class based on an outline of the crop and an area of an internal region of the outline calculated from the captured image,
The surface abnormality calculation unit is a crop sorting device having a comparison calculation function that calculates the surface abnormality by comparing the crop area in the captured image with a preselected reference image for abnormality determination . A crop sorting device, comprising:
A conveying unit that conveys the agricultural products individually;
an image capturing unit that captures and outputs images of the agricultural products that are individually transported;
a size calculation unit that calculates a size class of the agricultural crop as evaluation information of the agricultural crop from the captured image;
a surface abnormality calculation unit that calculates a surface abnormality of the crop as the evaluation information from the captured image;
A sorting unit that sorts the agricultural products based on the evaluation information;
Equipped with
The size calculation unit calculates the size class based on an outline of the crop and an area of an internal region of the outline calculated from the captured image,
The surface anomaly calculation unit calculates the surface anomaly based on shape characteristics of parts divided by the overall geometric characteristics of the contour . A crop sorting device, comprising:
A conveying unit that conveys the agricultural products individually;
an image capturing unit that captures and outputs images of the agricultural products that are individually transported;
a size calculation unit that calculates a size class of the agricultural crop as evaluation information of the agricultural crop from the captured image;
a surface abnormality calculation unit that calculates a surface abnormality of the crop as the evaluation information from the captured image;
A sorting unit that sorts the agricultural products based on the evaluation information;
Equipped with
The size calculation unit calculates the size class based on an outline of the crop and an area of an internal region of the outline calculated from the captured image,
The surface anomaly calculation unit of the agricultural product sorting device calculates the surface anomaly based on shape characteristics of partial contours that are divided from the contour by shape characteristics and geometric features . The farm product sorting device according to claim 1 , wherein the surface anomaly calculation unit calculates the surface anomaly based on an area and a number of brightness singular points within the contour. The agricultural product sorting device according to claim 1 , wherein the sorting unit sorts the agricultural products based on a combination of the size class and the surface abnormality, and distributes the agricultural products to each pre-set sorting destination.

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