A betel nut screening method based on image recognition

Abstract

The present invention relates to the field of fruit screening technology, and more particularly to a betel nut screening method based on image recognition, comprising: determining a screening strategy based on a defect index; based on a comparison result showing that the defect index is less than a preset defect index, determining the defect type through image recognition and determining an implementation plan based on the defect type, including direct elimination or determining whether the betel nut appearance meets a preset standard based on a skin color variation coefficient; if the betel nut appearance is determined not to meet the preset standard, secondary determination of whether the betel nut appearance meets the preset standard based on a chromaticity difference; if the betel nut appearance meets the preset standard, determining whether the hardness of the betel nut meets the preset standard based on a hardness value; determining whether the water content of the betel nut meets the preset standard based on a density value; and classifying the betel nut whose water content meets the preset standard by weight to complete screening. The present invention improves the screening efficiency of betel nut.

Description

Technical Field

[0001] The present invention relates to the technical field of fruit screening, and in particular to a betel nut screening method based on image recognition. Background Art

[0002] Traditional betel nut products have coarse and hard fibers, and long-term chewing can have adverse effects on oral health, such as oral mucosal fibrosis and tooth wear. With increasing health awareness, consumer demand for betel nut products is gradually shifting towards healthier, softer, and better-tasting products.

[0003] Chinese patent application publication number: CN113976475A, discloses a vegetable and fruit selection and screening system and vegetable and fruit selection method, belonging to the field of image processing technology, specifically comprising: a transmission component including a feeding end and a sorting end, a shooting area is set in the middle position of the transmission component; the shooting end of the imaging device faces the shooting area; a robotic arm is set at the sorting end; multiple soft suction cups are connected to a walker, the walker is connected to one side of a fixed plate, and the other side of the fixed plate is connected to the clamping end of the robotic arm; the imaging device, the robotic arm and the soft suction claw are all electrically connected to a controller.

[0004] However, the existing technology has the following problems: the existing technology only detects the outer surface of the fruit, but does not consider the impact of possible problems inside the fruit on the quality. Summary of the Invention

[0005] To this end, the present invention provides a betel nut screening method based on image recognition, which is used to overcome the problem in the prior art that the detection of fruits is limited to whether there are defects on the surface of the fruit.

[0006] To achieve the above object, the present invention provides a betel nut screening method based on image recognition, comprising:

[0007] The betel nut is rotated and transported through an optical sorting device, photographed from multiple angles, and the defect index of the betel nut surface is obtained based on the captured images to determine the screening strategy;

[0008] Based on the comparison result of the defect index being less than the preset defect index, the defect type is determined through image recognition and an action plan is determined based on the defect type, including direct rejection, or determining whether the appearance of the betel nut meets the preset standard based on the skin color variation coefficient;

[0009] When it is determined that the appearance of the betel nut does not meet the preset standard, a second determination is made based on the color difference whether the appearance of the betel nut meets the preset standard;

[0010] Under the condition that the appearance of the betel nut meets the preset standard, use a hardness tester to measure the hardness of the betel nut, and determine whether the hardness of the betel nut meets the preset standard based on the hardness value;

[0011] Weigh the betel nut fruits that meet the hardness standard, measure the volume and calculate the density, and then determine whether the water content of the betel nut fruits meets the preset standard based on the density value;

[0012] Betel nuts whose moisture content meets the preset standards are classified by weight and screened.

[0013] Furthermore, a screening strategy is determined based on the defect index of a single betel nut, wherein if the defect index is less than a preset defect index, an execution plan is determined based on the defect type;

[0014] If the defect index is greater than or equal to the preset defect index, the betel nut is locked and directly removed.

[0015] Furthermore, an execution plan is determined according to the defect type. If the defect is rotten or moldy, the betel nut with the defect will be directly discarded.

[0016] If the defect is a slight scratch or dent on the surface, the skin color variation coefficient is used to determine whether the appearance of the betel nut meets the preset standards.

[0017] Furthermore, under the condition that the skin color variation coefficient is less than the first preset variation coefficient, it is determined that the appearance of the betel nut meets the preset standard, and whether the hardness of the betel nut meets the preset standard is determined based on the hardness value.

[0018] Furthermore, under the condition that the skin color variation coefficient is greater than or equal to the first preset variation coefficient, it is determined that the appearance of the betel nut does not meet the preset standard; and if the skin color variation coefficient is greater than or equal to the first preset variation coefficient and less than a second preset variation coefficient, whether the appearance of the betel nut meets the preset standard is secondarily determined based on the chromaticity difference;

[0019] If the skin color variation coefficient is greater than or equal to the second preset variation coefficient, the betel nut will be discarded.

[0020] Furthermore, under the condition that the color difference is less than a preset color difference, it is determined for the second time that the appearance of the betel nut meets the preset standard, and a hardness test is performed.

[0021] Furthermore, under the condition that the chromaticity difference is greater than or equal to the preset chromaticity difference, the betel nut fruits are screened out and stored. When the chromaticity difference of the stored betel nut fruits is less than the preset chromaticity difference, they are continued to be sent to the subsequent hardness detection link.

[0022] Furthermore, under the condition that the hardness value is less than a preset hardness value, it is determined that the hardness of the betel nut does not meet the preset standard, and the betel nut is cut open for internal quality inspection.

[0023] Furthermore, under the condition that the hardness value is greater than or equal to the preset hardness value, it is determined that the hardness of the betel nut meets the preset standard, and whether the water content of the betel nut meets the preset standard is determined based on the density value.

[0024] Furthermore, under the condition that the density value is greater than or equal to the preset density value, it is determined that the moisture content of the betel nut meets the preset standard, and the betel nut whose moisture content meets the preset standard is classified according to weight to complete the screening.

[0025] Compared with the existing technology, the beneficial effect of the present invention is that the present invention comprehensively screens betel nut fruits through multi-dimensional detection, including appearance detection, hardness detection and density detection. Through the multi-dimensional and multi-level screening method, comprehensive control of the quality of betel nut fruits is achieved, which significantly improves the screening efficiency and product quality, and reduces resource waste.

[0026] Furthermore, the present invention formulates different execution plans according to the defect index and defect type to avoid a one-size-fits-all screening method. When it is determined that the product does not meet the standards, a secondary judgment is performed based on the color difference to reduce the misjudgment rate.

[0027] Furthermore, the present invention stores and re-inspects betel nuts with excessive chromaticity differences, thereby avoiding waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a betel nut screening method according to an embodiment of the present invention;

[0029] Figure 2 This is a flowchart of an embodiment of the present invention for determining an execution plan based on a defect type;

[0030] Figure 3 This is a flow chart of an embodiment of the present invention for determining whether the appearance of a betel nut meets a preset standard;

[0031] Figure 4 This is a flow chart of an embodiment of the present invention for secondary determination of whether the appearance of betel nut meets preset standards. DETAILED DESCRIPTION

[0032] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below with reference to embodiments. It should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0033] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0034] It should be pointed out that the data in this embodiment are obtained by comprehensive analysis and evaluation of the historical test data and the corresponding historical test results of the three months before this test. It can be understood by those skilled in the art that the present invention can determine the above parameters for a single item by selecting the value with the highest proportion as the preset standard parameter based on the data distribution, using weighted summation to use the obtained value as the preset standard parameter, substituting each historical data into a specific formula and using the value obtained by the formula as the preset standard parameter or other selection methods, as long as the present invention can clearly define the different specific situations in the single determination process through the obtained values.

[0035] See also Figures 1 to 4 As shown, they are respectively a flow chart of a betel nut screening method according to an embodiment of the present invention; a flow chart of determining an execution plan according to a defect type according to an embodiment of the present invention; a flow chart of determining whether the appearance of betel nut meets a preset standard according to an embodiment of the present invention; and a flow chart of secondary determination of whether the appearance of betel nut meets a preset standard according to an embodiment of the present invention.

[0036] The betel nut screening method based on image recognition in an embodiment of the present invention includes:

[0037] Step S1: rotating betel nut through an optical sorting device, completing multi-angle photography, and determining a screening strategy based on a surface defect index of the betel nut obtained from the captured images;

[0038] Step S2: Based on the comparison result that the defect index is less than the preset defect index, the defect type is determined through image recognition and an implementation plan is determined according to the defect type, including direct rejection or determining whether the appearance of the betel nut meets the preset standard based on the skin color variation coefficient;

[0039] Step S3, when it is determined that the appearance of the betel nut does not meet the preset standard, a secondary determination is made based on the color difference whether the appearance of the betel nut meets the preset standard;

[0040] Step S4, under the condition that the appearance of the betel nut meets the preset standard, using a hardness meter to measure the hardness of the betel nut, and determining whether the hardness of the betel nut meets the preset standard based on the hardness value;

[0041] Step S5, weighing the betel nut that meets the hardness standard, measuring the volume and calculating the density, and determining whether the water content of the betel nut meets the preset standard based on the density value;

[0042] Step S6: Classify the betel nuts whose water content meets the preset standard according to weight and complete the screening.

[0043] Specifically, the optical sorting equipment, such as a drum-type fruit sorting machine, a conveyor belt-type fruit sorting machine, an airflow-type fruit sorting machine, etc., is not specifically limited, and only needs to meet the requirements of the equipment having rolling transmission, image recognition and screening capabilities.

[0044] Specifically, the optical sorting equipment uses an industrial-grade digital camera to capture the surface image of the betel nut and obtains the defect index, skin color variation coefficient and chromaticity difference through visual algorithm analysis.

[0045] Specifically, in step S1, an image is taken every time the betel nut rotates by a preset angle of 30°, and the defect index of the betel nut surface is the ratio of the area of the defect to the surface area of the betel nut.

[0046] In the embodiment of the present invention, the preset angle is set to 30°, but the value is not limited thereto, and those skilled in the art may adjust the value according to actual needs.

[0047] Specifically, in step S5, an electronic scale is provided at the end of the transmission module of the optical sorting equipment to weigh the betel nut and upload the weight data to the control system. At the same time, a 3D camera or a multi-angle camera is used to photograph the betel nut, and a three-dimensional model is reconstructed through the point cloud data to calculate the volume.

[0048] Specifically, a screening strategy is determined based on the defect index of a single betel nut, wherein if the defect index is less than 5% of a preset defect index, an implementation plan is determined based on the defect type;

[0049] If the defect index is greater than or equal to the preset defect index, the betel nut is locked and directly removed.

[0050] In the embodiment of the present invention, the preset defect index is set to 5%, but the above value is not limited thereto, and those skilled in the art can adjust the value according to actual needs.

[0051] Specifically, the execution plan is determined according to the defect type, among which:

[0052] If the defect is rotten or moldy, the betel nut with defects will be directly removed;

[0053] If the defect is a slight scratch or dent on the surface, the skin color variation coefficient is used to determine whether the appearance of the betel nut meets the preset standards.

[0054] Specifically, the skin color variation coefficient is the ratio of the area of the abnormal skin color region to the surface area of the betel nut.

[0055] Specifically, the appearance of the betel nut is determined to meet the preset standard under the condition that the skin color variation coefficient is less than the first preset variation coefficient of 8%, and the hardness of the betel nut is determined to meet the preset standard based on the hardness value.

[0056] Specifically, if the skin color variation coefficient is greater than or equal to the first preset variation coefficient, it is determined that the appearance of the betel nut does not meet the preset standard; and if the skin color variation coefficient is greater than or equal to the first preset variation coefficient and less than 15% of the second preset variation coefficient, a secondary determination is made as to whether the appearance of the betel nut meets the preset standard based on the chromaticity difference.

[0057] If the skin color variation coefficient is greater than or equal to the second preset variation coefficient, the betel nut will be discarded.

[0058] In the embodiment of the present invention, the first preset coefficient of variation is 8%, and the second preset coefficient of variation is 15%, but the above values are not limited thereto. Those skilled in the art may adjust the values according to actual needs.

[0059] Specifically, under the condition that the color difference is less than the preset color difference of 3.5, it is determined for the second time that the appearance of the betel nut meets the preset standard, and a hardness test is performed.

[0060] Specifically, under the condition that the color difference is greater than or equal to the preset color difference, the betel nut fruits are screened out and stored. When the color difference of the stored betel nut fruits is less than the preset color difference, they are sent to the subsequent hardness detection link.

[0061] In the embodiment of the present invention, the preset chromaticity difference value is 3.5, but the above value is not limited thereto, and those skilled in the art can adjust the value according to actual needs.

[0062] Specifically, under the condition that the hardness value is less than the preset hardness value of 2kgf / cm², it is determined that the hardness of the betel nut does not meet the preset standard and is cut open for internal quality inspection.

[0063] In the embodiment of the present invention, the preset hardness value is 2 kgf / cm², but the above value is not limited thereto, and those skilled in the art can adjust the value according to actual needs.

[0064] Specifically, under the condition that the hardness value is greater than or equal to the preset hardness value, it is determined that the hardness of the betel nut meets the preset standard, and whether the water content of the betel nut meets the preset standard is determined based on the density value.

[0065] Specifically, under the condition that the density value is greater than or equal to the preset density value of 0.9g / cm³, it is determined that the moisture content of the betel nut meets the preset standard, and the betel nut whose moisture content meets the preset standard is classified according to weight to complete the screening.

[0066] In the embodiment of the present invention, the preset density value is 0.9 g / cm³, but the above value is not limited thereto, and those skilled in the art can adjust the value according to actual needs.

[0067] Specifically, betel nut fruits are classified according to weight, wherein if the weight of betel nut fruit is greater than or equal to 10g, it is determined to be large fruit;

[0068] If the weight of the betel nut is greater than or equal to 5g and less than 10g, it is determined to be a medium fruit;

[0069] If the weight of the betel nut is less than 5g, it is determined to be a small fruit.

[0070] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0071] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A betel nut screening method based on image recognition, characterized in that: include: The betel nut is rotated and transported through an optical sorting device, photographed from multiple angles, and the defect index of the betel nut surface is obtained based on the captured images to determine the screening strategy; Based on the comparison result of the defect index being less than the preset defect index, the defect type is determined through image recognition and an action plan is determined based on the defect type, including direct rejection, or determining whether the appearance of the betel nut meets the preset standard based on the skin color variation coefficient; When it is determined that the appearance of the betel nut does not meet the preset standard, a second determination is made based on the color difference whether the appearance of the betel nut meets the preset standard; Under the condition that the appearance of the betel nut meets the preset standard, use a hardness tester to measure the hardness of the betel nut, and determine whether the hardness of the betel nut meets the preset standard based on the hardness value; Weigh the betel nut fruits that meet the hardness standard, measure the volume and calculate the density, and then determine whether the water content of the betel nut fruits meets the preset standard based on the density value; Betel nut fruits with water content that meets the preset standards are sorted by weight and screened; Under the condition that the skin color variation coefficient is greater than or equal to the first preset variation coefficient, it is determined that the appearance of the betel nut does not meet the preset standard, and, If the skin color variation coefficient is greater than or equal to the first preset variation coefficient and less than the second preset variation coefficient, a secondary determination is made as to whether the appearance of the betel nut meets the preset standard based on the chromaticity difference; If the skin color variation coefficient is greater than or equal to the second preset variation coefficient, the betel nut is discarded; Under the condition that the color difference is less than the preset color difference, a second determination is made as to whether the appearance of the betel nut meets the preset standard, and a hardness test is performed; Under the condition that the color difference is greater than or equal to the preset color difference, the betel nut fruits are screened out and stored. When the color difference of the stored betel nut fruits is less than the preset color difference, they are sent to the subsequent hardness detection link.

2. The betel nut screening method based on image recognition according to claim 1, wherein The screening strategy is determined based on the defect index of a single betel nut, where: If the defect index is less than the preset defect index, determining an execution plan based on the defect type; If the defect index is greater than or equal to the preset defect index, the betel nut is locked and directly removed.

3. The betel nut screening method based on image recognition according to claim 2, wherein Determine the execution plan based on the defect type, where: If the defect is rotten or moldy, the betel nut with defects will be directly discarded; If the defect is a slight scratch or dent on the surface, the skin color variation coefficient is used to determine whether the appearance of the betel nut meets the preset standards.

4. The betel nut screening method based on image recognition according to claim 3, wherein Under the condition that the skin color variation coefficient is less than the first preset variation coefficient, it is determined that the appearance of the betel nut meets the preset standard, and whether the hardness of the betel nut meets the preset standard is determined based on the hardness value.

5. The betel nut screening method based on image recognition according to claim 4, wherein Under the condition that the hardness value is less than the preset hardness value, it is determined that the hardness of the betel nut does not meet the preset standard, and the betel nut is cut open for internal quality inspection.

6. The betel nut screening method based on image recognition according to claim 5, characterized in that: Under the condition that the hardness value is greater than or equal to the preset hardness value, it is determined that the hardness of the betel nut meets the preset standard, and whether the water content of the betel nut meets the preset standard is determined based on the density value.

7. The betel nut screening method based on image recognition according to claim 6, characterized in that: Under the condition that the density value is greater than or equal to the preset density value, it is determined that the water content of the betel nut meets the preset standard, and the betel nut whose water content meets the preset standard is classified according to weight to complete the screening.

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