Method and equipment for evaluating quality and maximum utilization rate of potatoes

Abstract

The invention discloses a potato quality and maximum utilization rate evaluation method, and relates to the technical field of food grading detection, and the method comprises the steps: obtaining the surface data of potatoes through a 2D camera, judging whether the potatoes have surface defects, obtaining the types of the surface defects of the potatoes for quality rating, obtaining the point cloud data of the potatoes through a 3D camera, and obtaining the maximum utilization rate of the potatoes. The method comprises the following steps of: obtaining the ellipsoid, fullness and the proportion of complete cutting units of the potatoes, obtaining the internal data of the potatoes through a multispectral camera, judging whether the potatoes have internal defects or not, and obtaining the water content and starch content of the potatoes. Meanwhile, the quality and defects of the potatoes and the number of the potatoes which can be cut into chips or other standard shapes can be evaluated on one production line in a visual inspection mode, the quality grade of the potatoes in one batch is evaluated, the overall structure is simple and efficient, and it is guaranteed that the high-quality potatoes are screened out for subsequent processing.

Description

Technical Field

[0001] The present invention relates to the technical field of food grading detection, in particular to a method for evaluating potato quality and maximum utilization rate and an evaluation device thereof. Background Art

[0002] Potatoes are one of the major crops widely grown and consumed around the world. Their output and economic value occupy an important position in global agriculture. Potatoes are not only a basic food source for humans, but also have a wide range of applications in food processing, feed, pharmaceuticals and other fields. In the potato processing process, accurate quality rating and maximum utilization evaluation are particularly important. Effective potato quality rating helps optimize the planting, harvesting and subsequent processing processes, and improves the market competitiveness of agricultural products.

[0003] Especially in the field of potato strip processing, accurate potato grading is crucial to improving production efficiency and product quality. As a fast food that is deeply loved by consumers, potato strips have a huge market demand. High-quality potatoes can not only improve the yield of French fries, but also ensure the consistency of the taste and appearance of French fries, thereby enhancing the commercial value and market competitiveness of the product. In actual production, factors such as the shape, size, and internal defects of potatoes will directly affect the quantity and quality of the strips. For example, potatoes with higher ellipsoidality and plumpness are more suitable for cutting into uniform strips, while potatoes without internal defects can ensure the yield and quality of the strips during the strip cutting process.

[0004] Traditional potato quality assessment methods rely on manual inspection, judging the quality of potatoes by observing their surface features such as appearance, shape and size. This method is inefficient, highly subjective and has large errors, making it difficult to meet the needs of modern agricultural production. With the development of agricultural technology, automated and precise potato quality assessment methods have become a research focus. In addition, the detection of internal defects in potatoes also has important economic significance. If problems such as internal sprouting, discoloration, voids and corruption cannot be detected in time, the quality of the entire batch of potato products will decline, affecting the profits of processing companies.

[0005] Therefore, we proposed a potato quality and maximum utilization evaluation method and evaluation equipment to solve the above problems. Summary of the Invention

[0006] The object of the present invention is to provide a steel structure square tube column welding structure to solve the problems raised by the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a steel structure square tube column welding structure, comprising the following operating steps: S1. Projecting a multispectral camera onto potatoes to obtain internal data of the potatoes and analyze whether the potatoes have internal defects; S2. Acquire surface data of the potato using a 2D camera, analyze the presence and types of surface defects, scan the potato using a 3D camera to obtain 3D point cloud data, fit the point cloud into a surface to reconstruct the 3D structure of the potato, extract edge points to construct the edge structure of the potato, and derive the center of mass coordinates, volume estimate, and mass estimate of the potato; S3. Based on the 3D point cloud data, capture any point on the periphery of the potato, calculate the major axis, minimum circumscribed cube, and minimum circumscribed ellipsoid of the potato, and evaluate the ellipsoidality and plumpness of the potato; S4. Calculate the number of complete cutting units with overlapping areas of the entire potato based on the three-dimensional point cloud data; S5. Comprehensively evaluate the quality and maximum utilization of potatoes based on their mass, ellipsoidality, plumpness, skin color, surface defects, internal defects, and the ratio of complete cut units.

[0008] Preferably, in step S1, a spectrometer projects light of different wavelengths and cooperates with a multispectral camera to perform a multispectral scan of the interior of the potato to determine whether the potato has internal defects such as internal sprouting, internal discoloration, internal cavities and / or internal corruption, and the detection result is recorded as a dimension in the potato quality rating and added to the rating in step S5.

[0009] Preferably, in step S1, the moisture content and starch content of the potato are determined by multispectral scanning based on the internal data of the potato acquired by the multispectral camera.

[0010] Preferably, in step S2, a scaling cube is constructed based on potato images in the database by using a shape descriptor method, and the shape characteristics of the potato are roughly judged based on the scaling cube as a dimension for evaluating potato quality and maximum utilization.

[0011] Preferably, in step S3, the following steps are included: S31. Based on the three-dimensional point cloud data, capture any point on the periphery of the potato, and calculate the major axis, minimum circumscribed cube, and minimum circumscribed ellipsoid of the potato based on a two-dimensional convex hull algorithm, a three-dimensional convex hull algorithm, and a rotating caliper algorithm; S32. Obtain the convex hull outer contour of the potato, compare the three-dimensional structural contour of the potato with the convex hull outer contour, calculate the volume of the unfilled blank area, and evaluate the ellipsoidality and plumpness of the potato based on the spatial three-dimensional characteristics of the blank area.

[0012] Preferably, in step S4, the following steps are included: S41, simulating and generating a cube at the center of mass of the potato, where the cube is the minimum cutting unit; S42. Using the minimum cutting unit as a standard body, the two opposite faces of the cube are expanded to the surrounding area by performing a region growing algorithm until the standard body fully fills the entire area of ​​the potato. S43. Taking a fixed number of standard bodies with fixed postures as a complete cutting unit, calculate the number of complete cutting units with overlapping areas of the entire potato.

[0013] Preferably, in step S5, when evaluating the quality and maximum utilization of potatoes, the preset amount of the complete cutting unit has an adjustment function, and the potato rating is changed in real time according to the adjusted result. After completing the evaluation of the quality and maximum utilization of potatoes, the tested potatoes are classified and collected according to their grades.

[0014] A device for evaluating potato quality and maximum utilization includes a vibrating hopper, a conveyor line, an inspection station, a sorting mechanism, and a collecting mechanism. The inspection station and the sorting mechanism are mounted on the conveyor line. The vibrating hopper is connected to one end of the conveyor line, and the collecting mechanism is connected to the other end of the conveyor line. The inspection station includes an internal inspection station and an external inspection station. The external inspection station is used to inspect the quality, shape, plumpness, skin color, surface defects, the proportion of potatoes that can be cut into potato strips of a preset length, and the proportion of potatoes that can be cut into potato slices of an effective circumference. The external inspection station includes at least one 2D camera and at least two 3D cameras. The two 3D cameras are fixedly arranged on opposite sides of the conveyor line, and the 2D camera is fixedly arranged above the conveyor line.

[0015] Preferably, a multispectral detection system is set up at the internal inspection station, including a multispectral camera and a multispectral light source. The direction of the conveyor line is illuminated and collected by the multispectral light source and the multispectral camera. The inspection station is surrounded by a light shield to form a dark field environment. The light source is fixed inside the light shield or at the lens of the 2D camera. The light shield includes an internal inspection light shield fixed on the outside of the internal inspection station and an external inspection light shield fixed on the outside of the external inspection station.

[0016] Preferably, the collecting mechanism includes a good product collecting mechanism and a defective product collecting mechanism, the good product collecting mechanism is used to collect good potatoes after testing, and the defective product collecting mechanism is used to collect defective potatoes after testing.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. By introducing advanced technologies such as 3D scanning and multispectral imaging, this invention can not only accurately reconstruct the three-dimensional structure of potatoes and evaluate their external characteristics such as volume and quality, but also detect potential internal defects, making potato quality assessment more comprehensive and accurate, thereby improving potato grading accuracy and reducing potato grading errors, meeting the needs of modern agricultural production; 2. The present invention uses visual inspection to assess potato quality, defects, and the number of potatoes that can be cut into French fries or other standard shapes on a single production line, thereby evaluating the quality grade of a batch of potatoes. The overall structure is simple and efficient. This multi-dimensional assessment method can help processing companies optimize production processes, reduce waste, and improve economic efficiency. By using multiple visual measurement methods to judge potato quality in multiple dimensions, high-quality potatoes are selected for subsequent processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of the detection process of a method for evaluating potato quality and maximum utilization rate according to the present invention; Figure 2 The present invention is a schematic front view of the overall structure of a device for evaluating potato quality and maximum utilization.

[0019] In the picture: 1. Vibrating hopper; 2. Conveyor line; 3. Internal inspection station; 4. External inspection station; 5. Sorting mechanism; 6. Good product collection mechanism; 7. Defective product collection mechanism; 301. Multispectral camera; 302. Multispectral light source; 303. Internal inspection light shield; 401. 2D camera; 402. 3D camera; 403. External inspection light shield. DETAILED DESCRIPTION

[0020] In the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be understood as a limitation on the present invention.

[0021] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be internal communication between two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described implementation regulations are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0023] The potato quality assessment in this invention is mainly divided into the following dimensions: (1) Quality: Since the density of potatoes of the same type in the same batch is relatively uniform, this part can be estimated through 3D point clouds, and the approximate quality can be calculated through volume detection; (2) Ellipsoidality: The three-dimensional structure of the potato is constructed from a 3D point cloud. The potato is fitted into a non-concave ellipsoid using a convex hull algorithm. Based on this ellipsoid, the ellipsoidality of the potato can be calculated. (3) Fullness: Based on the comparison between the non-concave ellipsoid fitted by the convex hull algorithm and the three-dimensional structure of the potato, the non-overlapping area is calculated as the unfilled blank area. The smaller this area is, the higher the fullness is. (4) Skin color: Based on the RGB recognition features in the image, a comprehensive traversal analysis of the potato surface is performed to obtain RGB parameters; (5) Surface defects: Based on the defect features in the image, the abnormal features on the potato surface are analyzed using traditional algorithms or deep learning algorithms to obtain various defect data, such as defect type, defect size, and defect distribution; (6) The ratio of the number of complete cutting units: Based on the three-dimensional characteristics of the potato and the preset minimum cutting unit, the total amount of cutting is calculated according to the long axis direction of the potato. According to the preset parameters, several minimum cutting units are adjusted to form a complete cutting unit. The number of complete cutting units that do not overlap each other is calculated. The volume ratio of all complete cutting units is calculated, and the ratio of the number of complete cutting units is calculated. The minimum cutting unit can be regarded as a rectangular block of 1 mm square. One of the purposes of calculating the complete cutting unit is to determine whether French fries of the right size can be cut. (7) Internal defects: Based on multi-spectral feature detection, internal defects of potatoes can be detected by spectra of different wavelengths and frequency bands to determine whether there are various defects such as internal sprouting, internal discoloration, internal cavities and / or internal corruption; (8) Moisture content and starch content: Based on the multispectral feature detection characteristics, the moisture content and starch content inside the potato can be analyzed.

[0024] In combination with the above dimensions, the present invention is equipped with corresponding hardware facilities; in the quality assessment conditions of (1) quality, (2) ellipsoidality, (3) plumpness and (6) ratio of the number of complete cut units of potatoes, it is necessary to scan the entire potato with a 3D camera; in the quality assessment conditions of (4) skin color and (5) surface defects of potatoes, it is necessary to photograph the entire surface of potatoes with a 2D camera; in the quality assessment conditions of (7) internal defects, (8) moisture content and starch content of potatoes, it is necessary to use a multispectral light source in combination with a multispectral camera for detection.

[0025] The overall structure of the present invention is described below. Figure 2 A device for evaluating potato quality and maximum utilization rate includes a vibrating hopper 1, a conveyor line 2, an inspection station (including an internal inspection station 3 and an external inspection station 4), a sorting mechanism 5 and a collecting mechanism; the inspection station and the sorting mechanism 5 are set up on the conveyor line 2, the vibrating hopper 1 is connected to one end of the conveyor line 2, and the collecting mechanism is connected to the other end of the conveyor line 2. The internal inspection station 3 is provided with a multispectral detection system, including a multispectral camera 301 and a multispectral light source 302. The direction of the conveyor line 2 is illuminated and collected by the multispectral light source 302 and the multispectral camera 301. The inspection station is surrounded by a light shield to form a dark field environment, and a light source is fixedly set inside the light shield or at the lens of the 2D camera 401.

[0026] The specific implementation is as follows: The vibrating hopper 1 is used for loading. After the potatoes are poured into the vibrating hopper 1, they enter the inspection sequence in sequence through the conveyor line 2. First, the potatoes pass through the internal inspection station 3. The potatoes are projected by the multispectral camera 301. The multispectral light source 302 projects light of different wavelength bands in cooperation with the multispectral camera 301 to identify the potatoes, obtain internal potato data, and judge various defects inside the potatoes (whether the potatoes have internal sprouting, internal discoloration, internal cavities and / or internal corruption). At the same time, the moisture content and starch content of the potatoes are detected by multispectral scanning. The moisture content and starch content will affect the fluffiness and taste of the potatoes after frying, and therefore will also serve as potato quality assessment standards. After the potato completes the internal inspection, if there is a problem with the potato inside, no further evaluation is required and it is directly regarded as a defective product.

[0027] After the potatoes have completed internal defect inspection, they continue to be actively transported by the conveyor line 2 into the inspection area of ​​the external inspection station 4. The external inspection station 4 is used to inspect the quality, shape, fullness, skin color, surface defects, the proportion of potatoes that can be cut into potato strips of preset length, and the proportion of potatoes that can be cut into potato slices of effective circumference. The external inspection station 4 includes at least one 2D camera 401 and at least two 3D cameras 402. The two 3D cameras 402 are fixedly arranged on both sides of the conveyor line 2 in opposite directions, and the 2D camera 401 is fixedly arranged above the conveyor line 2. The surface information of the potatoes is collected by the 2D camera 401 to analyze whether the potatoes have surface defects and the type of defects.

[0028] The potato is scanned by a 3D camera 402 to obtain three-dimensional point cloud data, the point cloud is fitted into a surface to reconstruct the three-dimensional structure of the potato, the edge points are extracted to construct the edge structure of the potato, and the center of mass coordinates, volume estimation, and mass estimation of the potato are obtained; based on the three-dimensional point cloud data, any point on the periphery of the potato is captured, and the major axis, minimum circumscribed cube, and minimum circumscribed ellipsoid of the potato are calculated based on the two-dimensional convex hull algorithm, the three-dimensional convex hull algorithm, and the rotating caliper algorithm, and the convex hull outer contour of the potato is obtained. The three-dimensional structure contour of the potato is compared with the convex hull outer contour to calculate the unfilled blank area. The volume of the potato is combined with the spatial three-dimensional characteristics of the blank area to judge the ellipsoidality and fullness of the potato; a cube is simulated at the center of mass of the potato, which is the minimum cutting unit. The two opposite faces of the cube are used to perform a region growing algorithm with the minimum cutting unit as the standard body to spread to the surrounding area until the standard body fully fills the entire area of ​​the potato. The standard body with a fixed number and fixed posture is used as a complete cutting unit to calculate the number of complete cutting units with overlapping areas of the entire potato. The above judgments (1) to (6) are made based on the surface information and the 3D point cloud to evaluate the quality of the potato.

[0029] According to the quality grade, the potatoes will pass through the slope behind the conveyor line 2. A sorting mechanism 5 is provided on the slope. The sorting mechanism 5 is an automatic leakage plate. A collecting mechanism is provided at the bottom of the slope (including a good product collecting mechanism 6 and a defective product collecting mechanism 7. The good product collecting mechanism 6 is used to collect good potatoes after inspection, and the defective product collecting mechanism 7 is used to collect defective potatoes after inspection). When the potatoes are high-quality good products, the leakage plate opens and the potatoes enter the good product collecting mechanism 6. When the potatoes are low-quality defective products, the leakage plate does not move, and the potatoes roll into the defective product collecting mechanism 7.

[0030] In order to ensure the lighting quality in the overall detection equipment, it is preferred to set an internal detection station light shield 303 and an external detection station light shield 403 outside the internal detection station 3 and the external detection station 4 respectively. It should be noted that regardless of whether the external detection station light shield 403 is set, the station should be equipped with an auxiliary light source to illuminate and brighten the passing potatoes to achieve the detection effect. Preferably, the potatoes should be illuminated in multiple directions and angles.

[0031] During the potato quality rating process, the shape descriptor method can be used to construct a scale cube based on potato images in the database. The approximate shape characteristics of the potato can be determined based on the scale cube, which serves as a dimension for evaluating potato quality and maximum utilization. Secondly, the inspectors have the function of adjusting the preset quantity of the complete cutting unit. The potato rating changes in real time according to the adjusted results, and all mechanical structures make quality judgments based on the changed parameters.

[0032] In the present invention, a 2D camera is used to obtain surface data of potatoes, thereby analyzing the surface data to determine whether the potatoes have surface defects and the types of surface defects for quality rating. A 3D camera is used to obtain point cloud data of potatoes, thereby analyzing the point cloud data to determine the ellipsoidality, plumpness, and proportion of complete cut units of the potatoes. A multispectral camera is used to obtain internal data of potatoes, thereby analyzing the internal data to determine whether the potatoes have internal defects and to determine the moisture content and starch content of the potatoes. By introducing advanced technologies such as 3D scanning and multispectral imaging, the present invention can not only accurately reconstruct the three-dimensional structure of potatoes and evaluate their external features such as volume and quality, but also detect whether there are potential defects inside them, making potato quality assessment more comprehensive and accurate, thereby improving the potato grading accuracy and reducing potato grading errors, meeting the needs of modern agricultural production. At the same time, it realizes the evaluation of potato quality, defects and the number of potatoes that can be cut into French fries or other standard shapes through visual inspection on a single production line, and evaluates the quality grade of a batch of potatoes. The overall structure is simple and efficient. This multi-dimensional evaluation method can help processing companies optimize production processes, reduce waste, and improve economic benefits. The quality of potatoes is judged in multiple dimensions through multiple visual measurement methods to ensure that high-quality potatoes are screened for subsequent processing.

[0033] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings, but it is easy for those skilled in the art to understand 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 can make equivalent changes or replacements to the relevant technical features, and the technical solutions after these changes or replacements will fall within the scope of protection of the present invention.

Claims

1. A method for evaluating potato quality and maximum utilization, characterized in that: The following steps are included: S1. Projecting a multispectral camera onto potatoes to obtain internal data of the potatoes and analyze whether the potatoes have internal defects; S2. Acquire surface data of the potato using a 2D camera, analyze the presence and types of surface defects, scan the potato using a 3D camera to obtain 3D point cloud data, fit the point cloud into a surface to reconstruct the 3D structure of the potato, extract edge points to construct the edge structure of the potato, and derive the center of mass coordinates, volume estimate, and mass estimate of the potato; S3. Based on the 3D point cloud data, capture any point on the periphery of the potato, calculate the major axis, minimum circumscribed cube, and minimum circumscribed ellipsoid of the potato, and evaluate the ellipsoidality and plumpness of the potato; S4. Calculate the number of complete cutting units with overlapping areas of the entire potato based on the three-dimensional point cloud data; S5. Comprehensively evaluate the quality and maximum utilization of potatoes based on their mass, ellipsoidality, plumpness, skin color, surface defects, internal defects, and the ratio of complete cut units.

2. The method for evaluating potato quality and maximum utilization according to claim 1, wherein: In step S1, a spectrometer projects light of different wavelengths and cooperates with a multispectral camera to perform a multispectral scan of the interior of the potato to determine whether the potato has internal defects such as internal sprouting, internal discoloration, internal cavities and / or internal corruption. The detection result is recorded as a dimension in the potato quality rating and included in the rating in step S5.

3. The method for evaluating potato quality and maximum utilization according to claim 2, wherein: In step S1, based on the internal data of potatoes acquired by a multispectral camera, the moisture content and starch content of potatoes are determined by multispectral scanning.

4. The method for evaluating potato quality and maximum utilization according to claim 1, wherein: In step S2, based on the potato images in the database, a scaling cube is constructed by the shape descriptor method, and the shape characteristics of the potato are roughly judged based on the scaling cube as a dimension for evaluating the potato quality and maximum utilization rate.

5. The method for evaluating potato quality and maximum utilization according to claim 1, wherein: In step S3, the following steps are included: S31. Based on the three-dimensional point cloud data, capture any point on the periphery of the potato, and calculate the major axis, minimum circumscribed cube, and minimum circumscribed ellipsoid of the potato based on a two-dimensional convex hull algorithm, a three-dimensional convex hull algorithm, and a rotating caliper algorithm; S32. Obtain the convex hull outer contour of the potato, compare the three-dimensional structural contour of the potato with the convex hull outer contour, calculate the volume of the unfilled blank area, and evaluate the ellipsoidality and plumpness of the potato based on the spatial three-dimensional characteristics of the blank area.

6. The method for evaluating potato quality and maximum utilization according to claim 1, wherein: In step S4, the following steps are included: S41, simulating and generating a cube at the center of mass of the potato, where the cube is the minimum cutting unit; S42. Using the minimum cutting unit as a standard body, the two opposite faces of the cube are expanded to the surrounding area by performing a region growing algorithm until the standard body fully fills the entire area of ​​the potato. S43. Taking a fixed number of standard bodies with fixed postures as a complete cutting unit, calculate the number of complete cutting units with overlapping areas of the entire potato.

7. The method for evaluating potato quality and maximum utilization according to claim 1, wherein: In step S5, when evaluating the quality and maximum utilization of potatoes, the preset amount of the complete cutting unit has an adjustment function, and the potato rating changes in real time according to the adjusted results. After completing the evaluation of the quality and maximum utilization of potatoes, the tested potatoes are collected and classified by grade.

8. A device for evaluating potato quality and maximum utilization, characterized in that: A method for evaluating potato quality and maximum utilization rate using any one of claims 1 to 7 comprises a vibrating hopper (1), a conveyor line (2), an inspection station, a sorting mechanism (5) and a collecting mechanism, wherein the inspection station and the sorting mechanism (5) are mounted on the conveyor line (2), the vibrating hopper (1) is connected to one end of the conveyor line (2), and the collecting mechanism is connected to the other end of the conveyor line (2), the inspection station comprises an internal inspection station (3) and an external inspection station (4), the external inspection station (4) is used to inspect the quality, shape, fullness, skin color, surface defects, proportion of potatoes that can be cut into potato strips of a preset length, and proportion of potatoes that can be cut into potato slices of an effective circumference, the external inspection station (4) comprises at least one 2D camera (401) and at least two 3D cameras (402), the two 3D cameras (402) are fixedly arranged on opposite sides of the conveyor line (2), and the 2D camera (401) is fixedly arranged above the conveyor line (2).

9. The device for evaluating potato quality and maximum utilization according to claim 8, characterized in that: The internal inspection station (3) is provided with a multispectral inspection system, including a multispectral camera (301) and a multispectral light source (302), and the direction of the conveyor line (2) is illuminated and collected by the multispectral light source (302) and the multispectral camera (301). The inspection station is surrounded by a light shield to form a dark field environment, and a light source is fixedly provided inside the light shield or at the lens of the 2D camera (401). The light shield includes an internal inspection light shield (303) fixed on the outside of the internal inspection station (3) and an external inspection light shield (403) fixed on the outside of the external inspection station (4).

10. The device for evaluating potato quality and maximum utilization according to claim 8, characterized in that: The collecting mechanism comprises a good product collecting mechanism (6) and a defective product collecting mechanism (7). The good product collecting mechanism (6) is used to collect good potatoes after testing, and the defective product collecting mechanism (7) is used to collect defective potatoes after testing.

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