Fruit and vegetable processing detection system based on spectral imaging technology

Abstract

The invention discloses a fruit and vegetable processing detection system based on a spectral imaging technology, and relates to the technical field of vegetable detection.The fruit and vegetable processing detection system comprises a spectral imaging module, a quality analysis module and a cultivation improvement module, and the spectral imaging module is used for collecting internal structure data of vegetables through the spectral imaging technology; the quality analysis module is used for processing the image data to judge the storage quality, and the cultivation improvement module is used for adjusting the pesticide application amount in the vegetable cultivation process. The spectral imaging module comprises an infrared spectrum emitting unit, an infrared spectrum imaging unit, a wormhole recognition module, a three-dimensional modeling module, a photographing unit, a high-definition imaging unit and a leaf area recognition module, the infrared spectrum emitting unit is used for penetrating through vegetables through infrared spectrums, and the infrared spectrum imaging unit is electrically connected with the wormhole recognition module. And the high-definition imaging unit is electrically connected with the three-dimensional modeling module. The system has the characteristic of accurate identification.

Description

Technical Field

[0001] The invention relates to the technical field of vegetable detection, and in particular to a fruit and vegetable processing detection system based on spectral imaging technology. Background Art

[0002] In the processing and production of dried fruit and vegetable products, raw material selection, processing dehydration and product terminal grading are key links to ensure high-quality products. In terms of establishing fruit and vegetable raw material grading methods, foreign countries started earlier, while China mainly focused on the construction of grading models, but the accuracy is not high and there is a lack of research on related non-destructive testing mechanisms. In the production process of dried fruit and vegetable products, relevant research institutions at home and abroad focus on the use of vacuum drying, low-temperature dehydration and other process links. Although the process can effectively remove the moisture from the product and better maintain the nutritional components, there are deficiencies in endpoint judgment, product raw materials and terminal grading.

[0003] When vegetables are attacked by insect pests, their internal structure and chemical composition will change, which will lead to changes in their spectral reflectance characteristics. Spectral imaging technology uses this principle to analyze the spectral reflectance information of vegetables in different bands and identify the insect hole area, thereby realizing insect hole detection. The existing technology only increases the amount of pesticides to reduce insect pests, and cannot grasp the relationship between vegetable insect holes and the amount of pesticides. Therefore, it is necessary to design a fruit and vegetable processing detection system based on spectral imaging technology for accurate identification. Summary of the invention

[0004] The object of the present invention is to provide a fruit and vegetable processing detection system based on spectral imaging technology to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a fruit and vegetable processing detection system based on spectral imaging technology, comprising a spectral imaging module, a quality analysis module, and a cultivation improvement module, wherein the spectral imaging module is used to collect internal structure data of vegetables using spectral imaging technology, the quality analysis module is used to process image data to determine preservation quality, and the cultivation improvement module is used to adjust the amount of pesticide applied during vegetable cultivation.

[0006] According to the above technical solution, the spectral imaging module includes an infrared spectrum emission unit, an infrared spectrum imaging unit, a wormhole recognition module, a three-dimensional modeling module, a camera unit, a high-definition imaging unit, and a leaf area recognition module. The infrared spectrum emission unit is used to penetrate vegetables using infrared spectrum, the infrared spectrum imaging unit is electrically connected to the wormhole recognition module, the high-definition imaging unit is electrically connected to the three-dimensional modeling module, and the leaf area recognition module is electrically connected to the three-dimensional modeling module; the infrared spectrum imaging unit is used to detect the reflection and scattering of the infrared spectrum to obtain the image information inside the vegetable, the wormhole recognition module is used to determine whether there are wormholes in the vegetable leaves by the degree of penetration of the infrared spectrum, the camera unit is used to scan the vegetables by shooting, the high-definition imaging unit is used to image the shot image, the three-dimensional modeling module is used to form a three-dimensional model of the overall structure of the vegetable according to the shot image, and the leaf area recognition module is used to identify the leaf area of ​​the vegetable stem on the vegetable structure;

[0007] The quality analysis module includes a wormhole counting module, an integrity analysis module, a vegetable model library, and a quality judgment module. The wormhole counting module is electrically connected to the wormhole identification module, the vegetable model library is electrically connected to the high-definition imaging unit, the integrity analysis module is electrically connected to the vegetable model library, and the quality judgment module is electrically connected to both the wormhole counting module and the integrity analysis module; the wormhole counting module is used to count the number of wormholes judged to exist in the vegetable leaves, the integrity analysis module is used to analyze the integrity of the leaf area of ​​the vegetable stem, the vegetable model library is used to store standard vegetable structure models for analysis and comparison with actual consumption conditions, and the quality judgment module is used to comprehensively analyze the preservation quality based on the number of wormholes in the vegetables and the integrity of the leaf area;

[0008] The cultivation improvement module includes a pesticide dosage calculation module, a data fine-tuning module, and an optimization determination module. The pesticide dosage calculation module is used to re-improve the pesticide application dosage suitable for this type of vegetable. The data fine-tuning module is used to modify the parameters of the pesticide in the cultivation environment of the vegetable. The optimization determination module is used to determine whether the improved vegetables can be sold and eaten normally.

[0009] According to the above technical solution, the working method of the system is:

[0010] S1. When the vegetables are processed, the vegetables are photographed using a photographing unit to obtain a standard vegetable structure model;

[0011] S2. After the vegetables enter the quality inspection, the infrared spectrum emission unit is used to scan the vegetables, the number of insect holes in the leaf area is counted, and the vegetables are photographed again using the camera unit to obtain the overall structure of the vegetables after quality inspection;

[0012] S3, comprehensive identification of preservation quality based on the number of insect holes and the degree of integrity of the leaf area;

[0013] S4. Improve the amount of pesticide applied to vegetables and apply the improved parameters to the vegetable model to confirm that the improved vegetables can be sold and eaten normally.

[0014] According to the above technical solution, in S3, the specific method for identifying the degree of integrity of the blade area is:

[0015] S3-1. The scanned standard vegetable structure model is processed along the leaf area to remove the wormhole area until the processed vegetable structure reaches the level of being inedible, which is recorded as 0% completeness, i.e. the worst quality. The model with this degree of completeness of the leaf area is the limit state, while the standard vegetable structure model is 100% completeness, i.e. the best quality;

[0016] S3-2, by collecting two different pesticide application amounts of vegetables and the leaf integrity scanned after the vegetables enter the quality inspection, the leaf integrity of the two times is between 0-100%, and the functional relationship between the pesticide application amount of the vegetable and the image model scanned after entering the quality inspection is obtained, the formula is:

[0017]

[0018] Among them, H x is the amount of pesticide applied to the vegetables during the first scan, H y is the amount of pesticide applied to the vegetable during the second scan, A% is the leaf integrity of the vegetable during the first scan, and B% is the leaf integrity of the vegetable during the second scan. Finally, k is obtained;

[0019] S3-3. After the functional relationship is obtained, the pesticide application amount H0 corresponding to 0% integrity, that is, when the model of the leaf area integrity degree is in the limit state, is obtained. This value is the minimum allowable pesticide application amount.

[0020] According to the above technical solution, in S3, the comprehensive identification method is: under the premise of ensuring that the amount of pesticide applied is greater than H0, the number of insect holes scanned after the vegetables enter the quality inspection is counted multiple times, and the preservation quality is judged according to the number of insect holes. The fewer the number of insect holes, the better the preservation quality. Since the number of insect holes and the amount of pesticide applied are not linearly positively correlated, the preservation quality is best when the number of insect holes decreases fastest with the change of the amount of pesticide applied.

[0021] According to the above technical solution, in S4, the specific method for improving the amount of pesticide applied to vegetables is: directly reduce the amount of pesticide applied for the first improvement, calculate the improved amount of pesticide applied for subsequent improvements, record the number of insect holes in the vegetables after the last improvement, and compare it with the number of insect holes in the vegetables after the second to last improvement. If it tends to decrease, do not change the improved amount of pesticide applied; if it tends to increase, correct the improved amount of pesticide applied according to the increase ratio.

[0022] According to the above technical solution, in S4, the pesticide application amount H is improved. n The calculation formula is:

[0023] When G n ≤G n―1 When H n =H n―1 ;

[0024] When G n >G n―1 hour,

[0025] Among them, H n―1 is the improved pesticide application rate obtained after the penultimate improvement, G n is the number of wormholes measured during the last improvement, G n―1 is the number of wormholes measured during the penultimate improvement, k is the weight parameter for the number of wormholes when calculating the improved pesticide application amount, p is the weight parameter for the trend of wormhole number when calculating the improved pesticide application amount, and n is the number of improvements.

[0026] According to the above technical solution, in S4, the specific method of applying the improved parameters to the vegetable model is: detect the new leaf area integrity according to the optimized pesticide application amount, update the vegetable model and conduct cultivation tests, and determine whether the improved pesticide application amount affects the normal growth of vegetables. If it does not affect the normal growth, continue to use the improved external temperature and humidity environment to conduct controlled variable experiments to ensure that the improved parameters can be applied.

[0027] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention utilizes infrared spectroscopy technology to detect holes in vegetable leaves that are eaten by insects, and the detection method does not require opening the vegetables, thereby maintaining the integrity of the vegetables. At the same time, the subsequent amount of pesticide application is adaptively adjusted according to the number of insect holes detected, ensuring that the amount of pesticide application matches the integrity of the vegetable leaves, avoiding excessive application of pesticides, and ensuring the health and safety of the vegetables. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0029] Figure 1 It is a schematic diagram of the overall module structure of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] See also Figure 1 , the present invention provides a technical solution: a fruit and vegetable processing detection system based on spectral imaging technology, including a spectral imaging module, a quality analysis module, and a cultivation improvement module, the spectral imaging module is used to collect internal structure data of vegetables using spectral imaging technology, the quality analysis module is used to process image data to determine preservation quality, and the cultivation improvement module is used to adjust the amount of pesticide applied during vegetable cultivation;

[0032] The spectral imaging module includes an infrared spectrum emission unit, an infrared spectrum imaging unit, a wormhole recognition module, a three-dimensional modeling module, a camera unit, a high-definition imaging unit, and a leaf area recognition module. The infrared spectrum emission unit is used to penetrate vegetables using infrared spectrum. The infrared spectrum imaging unit is electrically connected to the wormhole recognition module. The high-definition imaging unit is electrically connected to the three-dimensional modeling module. The leaf area recognition module is electrically connected to the three-dimensional modeling module. The infrared spectrum imaging unit is used to detect the reflection and scattering of the infrared spectrum to obtain the image information inside the vegetable. The wormhole recognition module is used to determine whether there are wormholes in the vegetable leaves by the degree of penetration of the infrared spectrum. The camera unit is used to scan the vegetables by shooting. The high-definition imaging unit is used to image the shot image. The three-dimensional modeling module is used to form a three-dimensional model of the overall structure of the vegetable according to the shot image. The leaf area recognition module is used to identify the leaf area of ​​the vegetable stem on the vegetable structure.

[0033] The quality analysis module includes an insect hole counting module, an integrity analysis module, a vegetable model library, and a quality judgment module. The insect hole counting module is electrically connected to the insect hole identification module, the vegetable model library is electrically connected to the high-definition imaging unit, the integrity analysis module is electrically connected to the vegetable model library, and the quality judgment module is electrically connected to the insect hole counting module and the integrity analysis module; the insect hole counting module is used to count the number of insect holes judged to exist in the vegetable leaves, the integrity analysis module is used to analyze the integrity of the leaf area of ​​the vegetable stem, the vegetable model library is used to store standard vegetable structure models for analysis and comparison with actual consumption conditions, and the quality judgment module is used to comprehensively analyze the preservation quality based on the number of insect holes in the vegetables and the integrity of the leaf area;

[0034] The cultivation improvement module includes a pesticide amount calculation module, a data fine-tuning module, and an optimization determination module. The pesticide amount calculation module is used to re-improve the pesticide application amount suitable for this type of vegetable. The data fine-tuning module is used to modify the parameters of the pesticide in the cultivation environment of the vegetable. The optimization determination module is used to determine whether the improved vegetables can be sold and eaten normally.

[0035] The system works as follows:

[0036] S1. When the vegetables are processed, the vegetables are photographed using a photographing unit to obtain a standard vegetable structure model;

[0037] S2. After the vegetables enter the quality inspection, the infrared spectrum emission unit is used to scan the vegetables, the number of insect holes in the leaf area is counted, and the vegetables are photographed again using the camera unit to obtain the overall structure of the vegetables after quality inspection;

[0038] S3, comprehensive identification of preservation quality based on the number of insect holes and the degree of integrity of the leaf area;

[0039] S4. Improve the amount of pesticide applied to vegetables and apply the improved parameters to the vegetable model to confirm that the improved vegetables can be sold and eaten normally;

[0040] In S3, the specific method for identifying the degree of integrity of the blade area is:

[0041] S3-1. The scanned standard vegetable structure model is processed along the leaf area to remove the wormhole area until the processed vegetable structure reaches the level of being inedible, which is recorded as 0% completeness, i.e. the worst quality. The model with this degree of completeness of the leaf area is the limit state, while the standard vegetable structure model is 100% completeness, i.e. the best quality;

[0042] S3-2, by collecting two different pesticide application amounts of vegetables and the leaf integrity scanned after the vegetables enter the quality inspection, the leaf integrity of the two times is between 0-100%, and the functional relationship between the pesticide application amount of the vegetable and the image model scanned after entering the quality inspection is obtained, the formula is:

[0043]

[0044] Among them, H x is the amount of pesticide applied to the vegetables during the first scan, H y is the amount of pesticide applied to the vegetable during the second scan, A% is the leaf integrity of the vegetable during the first scan, and B% is the leaf integrity of the vegetable during the second scan. Finally, k is obtained;

[0045] S3-3. After the functional relationship is obtained, the pesticide application amount H0 corresponding to 0% integrity, that is, when the model of the leaf area integrity degree is in the limit state, is obtained. This value is the minimum value allowed for the pesticide application amount;

[0046] In S3, the comprehensive identification method is: under the premise of ensuring that the amount of pesticide applied is greater than H0, the number of insect holes scanned after the vegetables enter the quality inspection is counted multiple times, and the preservation quality is judged according to the number of insect holes. The fewer the number of insect holes, the better the preservation quality. Since the number of insect holes and the amount of pesticide applied are not linearly positively correlated, the preservation quality is best when the number of insect holes decreases fastest with the amount of pesticide applied.

[0047] In S4, the specific method for improving the amount of pesticide applied to vegetables is: directly reducing the amount of pesticide applied in the first improvement, calculating the improved amount of pesticide applied in subsequent improvements, recording the number of insect holes in vegetables after the last improvement, and comparing it with the number of insect holes in vegetables after the penultimate improvement. If it tends to decrease, the improved amount of pesticide applied is not changed; if it tends to increase, the improved amount of pesticide applied is corrected according to the increase ratio;

[0048] In S4, the pesticide application amount H is improved n The calculation formula is:

[0049] When G n ≤G n―1 When H n =H n―1 ;

[0050] When G n >G n―1 hour,

[0051] Among them, H n―1 is the improved pesticide application rate obtained after the penultimate improvement, G n is the number of wormholes measured during the last improvement, Gn―1 is the number of wormholes measured during the penultimate improvement, k is the weight parameter of the number of wormholes in the calculation of the improved pesticide application amount, p is the weight parameter of the change trend of the number of wormholes in the calculation of the improved pesticide application amount, and n is the number of improvements;

[0052] In S4, the specific method of applying the improved parameters in the vegetable model is: detect the new leaf area integrity according to the optimized pesticide application amount, update the vegetable model and conduct cultivation tests, and determine whether the improved pesticide application amount affects the normal growth of vegetables. If it does not affect the normal growth, continue to use the improved external temperature and humidity environment to conduct controlled variable experiments to ensure that the improved parameters can be applied.

[0053] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0054] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A fruit and vegetable processing detection system based on spectral imaging technology, characterized in that: It includes a spectral imaging module, a quality analysis module, and a cultivation improvement module. The spectral imaging module is used to collect internal structure data of vegetables using spectral imaging technology, the quality analysis module is used to process image data to determine preservation quality, and the cultivation improvement module is used to adjust the amount of pesticide applied during the vegetable cultivation process.

2. The fruit and vegetable processing detection system based on spectral imaging technology according to claim 1, characterized in that: The spectral imaging module includes an infrared spectrum emission unit, an infrared spectrum imaging unit, a wormhole identification module, a three-dimensional modeling module, a camera unit, a high-definition imaging unit, and a leaf area identification module. The infrared spectrum emission unit is used to penetrate vegetables using infrared spectrum. The infrared spectrum imaging unit is electrically connected to the wormhole identification module. The high-definition imaging unit is electrically connected to the three-dimensional modeling module. The leaf area identification module is electrically connected to the three-dimensional modeling module. The infrared spectrum imaging unit is used to detect the reflection and scattering of the infrared spectrum to obtain the image information inside the vegetable. The wormhole identification module is used to determine whether there are wormholes in the vegetable leaves by the degree of penetration of the infrared spectrum. The camera unit is used to scan the vegetables by shooting. The high-definition imaging unit is used to image the shot image. The three-dimensional modeling module is used to form a three-dimensional model of the overall structure of the vegetable according to the shot image. The leaf area identification module is used to identify the leaf area of ​​the vegetable stem on the vegetable structure. The quality analysis module includes a wormhole counting module, an integrity analysis module, a vegetable model library, and a quality judgment module. The wormhole counting module is electrically connected to the wormhole identification module, the vegetable model library is electrically connected to the high-definition imaging unit, the integrity analysis module is electrically connected to the vegetable model library, and the quality judgment module is electrically connected to both the wormhole counting module and the integrity analysis module; the wormhole counting module is used to count the number of wormholes judged to exist in the vegetable leaves, the integrity analysis module is used to analyze the integrity of the leaf area of ​​the vegetable stem, the vegetable model library is used to store standard vegetable structure models for analysis and comparison with actual consumption conditions, and the quality judgment module is used to comprehensively analyze the preservation quality based on the number of wormholes in the vegetables and the integrity of the leaf area; The cultivation improvement module includes a pesticide dosage calculation module, a data fine-tuning module, and an optimization determination module. The pesticide dosage calculation module is used to re-improve the pesticide application dosage suitable for this type of vegetable. The data fine-tuning module is used to modify the parameters of the pesticide in the cultivation environment of the vegetable. The optimization determination module is used to determine whether the improved vegetables can be sold and eaten normally.

3. The fruit and vegetable processing detection system based on spectral imaging technology according to claim 2 is characterized in that: The system works as follows: S1. When the vegetables are processed, the vegetables are photographed using a photographing unit to obtain a standard vegetable structure model; S2. After the vegetables enter the quality inspection, the infrared spectrum emission unit is used to scan the vegetables, the number of insect holes in the leaf area is counted, and the vegetables are photographed again using the camera unit to obtain the overall structure of the vegetables after quality inspection; S3, comprehensive identification of preservation quality based on the number of insect holes and the degree of integrity of the leaf area; S4. Improve the amount of pesticide applied to vegetables and apply the improved parameters to the vegetable model to confirm that the improved vegetables can be sold and eaten normally.

4. The fruit and vegetable processing detection system based on spectral imaging technology according to claim 3 is characterized in that: In S3, the specific method for identifying the degree of integrity of the blade area is: S3-1. The scanned standard vegetable structure model is processed along the leaf area to remove the wormhole area until the processed vegetable structure reaches the level of being inedible, which is recorded as 0% completeness, i.e. the worst quality. The model with this degree of completeness of the leaf area is the limit state, while the standard vegetable structure model is 100% completeness, i.e. the best quality; S3-2, by collecting two different pesticide application amounts of vegetables and the leaf integrity scanned after the vegetables enter the quality inspection, the leaf integrity of the two times is between 0-100%, and the functional relationship between the pesticide application amount of the vegetable and the image model scanned after entering the quality inspection is obtained, the formula is: Among them, H x is the amount of pesticide applied to the vegetables during the first scan, H y is the amount of pesticide applied to the vegetable during the second scan, A% is the leaf integrity of the vegetable during the first scan, and B% is the leaf integrity of the vegetable during the second scan. Finally, k is obtained; S3-3. After the functional relationship is obtained, the pesticide application amount H0 corresponding to 0% integrity, that is, when the model of the leaf area integrity degree is in the limit state, is obtained. This value is the minimum allowable pesticide application amount.

5. The fruit and vegetable processing detection system based on spectral imaging technology according to claim 4 is characterized in that: In S3, the comprehensive identification method is: under the premise of ensuring that the amount of pesticide applied is greater than H0, the number of insect holes scanned after the vegetables enter the quality inspection is counted multiple times, and the preservation quality is judged according to the number of insect holes. The fewer the number of insect holes, the better the preservation quality. Since the number of insect holes and the amount of pesticide applied are not linearly positively correlated, the preservation quality is best when the number of insect holes decreases fastest with the change of the amount of pesticide applied.

6. The fruit and vegetable processing detection system based on spectral imaging technology according to claim 5 is characterized by: In S4, the specific method for improving the amount of pesticide applied to vegetables is: directly reduce the amount of pesticide applied for the first improvement, calculate the improved amount of pesticide applied for subsequent improvements, record the number of insect holes in the vegetables after the last improvement, and compare it with the number of insect holes in the vegetables after the second to last improvement. If it tends to decrease, do not change the improved amount of pesticide applied; if it tends to increase, correct the improved amount of pesticide applied according to the increase ratio.

7. The fruit and vegetable processing detection system based on spectral imaging technology according to claim 6 is characterized by: In said S4, the pesticide application amount H is improved n The calculation formula is: When G n ≤G n―1 When H n =H n―1 ; When G n >G n―1 hour, Among them, H n―1 is the improved pesticide application rate obtained after the penultimate improvement, G n is the number of wormholes measured during the last improvement, G n―1 is the number of wormholes measured during the penultimate improvement, k is the weight parameter for the number of wormholes when calculating the improved pesticide application amount, p is the weight parameter for the trend of wormhole number when calculating the improved pesticide application amount, and n is the number of improvements.

8. The fruit and vegetable processing detection system based on spectral imaging technology according to claim 7 is characterized in that: In S4, the specific method of applying the improved parameters in the vegetable model is: detect the new leaf area integrity according to the optimized pesticide application amount, update the vegetable model and conduct cultivation tests, determine whether the improved pesticide application amount affects the normal growth of vegetables, and if it does not affect the normal growth, continue to use the improved external temperature and humidity environment to conduct controlled variable experiments to ensure that the improved parameters can be applied.

Images