Chroma-based lycopene nondestructive testing device and method
Through non-destructive detection devices and methods based on chroma, non-destructive detection of tomatoes is performed, combined with BP neural network algorithm for data processing and estimation, solving the problems of waste and cumbersome detection of tomatoes in traditional measurement methods, and achieving fast and accurate detection of lycopene content.
Patent Information
- Application Number
- CN202411809447.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-10
- Publication Date
- 2025-03-14
AI Technical Summary
Traditional lycopene measurement methods require destructive sampling, resulting in increased tomato waste and production costs, and the detection is cumbersome and time-consuming.
The non-destructive detection device and method based on chroma is adopted to conduct non-destructive detection of tomatoes through three-claw grippers and chromaticity and hardness recognition system sensors, and the data is processed and estimated in combination with the BP neural network algorithm.
The non-destructive, fast and efficient detection of lycopene content is achieved, avoiding tomato waste and cumbersome processes of laboratory operations, and improving detection accuracy and efficiency.
Smart Images
Figure CN119619425A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the technical field of agriculture, and in particular relates to a lycopene nondestructive detection device and method based on chromaticity. Background Art
[0002] As an important carotenoid existing in plant foods, lycopene has attracted much attention from all walks of life due to its remarkable performance in anti-oxidation, reducing the risk of cardiovascular diseases, reducing genetic damage and inhibiting the occurrence and development of tumors. Tomatoes are rich in lycopene, but its determination faces many challenges. The traditional lycopene determination method requires destructive sampling during the processing of tomato products. For example, the tomato fruits are picked and beaten into a homogenate for determination in the laboratory. This operation is not only cumbersome and time-consuming, but the optimal picking period for tomatoes is short. Destructive sampling will undoubtedly delay the picking process, resulting in a large amount of tomato waste and increased production costs.
[0003] The relevant invention patent uses the difference in spectral wavelengths to detect lycopene in fruits and vegetables. The specific wavelength spectroscopy method mainly focuses on measuring the reflection, transmission or absorption spectrum of an object at a specific wavelength. This method is generally used to analyze the response of a sample at a specific wavelength, rather than a comprehensive description of the color, and has certain limitations. However, there is another method for determination-the colorimetric method, which can evaluate the entire growth process of the fruit. Combined with the hardness data, the advanced BP neural network algorithm is used to conduct an in-depth analysis of the full maturity process data of different varieties of tomatoes from green to red, which can improve the universality of the detection method. In view of this, there is an urgent need for a non-destructive and efficient lycopene estimation method and supporting detection device to solve existing problems and meet the needs of agricultural production and related industrial development. Summary of the invention
[0004] In order to solve the problems raised in the above background technology, the object of the present invention is to provide a lycopene nondestructive detection device and method based on chromaticity.
[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:
[0006] A lycopene nondestructive detection device and method based on colorimetry, comprising a three-claw gripper, a colorimetry recognition system sensor, a hardness recognition system sensor and a data processing unit, wherein the three-claw gripper is located at the operating front end of the device, and the three claw arms of the three-claw gripper are evenly distributed, the colorimetry recognition system sensor and the hardness recognition system sensor are arranged inside the three-claw gripper, and the data processing unit is in a control host, which is located at the rear end or side of the device and is connected to the acquisition units of the colorimetry detection system and the hardness detection system through data cables.
[0007] Furthermore, the opening and closing action of the three-claw gripper is precisely controlled by a mechanical claw moving module, and the mechanical claw moving module is located at the rear end or bottom of the three-claw gripper mechanism. The three-claw gripper and the mechanical claw moving module are connected by a movable connecting rod of a mechanical claw fixing module. Damping bolts are provided at the joints of the three-claw gripper for adjusting the motion damping of the mechanical claw to ensure that the mechanical claw moves smoothly when fixing tomatoes.
[0008] Furthermore, the bottom of the three-jaw gripper is connected to a supporting structure assembly, which includes a cross bar and a base. The base is circular and has a round rod protruding in the middle. The base is located at the bottom of the device, and the cross bar is installed parallel to the base. The position and height of the cross bar are determined according to the working requirements of the three-jaw gripper mechanism, the colorimetry detection system, and the hardness detection system.
[0009] Furthermore, the colorimetric recognition system sensor is arranged on the recognition system carrying module, the recognition system carrying module is arranged in the three-claw gripper, the three-claw gripper is provided with four colorimetric recognition system sensors, the three colorimetric recognition system sensors are respectively installed in the inner middle position of the three claw arms, and the other colorimetric recognition system sensor is located at the bottom of the claw arm. The colorimetric recognition system sensor and the hardness recognition system sensor can accurately approach the surface of the tomato.
[0010] Furthermore, three of the hardness identification system sensors are respectively arranged on the inner surfaces of the three claw tips of the three-claw gripper, and the other hardness identification system sensor is located at the bottom of the claw arm.
[0011] Furthermore, when placing the tomato, it should be ensured that it is located at the center of the three-claw gripper.
[0012] Furthermore, a buffer component is provided on the inner surface of the three-claw gripper, and the buffer component is made of a fixing system fixing rubber, and the fixing system fixing rubber is tightly fitted to the inner surface of each claw arm of the mechanical claw assembly.
[0013] Furthermore, a nondestructive detection method for lycopene based on colorimetry comprises the following steps:
[0014] 1. Place the tomato in the triangular claw, operate the mechanical claw moving module of the three-claw gripper to rotate the three mechanical claws outward to create a channel, adjust the movable link of the mechanical claw fixing module to fix the position of the tomato, and adjust the metal claw moving fixing module to make the color recognition system sensor on the three-finger mechanical part close to the tomato, while ensuring that the hardness recognition system sensor is close to the tomato;
[0015] 2. Data collection: Turn on the detection device to make the color recognition system sensor and the hardness recognition system sensor measure the color and hardness parameters of the tomato at the same time to obtain relevant data;
[0016] 3. Data processing and estimation: The parameters of multiple varieties of tomatoes at different maturity states in the database are trained through the BP neural network algorithm to build a prediction model based on the collected data. The database contains the color, hardness and lycopene content data of multiple varieties of tomatoes at different maturity states.
[0017] Compared with the prior art, the present invention has the following technical advances:
[0018] The present invention discloses a lycopene nondestructive detection device and method based on chromaticity. The method estimates the lycopene content based on external characteristics such as hardness and chromaticity, and does not require destructive sampling of tomatoes, thereby achieving nondestructive detection and avoiding the waste of tomatoes caused by slurrying and testing after picking in traditional detection methods. It also saves cumbersome laboratory operation procedures and time costs. The present invention estimates the lycopene content by combining multi-dimensional data such as hardness, chromaticity and CI fruit color index. Compared with single indicator detection, the detection accuracy and efficiency are improved. In large-scale tomato planting bases or tomato product production lines, a large number of tomato samples can be tested in a timely manner to make decisions quickly. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.
[0020] In the attached picture:
[0021] Figure 1 A front view of a lycopene nondestructive detection device based on colorimetry provided by an embodiment of the present invention;
[0022] Figure 2 A side view of a lycopene nondestructive detection device based on colorimetry provided by an embodiment of the present invention;
[0023] Figure 3 A top view of a lycopene nondestructive detection device based on colorimetry provided in an embodiment of the present invention;
[0024] In the figure:
[0025] 1-Hardness recognition system sensor; 2-Chroma recognition system sensor; 3-Fixed system fixing rubber; 4-Mechanical claw moving module; 5-Mechanical claw fixing module movable connecting rod; 6-Identification system mounting module; 7-Damping bolt; 8-Cross bar; 9-Base. DETAILED DESCRIPTION
[0026] The following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.
[0027] In the description of the following embodiments, for the purpose of explanation rather than limitation, specific details such as specific system structures, technologies, etc. are proposed so as to thoroughly understand the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed description of well-known systems, devices and methods is omitted to prevent unnecessary details from hindering the description of the present application. It should be understood that when used in the present application specification and the appended claims, the term "including" indicates the existence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the existence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections. It should also be understood that the term "and / or" used in the present application specification and the appended claims refers to any combination of one or more of the items listed in the association and all possible combinations, and includes these combinations. As used in the present application specification and the appended claims, the term "if" can be interpreted as "when..." or "once" or "in response to determination" or "in response to detection" according to the context. Similarly, the phrases “if it is determined” or “if [described condition or event] is detected” may be interpreted to mean “upon determination” or “in response to determination” or “upon detection of [described condition or event]” or “in response to detection of [described condition or event],” depending on the context.
[0028] In addition, in the description of the present application specification and the attached claims, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. The reference to "one embodiment" or "some embodiments" described in the present application specification means that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in the different parts of this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.
[0029] The embodiments of the present invention will be described below in conjunction with the accompanying drawings.
[0030] Embodiment 1
[0031] The embodiment of the present invention provides a lycopene nondestructive detection device based on colorimetry, such as Figure 1 , 2As shown in , 3, it includes a three-claw gripper, a color recognition system sensor 2, a hardness recognition system sensor 1 and a data processing unit. The three-claw gripper is a key part for fixing tomatoes. It is located at the front end of the operation of the device as a whole. During the fixing process, sponges or other buffering materials are used to reduce the damage to the tomato fruit, so as to achieve in-situ measurement of tomato color, reduce the waste of tomato fruit, and ensure the subsequent normal growth or processing and utilization of tomatoes. The color recognition system sensor 2 is arranged inside the three-claw gripper, and there are four of them. The color parameters of tomatoes are obtained without damaging the appearance of tomatoes. The color information of each position can be obtained in a short time, so as to extract the color data more comprehensively and comprehensively, and ensure the accuracy of the measurement results. The hardness recognition system sensor 1 is also arranged inside the three-claw gripper, and is used to detect the hardness of the fruit and obtain the hardness data of the tomato fruit. The data processing unit is configured to calculate the lycopene content in the tomato by comparing the chromaticity-firmness-lycopene association data in the database through the BP neural algorithm, wherein the database contains the chromaticity, firmness and lycopene content data of multiple varieties of tomatoes at different maturity states, and the data processing unit is connected to the chromaticity recognition system sensor 2 and the firmness recognition system sensor 1 to receive the chromaticity and firmness data and output the lycopene content value.
[0032] like Figure 1 As shown, a mechanical claw moving module 4 is provided on the three-claw gripper for controlling the opening and closing movement of the mechanical claw to generate a channel for placing tomatoes. The movable link 5 of the mechanical claw fixing module accurately fixes the tomatoes by adjusting its position so that the color recognition system sensor 2 and the hardness recognition system sensor 1 can be accurately close to the tomato surface. The fixing system fixing rubber 3 is used to enhance the fixing stability of the mechanical claw on the tomato while avoiding damage to the tomato. The recognition system carrying module 6 is used to carry the color recognition system sensor 2. The damping bolt 7 is used to adjust the motion damping of the mechanical claw to ensure that the mechanical claw moves smoothly when fixing tomatoes. The cross bar 8 and the base 9 provide structural support for the entire three-claw gripper.
[0033] like Figure 1 As shown, two of the color recognition system sensors 2 are respectively located at the top and bottom of the three-claw gripper, and the other two are distributed on the sides to achieve all-round collection of tomato color. The hardness recognition system sensor 1 is located near the color recognition system sensor 2 and can fit tightly to the surface of the tomato when the tomato is fixed, ensuring accurate acquisition of hardness data.
[0034] Embodiment 2
[0035] An embodiment of the present invention provides a lycopene nondestructive detection device based on colorimetry, comprising the following steps:
[0036] 1. Place the tomato in the triangular claw, operate the mechanical claw moving module 4 of the three-claw gripper to rotate the three mechanical claws outward to create a channel, adjust the movable link 5 of the mechanical claw fixing module to fix the position of the tomato, and adjust the metal claw moving fixing module to make the color recognition system sensor 2 on the three-finger mechanical part close to the tomato, and at the same time ensure that the hardness recognition system sensor 1 is close to the tomato;
[0037] 2. Data collection: Turn on the detection device to make the color recognition system sensor 2 and the hardness recognition system sensor 1 measure the color and hardness parameters of the tomato at the same time to obtain relevant data;
[0038] 3. Data processing and estimation: The parameters of multiple varieties of tomatoes at different maturity states in the database are trained through the BP neural network algorithm to build a prediction model based on the collected data. The database contains the color, hardness and lycopene content data of multiple varieties of tomatoes at different maturity states.
[0039] like Figure 1 and Figure 2 As shown, the tomato should be placed in the center of the three-claw gripper to ensure that the color recognition system sensor 2 and the hardness recognition system sensor 1 can collect data evenly. When adjusting the position of the mechanical claw and the sensor, excessive squeezing or damage to the tomato should be avoided to ensure the integrity of the tomato's appearance. During the collection process, the surrounding environment of the detection device should be kept stable to avoid strong light, vibration or other interference factors that affect the measurement results. During the measurement process, the color recognition system sensor 2 and the hardness recognition system sensor 1 should be ensured to work normally without abnormal data collection.
[0040] In specific operations, the training process of the BP neural network algorithm should use sample data of sufficient quantity and diversity to improve the accuracy and universality of the prediction model. The BP neural network algorithm module runs in the processor of the control host. It works closely with the database, uses the data in the database for training and optimization, and builds an accurate nonlinear mapping relationship model between color-hardness-lycopene content. After receiving the real-time detection data from the data receiving module, the module performs complex calculations and analysis based on the trained model to obtain an estimated result of the lycopene content.
[0041] Embodiment 3
[0042] Tables 1, 2 and 3 are the data tables of hardness, chroma, fruit color index and content of Provence, Busan 88 and Pink Jubilee at different stages. According to the experimental measurement results, the relevant structural parameters are optimized from the data of hardness, chroma, CI fruit color index and content.
[0043] Table 1 Data table of hardness, chroma, fruit color index and content of Provence at different stages
[0044]
[0045] Table 2 Data of hardness, chroma, fruit color index and content of Busan 88 at different stages
[0046]
[0047] Table 3 Hardness, chroma, fruit color index and content data of Fanxi at different stages
[0048]
[0049] Based on the above data, we can consider these data as a multivariable system and apply some mathematical methods to analyze and optimize. Here are some possible steps and mathematical formulas:
[0050] Assume hardness h, chroma c, CI color index i, content y, then we can establish the relationship:
[0051] y=x 0 +x 1 h+x 2 c+x 3 i+e
[0052] Among them, x 0 is the intercept term, which indicates the basic content when the hardness, chroma and CI fruit color index are all 0 (it may have no physical meaning in practice, but it has a role in the mathematical model), x 1 、x 2 、x 3 are the regression coefficients of firmness, chroma, and CI fruit color index, respectively, indicating the influence of these factors on the content; e is the error term, representing the influence of other factors or random errors not considered by the model on the content.
[0053] In practical applications, we estimate these coefficients x from data 1 、x 2 、x 3 to establish a specific prediction model.
[0054] Based on the above embodiments, it can be seen that the present invention estimates the lycopene content based on external features such as hardness and chromaticity through this method, without the need for destructive sampling of tomatoes, thus achieving non-destructive testing and avoiding the waste of tomatoes caused by the traditional testing method of making a homogenate after picking, while also saving cumbersome laboratory operation procedures and time costs. The present invention estimates the lycopene content by combining multi-dimensional data such as hardness, chromaticity and CI fruit color index. Compared with single indicator testing, the present invention improves detection accuracy and efficiency. In large-scale tomato planting bases or tomato product production lines, a large number of tomato samples can be tested in a timely manner to make quick decisions.
[0055] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0056] 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 with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of protection of the claims of the present invention.
Claims
1. A lycopene nondestructive detection device based on colorimetry, characterized in that: It includes a three-claw gripper, a chromaticity recognition system sensor, a hardness recognition system sensor and a data processing unit. The three-claw gripper is located at the operating front end of the device. The three claw arms of the three-claw gripper are evenly distributed. The chromaticity recognition system sensor and the hardness recognition system sensor are arranged inside the three-claw gripper. The data processing unit is in a control host. The host is located at the rear end or side of the device and is connected to the acquisition units of the chromaticity detection system and the hardness detection system through data cables.
2. The lycopene nondestructive detection device based on colorimetry according to claim 1, characterized in that: The opening and closing action of the three-claw gripper is precisely controlled by a mechanical claw moving module, and the mechanical claw moving module is located at the rear end or bottom of the three-claw gripper mechanism. The three-claw gripper and the mechanical claw moving module are connected by a movable connecting rod of a mechanical claw fixing module. Damping bolts are provided at the joints of the three-claw gripper to adjust the motion damping of the mechanical claw to ensure that the mechanical claw moves smoothly when fixing tomatoes.
3. The lycopene nondestructive detection device based on colorimetry according to claim 1, characterized in that: The bottom of the three-jaw gripper is connected to a supporting structure assembly, which includes a cross bar and a base. The base is circular and has a round rod protruding in the middle. The base is located at the very bottom of the device, and the cross bar is installed parallel to the base. The position and height of the cross bar are determined according to the working requirements of the three-jaw gripper mechanism, the colorimetry detection system, and the hardness detection system.
4. The lycopene nondestructive detection device based on colorimetry according to claim 1, characterized in that: The colorimetric recognition system sensor is arranged on the recognition system carrying module, the recognition system carrying module is arranged in the three-claw gripper, the three-claw gripper is provided with four colorimetric recognition system sensors, the three colorimetric recognition system sensors are respectively installed in the inner middle position of the three claw arms, and the other colorimetric recognition system sensor is located at the bottom of the claw arm. The colorimetric recognition system sensor and the hardness recognition system sensor can accurately approach the surface of the tomato.
5. The lycopene nondestructive detection device based on colorimetry according to claim 1, characterized in that: Three of the hardness identification system sensors are respectively arranged on the inner surfaces of the three claw tips of the three-claw gripper, and the other hardness identification system sensor is located at the bottom of the claw arm.
6. The lycopene nondestructive detection device based on colorimetry according to claim 1, characterized in that: When placing the tomato, ensure that it is located in the center of the three-claw gripper.
7. The lycopene nondestructive detection device based on colorimetry according to claim 1, characterized in that: The inner surface of the three-claw gripper is provided with a buffer component, and the buffer component is made of a fixing system fixing rubber, and the fixing system fixing rubber is tightly fitted to the inner surface of each claw arm of the mechanical claw assembly.
8. A colorimetric nondestructive detection method for lycopene according to claims 2-7, characterized in that: The following steps are involved:
1. Place the tomato in the triangular claw, operate the mechanical claw moving module of the three-claw gripper to rotate the three mechanical claws outward to create a channel, adjust the movable link of the mechanical claw fixing module to fix the position of the tomato, and adjust the metal claw moving fixing module to make the color recognition system sensor on the three-finger mechanical part close to the tomato, while ensuring that the hardness recognition system sensor is close to the tomato; 2. Data collection: Turn on the detection device to make the color recognition system sensor and the hardness recognition system sensor measure the color and hardness parameters of the tomato at the same time to obtain relevant data; 3. Data processing and estimation: The parameters of multiple varieties of tomatoes at different maturity states in the database are trained through the BP neural network algorithm to build a prediction model based on the collected data. The database contains the color, hardness and lycopene content data of multiple varieties of tomatoes at different maturity states.