CN116242813A - Method and system for detecting pesticide residues on fruits and vegetables based on multispectral imaging technology - Google Patents

Abstract

The invention provides a method and a system for detecting pesticide residues on fruits and vegetables based on a multispectral imaging technology, and relates to the field of intelligent agriculture. A method for detecting pesticide residues on fruits and vegetables based on a multispectral imaging technology comprises the following steps of constructing a multispectral fluorescent image acquisition system: sequentially irradiating a plurality of samples to be tested through an ultraviolet light source, and collecting image data of a channel with the most obvious fluorescence characteristic by using a multi-channel multi-spectrum camera; building a pesticide residue detection model: and (3) after processing the acquired images, constructing a pesticide residue detection model, comparing the pesticide residue detection model to find an optimal model, and deploying the optimal model to a control system to realize detection of the pesticide residues of fruits and vegetables through image recognition. In addition, the invention also provides a system for detecting the pesticide residues of fruits and vegetables based on the multispectral imaging technology. The method can effectively solve the problem that the sample is damaged in pesticide residue detection, reduce the detection cost and improve the pesticide residue detection accuracy.

Description

A method and system for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology

technical field

The invention relates to the field of smart agriculture, in particular to a method and system for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology.

Background technique

In order to maintain the health of the whole people and improve people's quality of life, food safety has always been an important part of people's attention. At present, there are three methods for the detection of pesticide residues in fruits and vegetables. The first method is to use pesticide residue test paper for detection, which takes a long time, has low accuracy, and has complicated steps; the second method is to detect pesticide residues with chemical reagents, such as chromatography, which is destructive to samples. , and the steps are complicated; the third method uses hyperspectral imaging equipment to detect pesticide residues, which is expensive and takes a long time to detect. Therefore, it is necessary to design a method for detecting pesticide residues in fruits and vegetables, which can effectively solve the problem that pesticide residue detection will destroy samples, reduce detection costs, and improve the accuracy of pesticide residue detection.

Contents of the invention

The purpose of the present invention is to provide a method for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology, which can effectively solve the situation that pesticide residue detection will destroy samples, reduce detection costs, and improve the accuracy of pesticide residue detection.

Another object of the present invention is to provide a system for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology, which can effectively solve the problem that pesticide residue detection will damage samples, reduce detection costs, and improve the accuracy of pesticide residue detection.

Embodiments of the present invention are achieved like this:

In the first aspect, the embodiment of the present application provides a method for detecting pesticide residues in fruits and vegetables based on multi-spectral imaging technology, which includes the following steps to build a multi-spectral fluorescence image acquisition system: sequentially irradiate multiple samples to be tested by ultraviolet light sources, and use multi-channel The multi-spectral camera collects the image data of the channel with the most obvious fluorescence characteristics; builds the pesticide residue detection model: builds the pesticide residue detection model after processing the collected images, finds the optimal model through the comparison of the above pesticide residue detection models, and deploys the optimal model to the control panel. The system realizes the detection of pesticide residues in fruits and vegetables through image recognition.

In some embodiments of the present invention, the above-mentioned multi-channel multi-spectral camera is a five-channel multi-spectral camera.

In some embodiments of the present invention, the above-mentioned method for detecting pesticide residues in fruits and vegetables based on multi-spectral imaging technology further includes the following steps, sequentially transporting a plurality of samples to be tested to designated locations, and then irradiating them with the above-mentioned ultraviolet light source.

In some embodiments of the present invention, a plurality of samples to be tested are sequentially delivered to the above-mentioned designated positions by using a conveyor belt.

In some embodiments of the present invention, processing the collected images includes preprocessing, constructing a data set, and data normalization processing.

In some embodiments of the present invention, the above-mentioned data set is constructed according to the collected image data and pesticide detection data of each of the above-mentioned tested samples.

In the second aspect, the embodiment of the present application provides a system for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology, which includes an image acquisition system building module: multiple samples to be tested are sequentially irradiated by an ultraviolet light source, and collected by a multi-channel multi-spectral camera Image data of the channel with the most obvious fluorescence characteristics; Pesticide residue detection model building module: build a pesticide residue detection model after processing the collected images, find the optimal model through the comparison of the above pesticide residue detection models, and deploy the optimal model to the control system to achieve Image recognition detection of pesticide residues in fruits and vegetables.

Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

In the first aspect, the embodiment of the present application provides a method for detecting pesticide residues in fruits and vegetables based on multi-spectral imaging technology, which includes the following steps to build a multi-spectral fluorescence image acquisition system: sequentially irradiate multiple samples to be tested by ultraviolet light sources, and use multi-channel The multi-spectral camera collects the image data of the channel with the most obvious fluorescence characteristics; builds the pesticide residue detection model: builds the pesticide residue detection model after processing the collected images, finds the optimal model through the comparison of the above pesticide residue detection models, and deploys the optimal model to the control panel. The system realizes the detection of pesticide residues in fruits and vegetables through image recognition.

In the second aspect, the embodiment of the present application provides a system for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology, which includes an image acquisition system building module: multiple samples to be tested are sequentially irradiated by an ultraviolet light source, and collected by a multi-channel multi-spectral camera Image data of the channel with the most obvious fluorescence characteristics; Pesticide residue detection model building module: build a pesticide residue detection model after processing the collected images, find the optimal model through the comparison of the above pesticide residue detection models, and deploy the optimal model to the control system to achieve pass Image recognition detection of pesticide residues in fruits and vegetables.

For the first aspect to the second aspect: this application builds a multi-spectral fluorescence image acquisition system, irradiates multiple samples to be tested by ultraviolet light sources in sequence, and collects images with a multi-channel multi-spectral camera to obtain image data of the channel with the most obvious fluorescence characteristics , improve the accuracy of feature recognition of collected images; build a pesticide residue detection model by building a pesticide residue detection model, process the collected images and build a pesticide residue detection model, so as to use the pesticide recognition results of different detection images to model through deep learning, through the above pesticide residue detection model The optimal model is found by comparison, the optimal model is deployed to the control system, and the detection results of pesticide residues in fruits and vegetables are obtained through image recognition through multi-spectral imaging non-destructive testing technology. The invention effectively solves the problem of pesticide residue detection destroying samples, and reduces the cost of non-destructive detection of pesticide residues; uses machine vision technology and deep learning algorithms to study automatic identification of pesticide residues, establishes relevant detection models, and improves pesticide residue detection. Accuracy of residue detection.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and thus It should be regarded as a limitation on the scope, and those skilled in the art can also obtain other related drawings based on these drawings without creative work.

Fig. 1 is the flowchart of the method for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology in embodiment 1 of the present invention;

2 is a schematic diagram of a system for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology in Example 2 of the present invention;

FIG. 3 is a schematic diagram of the principle of an electronic device according to Embodiment 3 of the present invention.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of this application, not all of them. The components of the embodiments of the application generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations.

Accordingly, the following detailed description of the embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application.

In the description of this application, it should also be noted that, unless otherwise clearly stipulated and limited, the terms "setting" and "connection" should be understood in a broad sense, for example, it can be a fixed connection or a detachable connection, or Integral connection; it can be mechanical connection or electrical connection; it can be direct connection or indirect connection through an intermediary, and it can be the internal communication of two components. Those of ordinary skill in the art can understand the specific meanings of the above terms in this application in specific situations.

Some implementations of the present application will be described in detail below in conjunction with the accompanying drawings. In the case of no conflict, each of the following embodiments and each feature in the embodiments can be combined with each other.

Example 1

Please refer to FIG. 1 . FIG. 1 is a schematic diagram of a method for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology provided in an embodiment of the present application. The method for detecting pesticide residues in fruits and vegetables based on multi-spectral imaging technology includes the following steps to build a multi-spectral fluorescence image acquisition system: multiple samples to be tested are irradiated by an ultraviolet light source in sequence, and a multi-channel multi-spectral camera is used to collect images of the channels with the most obvious fluorescence characteristics Data; Build a pesticide residue detection model: After processing the collected images, build a pesticide residue detection model, find the optimal model through the comparison of the above pesticide residue detection models, and deploy the optimal model to the control system to detect pesticide residues in fruits and vegetables through image recognition.

In this application, by building a multi-spectral fluorescence image acquisition system, multiple samples to be tested are irradiated by ultraviolet light sources in sequence, and images are collected with a multi-channel multi-spectral camera to obtain image data of the channel with the most obvious fluorescence characteristics, so as to improve the accuracy of feature recognition of collected images ;By building a pesticide residue detection model, after processing the collected images, a pesticide residue detection model is built, so that the pesticide recognition results of different detection images can be used for modeling through deep learning, and the optimal model can be found by comparing the above pesticide residue detection models. The model is deployed to the control system, and the detection results of pesticide residues in fruits and vegetables are obtained through image recognition through multi-spectral imaging non-destructive testing technology. The invention effectively solves the problem of pesticide residue detection destroying samples, and reduces the cost of non-destructive detection of pesticide residues; uses machine vision technology and deep learning algorithms to study automatic identification of pesticide residues, establishes relevant detection models, and improves pesticide residue detection. Accuracy of residue detection.

In some embodiments of the present invention, the above-mentioned multi-channel multi-spectral camera is a five-channel multi-spectral camera.

In some embodiments of the present invention, the above-mentioned method for detecting pesticide residues in fruits and vegetables based on multi-spectral imaging technology further includes the following steps, sequentially transporting a plurality of samples to be tested to designated locations, and then irradiating them with the above-mentioned ultraviolet light source. The image acquisition conditions at a specific position are collected, and for the same ultraviolet irradiation conditions, the channel with the most obvious fluorescence effects is obtained, so as to obtain the detection sample image with consistent detection standards, which improves the detection accuracy of the model.

In some embodiments of the present invention, a plurality of samples to be tested are sequentially delivered to the above-mentioned designated positions by using a conveyor belt. It is transported to a designated location by a conveyor belt, a robotic arm, or other transport mechanisms, so that images can be collected using specific ultraviolet radiation and camera shooting conditions, which facilitates the collection of large amounts of data.

In some embodiments of the present invention, processing the collected images includes preprocessing, constructing a data set, and data normalization processing. The collected image information is preprocessed to make the detection object more complete and accurate, and the data set of each tested sample is constructed, and after normalization processing for different data, the detection result is more accurate.

In some embodiments of the present invention, the above-mentioned data set is constructed according to the collected image data and pesticide detection data of each of the above-mentioned tested samples. The pesticide detection data corresponding to the collected image data of the tested sample is the existing record data, which can be obtained by any detection method. By constructing a data set and using multiple data to normalize the corresponding results, a pesticide residue detection model was constructed. The pesticide detection data can be any number of detection data such as various components and indicators, and is not specifically limited here.

Example 2

Please refer to FIG. 2 . FIG. 2 is a schematic diagram of a system for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology provided in an embodiment of the present application. A system for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology, which includes an image acquisition system building module: multiple samples to be tested are irradiated by an ultraviolet light source in sequence, and a multi-channel multispectral camera is used to collect image data of the channel with the most obvious fluorescence characteristics; Detection model building module: After processing the collected images, a pesticide residue detection model is built, the optimal model is found by comparing the above pesticide residue detection models, and the optimal model is deployed to the control system to detect pesticide residues in fruits and vegetables through image recognition.

The principle of this embodiment of the present application is the same as that of Embodiment 1, and will not be repeated here. It can be understood that the structure shown in Figure 2 is only for illustration, and the system for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology may also include more or fewer components than those shown in Figure 2, or have a configuration different from that shown in Figure 2 . Each component shown in Fig. 2 may be implemented by hardware, software or a combination thereof.

Example 3

Please refer to FIG. 3 . FIG. 3 is a schematic structural block diagram of an electronic device provided in an embodiment of the present application. The electronic device includes a memory 101, a processor 102, and a communication interface 103. The memory 101, the processor 102, and the communication interface 103 are electrically connected to each other directly or indirectly, so as to realize data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules, such as the program instructions/modules corresponding to the system for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology provided in Embodiment 2 of the present application. The processor 102 executes the software programs and modules stored in the memory 101. modules to perform various functional applications as well as data processing. The communication interface 103 can be used for signaling or data communication with other node devices.

Wherein, memory 101 can be but not limited to, random access memory (Random Access Memory, RAM), read-only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-OnlyMemory, PROM), erasable Read-only memory (Erasable Programmable Read-Only Memory, EPROM), Electric Erasable Programmable Read-Only Memory (EEPROM), etc.

The processor 102 may be an integrated circuit chip with signal processing capability. The processor 102 can be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (NetworkProcessor, NP), etc.; it can also be a digital signal processor (Digital Signal Processing, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

In the embodiments provided in this application, it should be understood that the disclosed devices and methods may also be implemented in other ways. The device embodiments described above are only illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functions and possible implementations of devices, methods and computer program products according to multiple embodiments of the present application. operate. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or part of code that includes one or more programmable components for implementing specified logical functions. Execute instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified function or action , or may be implemented by a combination of dedicated hardware and computer instructions.

In addition, each functional module in each embodiment of the present application may be integrated to form an independent part, each module may exist independently, or two or more modules may be integrated to form an independent part.

If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes. .

To sum up, a method and system for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology provided by the embodiment of the present application:

In this application, by building a multi-spectral fluorescence image acquisition system, multiple samples to be tested are irradiated by ultraviolet light sources in sequence, and images are collected with a multi-channel multi-spectral camera to obtain image data of the channel with the most obvious fluorescence characteristics, so as to improve the accuracy of feature recognition of collected images ; By building a pesticide residue detection model, after processing the collected images, a pesticide residue detection model is built, so that the pesticide recognition results of different detection images are used to model through deep learning, and the optimal model is found by comparing the pesticide residue detection models. The optimal model is deployed to the control system, and the detection results of pesticide residues in fruits and vegetables can be obtained through image recognition through multi-spectral imaging non-destructive testing technology. The invention effectively solves the problem of pesticide residue detection destroying samples, and reduces the cost of non-destructive detection of pesticide residues; uses machine vision technology and deep learning algorithms to study automatic identification of pesticide residues, establishes relevant detection models, and improves pesticide residue detection. Accuracy of residue detection.

The above descriptions are only preferred embodiments of the present application, and are not intended to limit the present application. For those skilled in the art, there may be various modifications and changes in the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this application shall be included within the protection scope of this application.

It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, but that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be regarded as exemplary and not restrictive in all points of view, and the scope of the application is defined by the appended claims rather than the foregoing description, and it is intended that the scope of the present application be defined by the appended claims rather than by the foregoing description. All changes within the meaning and range of equivalents of the elements are embraced in this application. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (7)

1. A method for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology, is characterized in that, comprises the steps, Build a multi-spectral fluorescence image acquisition system: multiple measured samples are irradiated by an ultraviolet light source in sequence, and the image data of the channel with the most obvious fluorescence characteristics is collected by a multi-channel multi-spectral camera; Build a pesticide residue detection model: build a pesticide residue detection model after processing the collected images, find the optimal model by comparing the pesticide residue detection models, and deploy the optimal model to the control system to detect pesticide residues in fruits and vegetables through image recognition. 2. a kind of method based on multi-spectral imaging technology detection fruit and vegetable pesticide residue as claimed in claim 1, is characterized in that, described multi-channel multi-spectral camera is a five-channel multi-spectral camera. 3. A method for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology as claimed in claim 1, further comprising the step of sequentially transporting a plurality of samples to be tested to a designated location and then irradiating them with the ultraviolet light source. 4. A method for detecting pesticide residues in fruits and vegetables based on multi-spectral imaging technology as claimed in claim 3, characterized in that a plurality of samples to be tested are sequentially transported to the designated position using a conveyor belt. 5. A method for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology as claimed in claim 1, wherein processing the collected images includes preprocessing, building data sets and data normalization. 6. A method for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology as claimed in claim 5, wherein the data set is constructed according to the collected image data and pesticide detection data of each of the tested samples. 7. A system for detecting pesticide residues in fruits and vegetables based on multispectral imaging technology, characterized in that, comprising, Image acquisition system building module: multiple samples to be tested are irradiated by ultraviolet light source in sequence, and the image data of the channel with the most obvious fluorescence characteristics is collected by a multi-channel multi-spectral camera; Pesticide residue detection model building module: build a pesticide residue detection model after processing the collected images, find the optimal model by comparing the pesticide residue detection models, and deploy the optimal model to the control system to detect pesticide residues in fruits and vegetables through image recognition.

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