Spectral detection method for detecting lycopene content in agricultural products

Abstract

The present application discloses a spectral detection method suitable for detecting the lycopene content in agricultural products. The method is implemented by a spectrometer, and the spectrometer includes: a light source device, a converging device, a chopping device, a filtering device, a detection device and a processing device; the spectral detection method includes: exciting the light source device to generate light source light; the converging device converges the light source light into converged light; controlling and driving the chopping device to obtain detection light with a modulation frequency for irradiating the agricultural products; arranging a filtering device on the optical path of the detection light to obtain characteristic light; controlling the detection device to convert the optical signal of the characteristic light into an electrical signal; and controlling the processing device to output the analysis result of the substance content according to the electrical signal output by the detection device. The present application is beneficial in that: it provides a spectral detection method suitable for detecting the lycopene content in agricultural products, which can effectively detect the lycopene content in agricultural products.

Description

Technical Field

[0001] The present application relates to the technical field of non-destructive testing of agricultural products, and in particular to a spectral detection method suitable for detecting the lycopene content in agricultural products. Background Art

[0002] The quality grading of agricultural products, especially fruits, has become an additional condition for their cross-border trade and an important means to increase the added value of agricultural products. The quality of agricultural products mainly includes external quality (size, shape, color, surface defects, etc.) and internal quality (sugar content, acidity, maturity, etc.).

[0003] Infrared spectroscopy is a powerful tool for determining molecular composition and structure. The near-infrared region mainly contains absorption bands produced by the frequency doubling and combination absorption of stretching vibrations of hydrogen-containing groups (such as OH, NH, CH). Thanks to the development of near-infrared spectroscopy technology, non-destructive testing of the internal quality of agricultural products has become possible. Now, it is possible to perform internal quality testing of some high-efficiency agricultural products, such as testing the internal sugar content and acidity of fruits, and use this as the basis for internal quality grading to improve its economic benefits.

[0004] Lycopene is a kind of pigment widely found in tomatoes, tomato products, watermelon, grapefruit and other fruits. It is the main pigment in ripe tomatoes and one of the common carotenoids. Studies have shown that lycopene can effectively reduce the risk of various tumors such as prostate cancer and cardiovascular diseases. Therefore, it is of broad significance to grade the lycopene content in agricultural products containing lycopene. However, since its content in agricultural products is much lower than that of other ingredients, some destructive detection methods are currently used, such as spectrophotometry, thin layer chromatography, high performance liquid chromatography, etc. These methods obviously do not meet the needs of rapid classification of agricultural products. However, the spectral data obtained by traditional near-infrared spectrometers contain less optical information related to the lycopene content, so it is still difficult to achieve rapid and non-destructive detection of lycopene content in agricultural products. Summary of the invention

[0005] The content of this application is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this application is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0006] Some embodiments of the present application propose a spectral detection method suitable for detecting the lycopene content in agricultural products to solve the technical problems mentioned in the above background technology section.

[0007] As a first aspect of the present application, some embodiments of the present application provide a spectral detection method suitable for detecting the lycopene content in agricultural products, the method is implemented by a spectrometer, the spectrometer includes: a light source device, a converging device, a chopping device, a filtering device, a detection device and a processing device; the spectral detection method includes: exciting the light source device to generate light source light; arranging a converging device on the optical path of the light source light to converge the light source light into converged light; controlling and driving the chopping device to adjust the frequency of the converged light to obtain a detection light with a modulation frequency for irradiating the agricultural product; arranging a filtering device on the optical path of the detection light to shield the light of other wavelengths except the preset wavelength in the detection light after passing through the agricultural product to obtain a characteristic light; controlling the detection device to receive the characteristic light and convert the optical signal of the characteristic light into an electrical signal;

[0008] The control processing device generates spectrum analysis data according to the electrical signal output by the detection device and outputs the analysis result of the substance content according to the spectrum analysis data; wherein the preset wavelength range of the filter device is 400 nm to 5000 nm.

[0009] Furthermore, the preset wavelength range of the filter device at least includes 900 nm to 1200 nm.

[0010] Furthermore, the preset wavelength range of the filter device at least includes 1300 nm to 1500 nm.

[0011] Furthermore, the preset wavelength range of the filter device at least includes 1600 nm to 1800 nm.

[0012] Furthermore, the preset wavelength range of the filter device includes 2200 nm to 2400 nm.

[0013] Furthermore, the preset wavelength of the filter device includes at least one or more of 900nm, 1180nm, 1400nm, 1720nm and 2350nm.

[0014] Further, the spectrometer further comprises: a phase-locking device;

[0015] Spectral detection methods also include:

[0016] The phase-locking device is controlled to improve the signal-to-noise ratio of the electrical signal output by the detection device to the processing device.

[0017] Furthermore, the spectrum detection method also includes:

[0018] controlling the chopper device to send a frequency signal of the modulation frequency to the phase-locked device;

[0019] The phase-locked device demodulates the electrical signal output by the detection device according to the frequency signal of the modulation frequency.

[0020] Furthermore, the light source device includes at least one halogen lamp to generate near-infrared light as the light source; and the detector includes at least one near-infrared light detector.

[0021] Furthermore, the near-infrared light detector includes at least one of a PbS detector array or an InGaAs detector array for detecting light signals of a preset wavelength.

[0022] As a second aspect of the present application, some embodiments of the present application provide a spectral detection method suitable for detecting the lycopene content in agricultural products, the method is implemented by a plurality of spectrometers and a server, the spectrometer comprising: a light source device, a converging device, a chopping device, a filtering device, a detection device and a processing device; the spectral detection method comprises the steps performed by the spectrometer: exciting the light source device to generate light source light; arranging a converging device on the optical path of the light source light to converge the light source light into converged light; controlling and driving the chopping device to adjust the frequency of the converged light to obtain a detection light with a modulation frequency for irradiating the agricultural product; arranging a filtering device on the optical path of the detection light to shield the light of other wavelengths except the preset wavelength in the detection light after passing through the agricultural product to obtain a characteristic light; controlling the detection device to receive the characteristic light and convert the optical signal of the characteristic light into an electrical signal; controlling the processing device to generate spectral analysis data according to the electrical signal output by the detection device and output the analysis result of the substance content according to the spectral analysis data; wherein the preset wavelength of the filtering device ranges from 400 nm to 5000 nm;

[0023] The spectral detection method includes the steps executed by a server: in response to the analysis result output by a spectrometer processing device, querying the modulation frequency and analysis result in the historical data of the spectrometer; selecting the modulation frequency and analysis result corresponding to the inverse N groups in the historical data and inputting them into a modulation frequency analysis model so that the modulation frequency analysis model outputs a modulation frequency prediction value and a corresponding confidence level; judging whether the confidence level is greater than or equal to a preset confidence threshold, if so, accepting the modulation frequency prediction value and feeding it back to the spectrometer; if not, not accepting the modulation frequency prediction value; wherein the modulation frequency analysis model is trained using the modulation frequencies and analysis results in the historical data of multiple spectrometers as training data.

[0024] As a third aspect of the present application, some embodiments of the present application provide a spectral detection device suitable for detecting the lycopene content in agricultural products, including: a query module, used to query the modulation frequency and analysis results in the historical data of a spectrometer in response to the analysis results output by a spectrometer processing device; an output module, used to select the modulation frequencies and analysis results corresponding to the inverse N groups in the historical data and input them into a modulation frequency analysis model so that the modulation frequency analysis model outputs a modulation frequency prediction value and a corresponding confidence level; a judgment module, used to judge whether the confidence level is greater than or equal to a preset confidence threshold, if so, the modulation frequency prediction value is accepted and fed back to the spectrometer; if not, the modulation frequency prediction value is not accepted; wherein the modulation frequency analysis model is trained using the modulation frequencies and analysis results in the historical data of multiple spectrometers as training data.

[0025] As the fourth aspect of the present application, some embodiments of the present application provide an electronic device, comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0026] As the fifth aspect of the present application, some embodiments of the present application provide a computer-readable medium on which a computer program is stored, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0027] The beneficial effect of the present application is that it provides a spectral detection method suitable for detecting the lycopene content in agricultural products, which can effectively detect the lycopene content in agricultural products. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The illustrative embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application.

[0029] In addition, throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the components and elements are not necessarily drawn to scale.

[0030] In the attached picture:

[0031] Figure 1 is a structural block diagram of a spectrometer according to an embodiment of the present application;

[0032] Figure 2 is a flowchart of a spectrum detection method according to an embodiment of the present application;

[0033] Figure 3 is a schematic diagram of the architecture of a spectrum detection system according to an embodiment of the present application;

[0034] Figure 4 is a schematic diagram of the architecture of a spectrum detection device according to an embodiment of the present application;

[0035] Figure 5 It is a block diagram of steps of a part of a spectrum detection method according to another embodiment of the present application;

[0036] Figure 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application;

[0037] The meanings of the reference numerals are:

[0038] 100. Spectrometer;

[0039] 101. light source device; 102. focusing device;

[0040] 103. Chopper device; 1031. Optical chopper; 1032. Chopper driver; 1033. Chopper controller;

[0041] 104. Filter device;

[0042] 105, detection device; 1051, near-infrared light detector; 105a, PbS detector array; 105b, InGaAs detector array;

[0043] 106. Processing device;

[0044] 107. Phase-locked device; 1071. Phase-locked amplifier;

[0045] 108. Signal amplifier;

[0046] 200. Agricultural products to be inspected;

[0047] 300. Server. DETAILED DESCRIPTION

[0048] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0049] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0050] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0051] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0052] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0053] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0054] like Figure 1 As shown, a spectrometer 100 according to an embodiment of the present application includes: a light source device 101 , a focusing device 102 , a chopping device 103 , a filtering device 104 , a detection device 105 and a processing device 106 .

[0055] Among them, the light source device 101 is used to generate the light required for spectral detection; the converging device 102 is used to converge the light generated by the light source device 101; the chopping device 103 is used to modulate the frequency of the light converged by the lens device; the filter device 104 is used to shield the light of other wavelengths except the preset wavelength; the detection device 105 is used to receive the light passing through the agricultural product 200 to be inspected and the filter device 104 and convert the optical signal into an electrical signal; the processing device 106 is used to generate spectral analysis data according to the electrical signal output by the detection device 105 and output the analysis result of the substance content according to the spectral analysis data.

[0056] By adopting the above scheme, since the content of trace substances or nutrients in agricultural products is too low, the spectrometer 100 of the present application greatly reduces the useless spectral information in the original spectrum by artificially selecting characteristic wavelengths of some trace substances or nutrients (such as characteristic wavelengths related to the chemical composition of lycopene) in the near-infrared light band, which is of guiding significance for the detection of trace elements in agricultural products.

[0057] Specifically, the preset wavelength of the filter device ranges from 400 nm to 5000 nm; according to the trace substances or nutrients to be detected, the preset wavelength of the filter device is set accordingly to filter out light of non-characteristic wavelengths.

[0058] More specifically, the preset wavelengths of the filter device 104 include at least one or more of 900 nm, 1180 nm, 1400 nm, 1720 nm and 2350 nm, and these preset wavelengths are used for the detection of lycopene.

[0059] As a preferred solution, the optical filtering device comprises one or more optical filters.

[0060] Specifically, the light generated by the light source device 101 includes infrared light, and its wavelength range covers the characteristic wavelength required to detect trace substances or nutrients. More specifically, the light source device 101 includes a halogen lamp.

[0061] More specifically, for the detection of lycopene, the wavelength of the light generated by the light source device 101 ranges from 400 nm to 5000 nm.

[0062] As a specific solution, the converging device 102 includes at least one convex lens. The convex lens is preferably a plano-convex lens, with its non-convex surface facing the light source.

[0063] As a specific solution, the chopper device 103 includes: an optical chopper 1031, a chopper driver 1032 and a chopper controller 1033; the optical chopper 1031 includes a rotating blade with an adjustable rotation frequency, and the rotating blade periodically blocks the light converged by the lens device; the chopper driver 1032 is used to drive the rotating blade to rotate; the chopper controller 1033 is used to control the operation of the chopper driver 1032 to control the rotation frequency of the rotating blade, the chopper driver 1032 and the rotating blade of the optical chopper 1031 are mechanically connected, and the chopper driver 1032 and the chopper controller 1033 are electrically connected.

[0064] With this solution, the chopper controller 1033 outputs a modulated frequency electrical signal, thereby controlling the modulation frequency of the optical chopper 1031, and modulating the light focused by the lens device into a high-frequency optical signal at a set frequency.

[0065] As a specific solution, the detection device 105 includes a near-infrared light detector 1051, and the near-infrared light detector 1051 includes at least one of a PbS detector array 105a or an InGaAs detector array 105b to achieve detection of a preset wavelength.

[0066] As a preferred solution, the spectrometer 100 of an embodiment of the present application further includes: a phase-locked device 107, which is used to improve the signal-to-noise ratio of the electrical signal output by the detection device 105 to the processing device 106. Specifically, the phase-locked device 107 includes a phase-locked amplifier 1071; the phase-locked amplifier 1071 is electrically connected to the detection device 105, the chopper controller 1033 and the processing device 106. Among them, the output signal interface of the detection device 105 is connected to the input signal interface of the phase-locked amplifier 1071, the frequency signal output interface of the chopper controller 1033 is connected to the reference signal interface of the phase-locked amplifier 1071, and the phase-locked amplifier 1071 separates a specific carrier frequency signal according to the frequency of the reference signal.

[0067] As a preferred solution, the spectrometer 100 of an embodiment of the present application further includes: a signal amplifier 108; the signal amplifier 108 is a metamaterial, and a series of micro-nano structure arrays are carved on its surface, which can amplify optical signals of specific wavelengths; specifically, in the present application, the signal amplifier 108 amplifies the preset wavelength of the filter device 104, and the amplified optical signal is received by the detection device 105. In some technical solutions, the signal amplifier 108 can be omitted.

[0068] like Figure 2 As shown, as a preferred solution, a spectrum detection method of an embodiment of the present application is implemented by a spectrometer, and the spectrometer includes: a light source device, a focusing device, a chopping device, a filtering device, a detection device and a processing device; the spectrum detection method includes the following main steps:

[0069] S1: Excite the light source device 101 to generate light source light.

[0070] S2: A converging device 102 is arranged on the optical path of the light source light to converge the light source light into converged light.

[0071] S3: Control and drive the chopper device 103 to adjust the frequency of the combined light to obtain the detection light with a modulated frequency for irradiating the agricultural products.

[0072] S4: A filter device is arranged on the optical path of the detection light to shield the light of other wavelengths except the preset wavelength in the detection light after passing through the agricultural product and obtain the characteristic light.

[0073] S5: Control the detection device 105 to receive the characteristic light and convert the optical signal of the characteristic light into an electrical signal.

[0074] S6: The control processing device 106 generates spectrum analysis data according to the electrical signal output by the detection device 105 and outputs the analysis result of the substance content according to the spectrum analysis data.

[0075] Specifically, for trace substances or nutrients in agricultural products, the preset wavelength range of the filter device 104 is 400 nm to 5000 nm.

[0076] More specifically, for the detection of lycopene in agricultural products, the preset wavelength of the filter device is defined as follows:

[0077] As a preferred solution, the preset wavelength range of the filter device at least includes 900 nm to 1200 nm.

[0078] As a preferred solution, the preset wavelength range of the filter device at least includes 1300 nm to 1500 nm.

[0079] As a preferred solution, the preset wavelength range of the filter device at least includes 1600 nm to 1800 nm.

[0080] As a preferred solution, the preset wavelength range of the filter device includes 2200 nm to 2400 nm.

[0081] More preferably, the preset wavelength of the filter device includes at least one or more of 900 nm, 1180 nm, 1400 nm, 1720 nm and 2350 nm.

[0082] like Figure 3 As shown, as a preferred solution, a spectrum detection method according to another embodiment of the present application is implemented by a plurality of spectrometers 100 and a server 300 .

[0083] like Figure 2 As shown, the spectral detection method includes the steps performed by the spectrometer:

[0084] S1: Excite the light source device 101 to generate light source light.

[0085] S2: A converging device 102 is arranged on the optical path of the light source light to converge the light source light into converged light.

[0086] S3: Control and drive the chopper device 103 to adjust the frequency of the combined light to obtain the detection light with a modulated frequency for irradiating the agricultural products.

[0087] S4: A filter device is arranged on the optical path of the detection light to shield the light of other wavelengths except the preset wavelength in the detection light after passing through the agricultural product and obtain the characteristic light.

[0088] S5: Control the detection device 105 to receive the characteristic light and convert the optical signal of the characteristic light into an electrical signal.

[0089] S6: The control processing device 106 generates spectrum analysis data according to the electrical signal output by the detection device 105 and outputs the analysis result of the substance content according to the spectrum analysis data.

[0090] The preset wavelength of the filter device ranges from 400 nm to 5000 nm.

[0091] like Figure 4 As shown, the spectrum detection method includes the steps performed by the server 300:

[0092] St1: In response to the analysis result output by a spectrometer processing device, query the modulation frequency and analysis result in the historical data of the spectrometer.

[0093] St2: Select the modulation frequencies and analysis results corresponding to the last N groups in the historical data and input them into a modulation frequency analysis model so that the modulation frequency analysis model outputs the modulation frequency prediction value and the corresponding confidence level.

[0094] St3: Determine whether the confidence is greater than or equal to a preset confidence threshold. If so, the modulation frequency prediction value is accepted and fed back to the spectrometer; if not, the modulation frequency prediction value is not accepted.

[0095] Specifically, the modulation frequency analysis model is trained using the modulation frequencies and analysis results in historical data of multiple spectrometers as training data.

[0096] like Figure 5 As shown, the spectral detection device of the present application includes: a query module, which is used to query the modulation frequency and analysis result in the historical data of the spectrometer in response to the analysis result output by a spectrometer processing device; an output module, which is used to select the modulation frequency and analysis result corresponding to the inverse N groups in the historical data and input them into a modulation frequency analysis model so that the modulation frequency analysis model outputs a modulation frequency prediction value and a corresponding confidence level; a judgment module, which is used to judge whether the confidence level is greater than or equal to a preset confidence threshold, if so, the modulation frequency prediction value is adopted and fed back to the spectrometer; if not, the modulation frequency prediction value is not adopted; wherein the modulation frequency analysis model is trained using the modulation frequencies and analysis results in the historical data of multiple spectrometers as training data.

[0097] As a preferred solution, the value of N is a positive integer and can be considered as set and adjustable.

[0098] The advantage of adopting the above solution is that when it is applied to a fruit production line, the spectral frequency band can be quickly selected and adjusted according to the different trace substances or nutrients that need to be detected in a batch of fruits. In addition, the model based on system training can reduce training costs while improving prediction accuracy.

[0099] As a preferred solution, the modulation frequency analysis model is a convolutional neural network model, and its input data is a number of matrices converted from a number of standard-sized spectrograms and the corresponding modulation frequencies, which are trained to achieve the prediction function. The specific model architecture and training method are well known to those skilled in the art and will not be elaborated on again.

[0100] like Figure 6 As shown, the electronic device 800 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage device 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 are also stored. The processing device 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0101] Typically, the following devices may be connected to the I / O interface 805: input devices 806 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 807 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 808 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 809. The communication device 809 may allow the electronic device 800 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 800 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 6 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0102] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 809, or installed from the storage device 808, or installed from the ROM 802. When the computer program is executed by the processing device 801, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0103] It should be noted that the computer-readable medium described above in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0104] In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device, or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0105] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperTextTransferProtocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an adhoc peer-to-peer network), as well as any currently known or future developed network.

[0106] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: in response to the analysis result output by a spectrometer processing device, queries the modulation frequency and analysis result in the historical data of the spectrometer; selects the modulation frequency and analysis result corresponding to the inverse N groups in the historical data and inputs them into a modulation frequency analysis model so that the modulation frequency analysis model outputs the modulation frequency prediction value and the corresponding confidence level; determines whether the confidence level is greater than or equal to a preset confidence threshold, and if so, the modulation frequency prediction value is adopted and fed back to the spectrometer; if not, the modulation frequency prediction value is not adopted; wherein the modulation frequency analysis model is trained using the modulation frequencies and analysis results in the historical data of multiple spectrometers as training data.

[0107] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0108] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, which contains one or more executable instructions for implementing a specified logical function.

[0109] 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.

[0110] For example, two boxes shown in succession may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of boxes in the block diagram and / or flow chart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0111] The units described in some embodiments of the present disclosure may be implemented by software or hardware. The units described may also be arranged in a processor, and the names of these units do not constitute limitations on the units themselves in some cases.

[0112] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0113] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A spectral detection method for detecting the lycopene content in agricultural products, the method being implemented by a plurality of spectrometers and a server, the spectrometers include: Light source device, focusing device, chopping device, filtering device, detection device and processing device; Features: The spectrum detection method comprises the steps performed by the spectrometer: Exciting the light source device to generate light source light; The converging device is arranged on the optical path of the light source light to converge the light source light into converged light; Controlling and driving the chopper device to adjust the frequency of the converged light to obtain detection light with a modulated frequency for irradiating agricultural products; The filter device is arranged on the optical path of the detection light to shield the light of other wavelengths except the preset wavelength in the detection light after passing through the agricultural product to obtain the characteristic light; Controlling the detection device to receive the characteristic light and converting the optical signal of the characteristic light into an electrical signal; Controlling the processing device to generate spectrum analysis data according to the electrical signal output by the detection device and outputting the analysis result of the substance content according to the spectrum analysis data; Wherein, the preset wavelength range of the filter device is 400 nm to 5000 nm; The spectrum detection method comprises the steps performed by the server: In response to an analysis result output by a spectrometer processing device, querying the modulation frequency and analysis result in historical data of the spectrometer; Selecting modulation frequencies and analysis results corresponding to the last N groups in historical data and inputting them into a modulation frequency analysis model so that the modulation frequency analysis model outputs a modulation frequency prediction value and a corresponding confidence level; Determine whether the confidence is greater than or equal to a preset confidence threshold, if yes, accept the modulation frequency prediction value and feed it back to the spectrometer; if no, do not accept the modulation frequency prediction value; The modulation frequency analysis model is trained using modulation frequencies and analysis results in historical data of a plurality of the spectrometers as training data.

2. The spectral detection method for detecting the lycopene content in agricultural products according to claim 1, characterized in that: in, The preset wavelength value range of the filter device at least includes 900 nm to 1200 nm.

3. The spectral detection method for detecting the lycopene content in agricultural products according to claim 1, characterized in that: in, The preset wavelength value range of the filter device at least includes 1300 nm to 1500 nm.

4. The spectral detection method for detecting the lycopene content in agricultural products according to claim 1, characterized in that: in, The preset wavelength range of the filter device at least includes 1600 nm to 1800 nm.

5. The spectral detection method for detecting the lycopene content in agricultural products according to claim 1, characterized in that: in, The preset wavelength value range of the filter device includes 2200 nm to 2400 nm.

6. The spectral detection method for detecting the lycopene content in agricultural products according to any one of claims 1 to 5, characterized in that: in, The preset wavelength of the filter device includes at least one or more of 900nm, 1180nm, 1400nm, 1720nm and 2350nm.

7. The spectral detection method for detecting the lycopene content in agricultural products according to claim 1 ， Features: Wherein, the spectrometer further comprises: a phase-locking device; The spectrum detection method further comprises: The phase locking device is controlled to improve the signal-to-noise ratio of the electrical signal output by the detection device to the processing device.

8. The spectral detection method for detecting the lycopene content in agricultural products according to claim 7, Features: The spectrum detection method further comprises: Controlling the chopping device to send the frequency signal of the modulation frequency to the phase locking device; The phase-locked device demodulates the electrical signal output by the detection device according to the frequency signal of the modulation frequency.

9. The spectral detection method for detecting the lycopene content in agricultural products according to claim 1, Features: The light source device comprises at least one halogen lamp to generate near-infrared light as the light source light; the detection device comprises at least one near-infrared light detector.

10. The spectral detection method for detecting the lycopene content in agricultural products according to claim 9, Features: The near-infrared light detector includes at least one of a PbS detector array or an InGaAs detector array for detecting light signals of a preset wavelength.

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