A method for inverting chlorophyll content in plant leaves based on a smartphone

Abstract

The present invention discloses a method for inverting chlorophyll content of plant leaves based on a smart phone, comprising: obtaining digital images of plant leaves in different bands by using the smart phone and a specific band filter; simulating and calculating a correction coefficient look-up table through a 6S radiative transfer model; the correction coefficient look-up table being used to characterize the mapping relationship between different solar zenith angles and correction coefficients; calculating a correction coefficient for the normalized difference vegetation index in the visible light band according to the specific time and longitude and latitude when obtaining the digital images of plant leaves; correcting the traditional reflectance-based normalized difference vegetation index based on the correction coefficient to construct a visible light band normalized difference vegetation index based on radiance; constructing a regression model between the visible light band normalized difference vegetation index and the chlorophyll content; and inverting the chlorophyll content based on the visible light band normalized difference vegetation index and the constructed regression model.

Description

Technical Field

[0001] The present invention relates to the technical field of inverting chlorophyll content in plant leaves, and relates to, but is not limited to, a method for inverting chlorophyll content in plant leaves based on a smart phone. Background Art

[0002] In the inversion of chlorophyll content in plant leaves, relevant algorithms based on remote sensing data products are widely used. In such algorithms, calculating designed vegetation indices through remote sensing data products is a necessary step in the inversion process. In the existing calculation methods of remote sensing vegetation indices, the main method is to calculate relevant vegetation indices by using reflectance in different remote sensing data products, and use them for inverting chlorophyll content in plant leaves.

[0003] However, the acquisition of reflectance is relatively complex and often requires auxiliary devices such as whiteboards. There is no existing method in the prior art to directly use the camera sensor of a smart phone to invert chlorophyll content in plant leaves. The difficulty of this method is that the broadband reflectance radiance of the leaf received by the sensor is affected by the incident radiance during measurement. The vegetation indices calculated directly using radiance perform poorly in the inversion of chlorophyll content in plant leaves, and it is difficult to achieve accurate inversion of chlorophyll content in plant leaves. Therefore, compared with reflectance, radiance cannot stably reflect the leaf state. Summary of the Invention

[0004] In view of this, an embodiment of the present invention provides a method for inverting chlorophyll content in plant leaves based on a smart phone, which can directly calculate the visible light band normalized vegetation index based on radiance on the smart phone.

[0005] The technical solution of the embodiment of the present invention is realized as follows:

[0006] An embodiment of the present invention provides a method for inverting chlorophyll content in plant leaves based on a smart phone, and the method includes:

[0007] Using a smart phone and a specific band filter to obtain digital images of plant leaves in different bands; simulating and calculating a correction coefficient look-up table through a 6S radiative transfer model; the correction coefficient look-up table is used to characterize the mapping relationship between different solar zenith angles and correction coefficients; determining a target correction coefficient for the visible light band normalized vegetation index according to the specific time and longitude and latitude when obtaining the digital image of the plant leaf, in combination with the correction coefficient look-up table; correcting the traditional reflectance-based normalized vegetation index based on the target correction coefficient to construct a visible light band normalized vegetation index based on radiance; constructing a regression model between the visible light band normalized vegetation index and the chlorophyll content; and inverting the chlorophyll content based on the visible light band normalized vegetation index and the constructed regression model.

[0008] The beneficial effects brought by the technical solution provided in the embodiment of the present invention at least include:

[0009] In the embodiment of the present invention, the traditional visible light band normalized difference vegetation index (NDVI) based on reflectance is corrected. By using the 6S radiative transfer model, a correction coefficient lookup table for irradiance correction is designed and obtained, and the corresponding correction coefficient is obtained according to the specific time and longitude and latitude during measurement. This coefficient is real-time and on-site, and can be applied to various different measurement conditions. It is possible to directly calculate the visible light band NDVI based on irradiance using a smart phone. There is no need to use the reflectance in remote sensing data products, and the reflected irradiance data of plants can be directly obtained using a more convenient device. Calculating the visible light band NDVI using irradiance is simple in calculation, good in practicability, and easy to apply and popularize. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings, where:

[0011] Figure 1 It is a schematic flow chart of a method for inverting the chlorophyll content of plant leaves based on a smart phone provided in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, rather than all, of the embodiments of the present invention. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0013] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0014] It should be noted that the terms "first / second / third" involved in the embodiments of the present invention are only used to distinguish similar objects and do not represent a specific order for the objects. Understandably, "first / second / third" can be interchanged with a specific order or sequence under allowable circumstances, so that the embodiments of the present invention described here can be implemented in an order other than that illustrated or described here.

[0015] Those skilled in the art of this technology can understand that unless otherwise defined, all terms used here (including technical terms and scientific terms) have the same meaning as the general understanding of those of ordinary skill in the art in the field to which the embodiments of the present invention belong. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless specifically defined as here.

[0016] Figure 1 It is a schematic flowchart of a method for inverting the chlorophyll content of plant leaves based on a smart phone provided for the embodiments of the present invention, as Figure 1 shown, the method at least includes the following steps:

[0017] Step S110, using a smart phone and a specific band filter to obtain digital images of plant leaves in different bands.

[0018] Here, the camera sensor in the smart phone can obtain the radiance spectral data in a wide band range, and there is no need to use the reflectance in the remote sensing data product in the calculation. In implementation, the plant leaves are vertically photographed and measured to obtain the digital images of their leaves, and the radiance spectral values are extracted.

[0019] In some embodiments, the smart phone is not limited to a specific mobile phone brand, and only needs to include a camera sensor. It can also be replaced by other terminal devices with similar functions, such as laptop computers, tablet computers, handheld Internet devices, multimedia devices, streaming media devices or other types of electronic devices.

[0020] In some embodiments, the specific band filter is a visible light band single-pass filter. When used in combination with a smart phone, it plays a role similar to that of a spectrometer, and usually the visible light band suitable for the camera sensor is selected for measurement.

[0021] Step S120, simulating and calculating through the 6S radiative transfer model to obtain a correction coefficient lookup table.

[0022] Here, the correction coefficient lookup table is used to represent the mapping relationship between different solar zenith angles and correction coefficients.

[0023] Models for simulating the atmospheric radiation transfer process can be used to calculate the correction coefficients, such as the LESS and Modtran models, which can also be used for data simulation. In the embodiments of the present invention, the 6S radiation transfer model (Second Simulation of The Satellite Signal in The Solar Spectrum) is used for simulation, which can simulate the radiation process required by the present invention more accurately and in line with the actual situation, and is the most reasonable choice for obtaining the most accurate inter-band correction coefficients for the results.

[0024] The 6S model is a widely used method in the radiation transfer model. It is an improvement of the 5S model (Simulation of The Satellite Signal in The Solar Spectrum), which absorbs the latest scattering calculation algorithm and establishes a relevant model using the principle of electromagnetic wave radiation transfer in the atmosphere. According to the technical problems to be solved by the present invention, the parameters to be set in the simulation are shown in Table 1, where the solar zenith angle and the solar altitude angle are complementary:

[0025] Table 1 6S model parameter settings

[0026]

[0027]

[0028] According to different atmospheric conditions or specific requirements, each parameter can be appropriately adjusted within the default range according to the actual situation.

[0029] Step S130: Determine the target correction coefficient for the visible light band normalized difference vegetation index according to the specific time and longitude and latitude when obtaining the digital image of the plant leaf, in combination with the correction coefficient look-up table.

[0030] Here, according to the specific time and longitude and latitude during the measurement, the corresponding correction coefficient is queried from the pre-constructed correction coefficient table. It should be noted that this correction coefficient is real-time and on-site and can be applied to various different measurement conditions.

[0031] Step S140: Correct the traditional reflectance-based normalized difference vegetation index based on the target correction coefficient to construct the visible light band normalized difference vegetation index based on radiance.

[0032] Here, the traditional reflectance-based normalized difference vegetation index is calculated by combining the solar irradiance ratio in different bands of the solar incident radiation and the reflectance calculation method in different bands. In the embodiments of the present invention, a correction coefficient for the normalized difference vegetation index in the visible light band is introduced for the involved bands, and the normalized difference vegetation index in the visible light band based on radiance is calculated.

[0033] The method of the present invention is applicable to any ratio-type vegetation index for the inversion of chlorophyll or other crop parameters, and the corresponding correction coefficient can be re-simulated and calculated for the bands involved in the vegetation index to be applied.

[0034] Step S150: Construct a regression model between the normalized difference vegetation index in the visible light band and the chlorophyll content.

[0035] Here, a regression model between the normalized difference vegetation index in the visible light band and the chlorophyll content of plant leaves is constructed, and the optimal model is selected. Multiple inversion regression models can be selected for inversion attempts. Common models include linear, quadratic, and exponential models. As shown in Table 2, the most suitable model can be selected according to the relevant results of the inversion.

[0036] Step S160: Invert the chlorophyll content based on the normalized difference vegetation index in the visible light band and the constructed regression model.

[0037] Here, the chlorophyll of plant leaves can be inverted through the corrected normalized difference vegetation index in the visible light band based on radiance, which can improve the inversion efficiency.

[0038] In the embodiments of the present invention, the traditional reflectance-based normalized difference vegetation index in the visible light band is corrected. By using the 6S radiative transfer model, a correction coefficient lookup table for correcting radiance is designed and obtained, and the corresponding correction coefficient is obtained according to the specific time and longitude and latitude during the measurement. This coefficient is real-time and on-site and can be applied to various different measurement conditions. It is possible to directly calculate the normalized difference vegetation index in the visible light band based on radiance on a smartphone. There is no need to use the reflectance in remote sensing data products, and the reflected radiance data of plants can be directly obtained using more convenient devices. Calculating the normalized difference vegetation index in the visible light band using radiance is simple in calculation, good in practicability, and easy to apply and popularize.

[0039] In some possible embodiments, in the above step S110, digital images of plant leaves under the 600 nm and 485 nm band filters are obtained.

[0040] Here, the normalized difference vegetation indices corresponding to the above two bands have a good effect on retrieving leaf chlorophyll content; and both of these two bands are visible light bands, which are suitable for camera sensors of various smartphone brands to obtain radiance spectral data today. Among them, the two bands of 600 nm and 485 nm are used. The embodiment of the present invention calculates a new visible light band normalized difference vegetation index based on radiance after introducing a correction coefficient based on this normalized difference vegetation index. Therefore, these two related bands are selected for measurement.

[0041] In some possible embodiments, the above step S120 "simulating and calculating a correction coefficient look-up table through the 6S radiative transfer model" is implemented through the following process: obtaining the direct solar irradiance LS of a specific band through the radiative transfer process of the 6S model λ ; calculating the ratio of the direct solar irradiance LS λ between different bands under different atmospheric conditions as the correction coefficient to obtain a correction coefficient look-up table; where the different atmospheric conditions are characterized by different solar zenith angles determined by different specific times and longitudes and latitudes.

[0042] Here, taking the simulation of the correction coefficient of 485 nm to 600 nm under various atmospheric conditions and finally obtaining the correction coefficient look-up table as an example for illustration. The direct solar irradiance LS λ (s.outputs.direct_solar_irradiance) corresponding to each band is obtained through the radiative transfer process of the 6S model, and the correction coefficient under various atmospheric conditions is calculated by the following formula, and the median value is taken as the final value.

[0043]

[0044] Among them, c represents the correction coefficient, and LS 600nm and LS 485nm are the direct solar irradiance corresponding to 600 nm and the direct solar irradiance corresponding to 485 nm respectively.

[0045] Through simulation calculations, a correction coefficient look-up table of the correction coefficient under specific dates, longitudes and latitudes, and different solar altitude angles can be finally obtained, as shown in Table 2, where the solar zenith angle and the solar altitude angle are complementary.

[0046] Table 2 Correction Coefficient Look-up Table

[0047]

[0048]

[0049]

[0050]

[0051] In some possible embodiments, the above step S130, "According to the specific time and longitude and latitude when acquiring the digital image of the plant leaf, and in combination with the correction coefficient look-up table, determine the target correction coefficient for the visible light band normalized difference vegetation index", is further implemented through the following process: Based on the specific time and longitude and latitude, determine the corresponding solar altitude angle under the current atmospheric conditions; take the complementary angle of the solar altitude angle to obtain the solar zenith angle, and find the corresponding correction coefficient in the correction coefficient look-up table as the target correction coefficient.

[0052] Here, under normal circumstances, the calculation formula for the solar altitude angle is:

[0053] sinh = sinδ·sinφ + cosδ·cosφ Formula (2);

[0054] In this equation, h is the solar altitude angle, φ is the latitude of the current location, δ represents the solar declination angle, and τ represents the solar hour angle. Among them, the solar declination angle δ can be calculated by Formula (3):

[0055]

[0056] Among them, N is the day of the year, referring to the Nth day of the year, which can be deduced from the specific time of acquiring the digital image. And the solar hour angle τ can be calculated from the longitude and the specific moment:

[0057] τ = 15×(t + (Λ - 120) / 15 - 12) Formula (4);

[0058] Among them, t is the specific time when acquiring the digital image of the plant leaf, and Λ represents the longitude of the current location.

[0059] Through the information of the specific measurement time and longitude and latitude, the corresponding solar altitude angle under the current atmospheric conditions can be obtained, and then the complementary angle is taken to obtain the solar zenith angle, and finally the corresponding correction coefficient is found in the correction coefficient look-up table.

[0060] In some possible embodiments, the above step S140, "Based on the correction coefficient, correct the traditional reflectance-based normalized difference vegetation index to construct the visible light band normalized difference vegetation index based on radiance", further includes the following steps:

[0061] Calculate the traditional visible light band normalized difference vegetation index α0 through the following formula:

[0062]

[0063] Among them, r 600nm and r 485nm are the reflectances of two bands respectively, and the calculation method is: L represents the radiance reflected by the leaves in each band, which is directly obtained by the smartphone; LS is the incident radiance, corresponding to different bands.

[0064] The normalized difference vegetation index α1 in the visible light band based on reflectance is calculated by the following formula:

[0065]

[0066] By introducing a correction coefficient, the new normalized difference vegetation index in the visible light band based on radiance can be expressed as:

[0067]

[0068] Then, the regression model between the normalized difference vegetation index in the visible light band based on radiance and chlorophyll can be constructed, and the optimal model can be selected. And chlorophyll is inverted based on the regression model.

[0069] The method of the present invention is applicable to any ratio-type vegetation index for inverting chlorophyll or other crop parameters, and the corresponding correction coefficient can be re-simulated and calculated for the bands involved in the vegetation index to be applied.

[0070] The following is a description of the above method for inverting the chlorophyll content of plant leaves based on a smartphone in combination with a specific embodiment. However, it should be noted that this specific embodiment is only for better explaining the present invention and does not constitute an improper limitation of the present invention.

[0071] Experiments were carried out on the method for inverting the chlorophyll content of plant leaves proposed by the present invention, and the experimental results of the data are as follows: Record the geographical location of the plants and the measurement time, use the smartphone and single-pass filter films in the 600nm and 485nm bands, and the method for obtaining the chlorophyll content of leaves in the laboratory to vertically photograph and measure the leaves of the plants (summer maize), obtain the digital images of the upper and lower leaves and extract relevant data such as the radiance spectral values and chlorophyll content. At the same time, the normalized difference vegetation index in the visible light band based on radiance was calculated according to the method proposed by the present invention, and the chlorophyll content of the leaves was inverted. The results are shown in Table 3 below.

[0072] Table 3 Chlorophyll inversion results

[0073]

[0074] Note: α’ represents the normalized difference vegetation index in the visible light band directly calculated from the radiance; α is the result obtained by inverting the ground chlorophyll index calculated after introducing the correction coefficient.

[0075] The data in Table 3 proves that the embodiments of the present invention have relatively good performance in the correlation and accuracy of the final inversion results, and the embodiments of the present invention do not overly focus on the selection of the final inversion model. As shown in Table 3, any commonly used inversion model can be applied to the final inversion process of the present invention.

[0076] Based on the same inventive concept, an embodiment of the present invention provides an electronic device for implementing the method for inverting the chlorophyll content of plant leaves based on a smart phone described in the above method embodiment.

[0077] It should be noted that in the embodiments of the present invention, if the method for inverting the chlorophyll content of plant leaves based on a smart phone is implemented in the form of software functional modules and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the related technology can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing an electronic device to execute all or part of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), magnetic disks, or optical discs that can store program codes. Thus, the embodiments of the present invention are not limited to any specific combination of hardware and software.

[0078] Correspondingly, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps in any one of the above-described methods for inverting the chlorophyll content of plant leaves based on a smart phone. Correspondingly, in the embodiments of the present invention, a computer program product is also provided. When the computer program product is executed by the processor of an electronic device, it is used to implement the steps in any one of the above-described methods for inverting the chlorophyll content of plant leaves based on a smart phone.

[0079] It should be pointed out here that: the descriptions of the above storage medium and device embodiments are similar to the descriptions of the above method embodiments and have beneficial effects similar to those of the method embodiments. For the technical details not disclosed in the storage medium and device embodiments of the present invention, please refer to the descriptions of the method embodiments of the present invention for understanding.

[0080] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, the appearances of "in one embodiment" or "in an embodiment" throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the magnitudes of the serial numbers of the above processes do not mean the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention. The serial numbers of the embodiments of the present invention above are only for description and do not represent the advantages or disadvantages of the embodiments.

[0081] It should be noted that in this text, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of another identical element in the process, method, article or device comprising that element.

[0082] The methods disclosed in several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments. The features disclosed in several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0083] As described above, it is only the implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for inverting chlorophyll content in plant leaves based on a smartphone, characterized in that: include: Use a smartphone and specific band filters to obtain digital images of plant leaves in different bands; A correction coefficient lookup table is obtained by simulating and calculating the 6S radiation transfer model; the correction coefficient lookup table is used to characterize the mapping relationship between different solar zenith angles and correction coefficients; According to the specific time and longitude and latitude when the digital image of the plant leaf is obtained, the target correction coefficient for the normalized vegetation index in the visible light band is determined in combination with the correction coefficient lookup table; Based on the target correction coefficient, the traditional normalized vegetation index based on reflectivity is corrected to construct a visible light band normalized vegetation index based on radiance; Constructing a regression model between the visible light band normalized vegetation index and chlorophyll content; The chlorophyll content is inverted based on the normalized vegetation index in the visible light band and the constructed regression model.

2. The method for inverting chlorophyll content in plant leaves based on a smartphone according to claim 1, characterized in that: The specific wavelength band filter is a single-pass filter in the visible light band.

3. The method for inverting chlorophyll content in plant leaves based on a smartphone according to claim 1, characterized in that: The correction coefficient lookup table obtained by simulation and calculation using the 6S radiation transmission model includes: The direct solar irradiance LS in a specific band is obtained through the 6S model radiation transfer process λ ; Calculate the direct solar irradiance LS between different bands under different atmospheric conditions λ The ratio of is used as the correction coefficient to obtain a correction coefficient lookup table; wherein the different atmospheric conditions are characterized by different solar zenith angles determined by different specific times and longitudes and latitudes.

4. The method for inverting chlorophyll content in plant leaves based on a smartphone according to any one of claims 1 to 3, characterized in that: The method of determining the target correction coefficient for the normalized vegetation index in the visible light band according to the specific time and longitude and latitude when the digital image of the plant leaf is obtained and combined with the correction coefficient lookup table includes: Based on the specific time and longitude and latitude, determine the solar altitude angle corresponding to the current atmospheric conditions; The complementary angle of the solar altitude angle is taken to obtain the solar zenith angle, and the corresponding correction coefficient is found in the correction coefficient lookup table as the target correction coefficient.

5. The method for inverting chlorophyll content in plant leaves based on a smart phone according to claim 4, characterized in that: Determining the solar altitude angle corresponding to the current atmospheric conditions includes: Calculate the solar hour angle τ: τ = 15 × (t + (Λ-120) / 15-12); where t is the specific time when the digital image of the plant leaf is obtained, and Λ represents the longitude of the current position; Calculate the solar declination angle δ: Wherein, N is the accumulated day, indicating the Nth day in a year, calculated by the specific time; Calculate the solar altitude angle h: sinh = sinδ·sinφ+cosδ·cosφ·cosτ; where φ is the latitude of the current position; δ represents the solar declination angle; and τ represents the solar hour angle.

6. The method for inverting chlorophyll content in plant leaves based on a smart phone according to claim 3, characterized in that: The different wavelengths include 600nm and 485nm, and the correction coefficient is expressed as LS 600nm LS 485nm They are the direct solar irradiance corresponding to 600nm and the direct solar irradiance corresponding to 485nm respectively.

7. The method for inverting chlorophyll content in plant leaves based on a smart phone according to claim 6, characterized in that: The method of correcting the conventional normalized vegetation index based on reflectivity based on the correction coefficient to construct a visible light band normalized vegetation index based on radiance includes: The traditional visible light band normalized vegetation index α0 is calculated by the following formula: Among them, r 600nm and r 485nm are the reflectances of the two bands respectively, and are calculated as follows: L represents the radiance reflected by the blade in each band, which is directly obtained by the smartphone; LS is the incident radiance, corresponding to different bands; The normalized vegetation index α1 in the visible light band based on reflectance is calculated by the following formula: Introducing the correction coefficient, the normalized vegetation index α2 of the visible light band based on radiance can be expressed as:

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