Evaluation method for multi-angle telemetry of fruit tree canopy fruit quality

Abstract

The invention provides an evaluation method for multi-angle telemetry of fruit tree canopy fruit quality, and solves the important problem of multi-scale in-situ precise calibration. Non-coherent light far-field partial coherence characteristics and different-angle telemetering spectrums are greatly different. The typical environment of an orchard in a to-be-measured area is selected by combining satellite remote sensing images and a ground interactive verification experiment, illumination and a temperature field are gridded in a spatial orientation, the reflectivity change rule of the same fruit in different angle polarization directions through multispectral and multi-angle polarization is utilized, and the multi-angle polarization spectrum morphological parameters are utilized to determine the orchard in the to-be-measured area. And fitting a multi-angle polarization reflectivity curve model. Spectrum quantitative detection and multi-angle unmanned aerial vehicle spectrum image area evaluation are combined through multiple times of calibration of a spectrograph detection angle and a spectrum camera field angle. A spectrograph detects and reconstructs a fruit canopy spectral image from multiple angles, and in-situ calibration and regional quality monitoring and evaluation of fruit tree canopy fruit quality are achieved.

Description

Technical Field

[0001] The present invention specifically relates to an evaluation method for remotely measuring the fruit quality of a fruit tree canopy from multiple angles Background Art

[0002] Remote sensing of the fruit quality in a region is of great significance. However, the far-field measurement environment of fruit tree fruits is complex, and how to accurately calibrate in situ at the canopy scale is an important issue. There are certain differences in temperature difference and light conditions in different regions, and even the quality of fruits at different growth positions on the same fruit tree varies greatly. That is, the fruit quality is affected by environmental factors such as outdoor light and temperature, and has certain regional distribution characteristics, which is also the result of the adaptability of fruit quality to the temperature and light environment. Affected by light and temperature conditions, the far-field spectra at different angles are different, which has a great impact on the detection efficiency and accuracy of regional fruit quality

[0003] To solve this problem, the prior art has adopted an unmanned aerial vehicle multi-spectral camera for detection to improve efficiency, but there is a disadvantage of low detection accuracy. Conventional fruit detection spectral models use a hyperspectral camera with high spectral resolution to improve the detection accuracy of fruit quality, but there are disadvantages of large data volume and high cost

[0004] In view of this, the present invention proposes a new multi-angle fruit spatial classification and marking quantitative detection model and its modeling method, which can use a spectrometer with relatively low spectral resolution to obtain high detection accuracy Summary of the Invention

[0005] The purpose of the present invention is to provide an evaluation method for remotely measuring the fruit quality of a fruit tree canopy. By using an unmanned aerial vehicle to perform multi-spectral and multi-angle polarization on the same fruit, the change law of the reflectance in different angle polarization directions is obtained, and the reflectance curve model is fitted to quantitatively detect the fruit quality accuracy. Then, a polarizer is added at the detection end to change the direction for calibration at different angles, realizing the calibration of a low-resolution spectral camera by a high-resolution spectrometer and improving the quantitative detection accuracy of spectral imaging

[0006] In order to achieve the above purpose, the technical solution adopted is as follows

[0007] An evaluation method for remotely measuring the fruit quality of a fruit tree canopy from multiple angles, comprising the following steps

[0008] S10: Establish a far-field spectral response function model of the fruit: The fruit is equivalent to a scattering medium to quantitatively describe the quality parameters, calculate the far-field spectra of the fruit at different angles, and establish a multi-angle spectral response function model through the change law of the morphological parameters. Accurately calibrate the angle, select the characteristic angles of the fruit reflectance, and establish a multi-angle reflectance database

[0009] S20: Perform grid processing on the temperature and illumination of the area to be measured using high-precision satellite remote sensing image data, and classify and partition the temperature and illumination characteristics of the grid-based orchard area through ground experimental control points;

[0010] S30: Establish a multi-angle profiling calibration field based on the temperature and illumination characteristics of the grid-based orchard area, and achieve multi-scale multi-angle quantitative detection through the transfer of quantitative detection models in the multi-angle reflectance quantitative detection database and the spectral database, combining spectral quantitative detection with the evaluation of UAV spectral image areas;

[0011] S40: Use a UAV multi-spectral camera to obtain the variation law of the reflectance of fruits in the same area at different angles and polarization directions, and fit the spectral reflectance curve model and spectral response function at the regional scale to determine the fruit quality.

[0012] Furthermore, for the said modeling method, modeling is first carried out indoors and then outdoors.

[0013] Still further, when carrying out modeling outdoors, in step S10, it is necessary to establish an angular spectral response function model and transfer multi-angle and spectral quantitative models.

[0014] Still further, for the transfer of the multi-angle and spectral quantitative models: taking the spatial azimuth distance and angle as key parameters, fitting the correlation coefficient model of angle, wavelength, and reflectance data to achieve the transfer of the multi-angle reflectance database model and the spectral database model.

[0015] Furthermore, in step S10: Based on the calibration of the standard plate angles in different detection spaces, establish a fruit angle reflectance model.

[0016] Furthermore, in step S20: For the grid division method of the space to be measured, verify each other through the correspondence between the image field angles at different distances of the spectral camera and the field angles of the spectrometer.

[0017] Furthermore, step S30 is: Correct the multi-angle reflectance database by the method of cross-verifying the reflectance of fruits at different positions inverted from the reflectance of a single fruit, realize the one-to-one correspondence between the spectral reflectance and the corresponding position of the spectral image, and establish a multi-angle spectral and image data model library by spatially dividing fruits at different angles and distances and matching the angles and azimuths in the model library.

[0018] Furthermore, in step S40: Establish a multi-angle calibration database through experiments with standard calibration plates at different azimuths, collect cross-verification and optimization of the multi-angle reflectance experiments of fruits and the angle-azimuth model, establish a multi-angle spatial azimuth spectral model of fruits, and combine the establishment of a spectral angle reflectance model to achieve refined quantitative description of the spatial spectra of different azimuths.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0020] The present invention combines satellite remote sensing images and ground interaction verification experiments to select typical orchard environments in the area to be measured. Through spatial azimuth grid illumination and temperature fields, a multi-angle profiling calibration field is established based on the temperature and illumination characteristics of the grid orchard area. The detection angle of the spectrometer and the field of view angle of the spectral camera are calibrated, and spectral quantitative detection is combined with the evaluation of multi-angle UAV spectral image areas. The variation law of the reflectance of the same fruit product in different angular polarization directions is utilized by the multi-spectral multi-angle polarization of the UAV to fit the reflectance curve model to quantitatively detect the accuracy of the fruit product quality. A multi-angle profiling calibration field is established based on the temperature and illumination characteristics of the grid orchard area, and spectral quantitative detection is combined with the evaluation of UAV spectral image areas.

[0021] Among them, for the multi-angle telemetry device of the fruit product:

[0022] A multi-angle profiling calibration field is established based on the temperature and illumination characteristics of the grid orchard area. The detection angle of the spectrometer and the field of view angle of the spectral camera are calibrated, and spectral quantitative detection is combined with the evaluation of multi-angle UAV spectral image areas. A multi-angle profiling calibration field is established according to the shape of the fruit tree. The telescope and the spectrometer lens are coaxial, and the field of view angle is the same as the detection angle of the spectrometer. There is a position on this device where the spectrometer can be replaced. The position and angle of the portable spectrometer are calibrated one by one to establish a distributed multi-angle calibration acquisition device for fruit products. Through the Bluetooth and WiFi modules of the distributed telemetry device, remote networking communication is carried out. The spectrometer detects multi-angles to reconstruct the spectral image of the fruit canopy, and in-situ calibration and regional quality monitoring and evaluation of the fruit quality of the fruit tree canopy are realized.

[0023] Lenses and polarizers are installed at the front end of the spectrometer to design different distances. A multi-angle profiling calibration field is established according to the shape of the fruit tree. The portable spectral sensor is equipped with a lens polarizer and a quarter-wave plate for in-situ spectral acquisition and calibration of the fruit products in the fruit tree canopy. Bluetooth or WiFi functions are added to form an Internet of Things to realize real-time monitoring of the fruit quality in the far field.

[0024] Secondly, a polarizer is added to the detection end to change the polarization direction for polarization calibration of multiple polarization directions and polarization multi-angle reflectance. The four polarization directions of the polarizer are (0p, 45p, 90p, 135p). Correspondingly, for the horizontal direction (0 - 360 degrees), it rotates 8 directions (0, 45, 90, 135, 180, 225, 270, 315) every 45 degrees; for the vertical direction, it changes the elevation angle (0 - 90 degrees) to obtain spectral images at multiple angles, and fits the multi-angle polarization reflectance function model. The present invention realizes the calibration of a low-resolution spectral camera by a high-resolution spectrometer. The present invention makes quality space distribution maps for thematic data fusion, conducts regional fruit quality distribution, moisture distribution, acidity distribution, and sugar content distribution, combines geographical information, illumination, temperature, and GPS coordinates, and marks to establish a big data system for fruit quality distribution environment, providing data support for improving the quality and efficiency of the fruit industry and future layout and structural adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic flow chart of the method for evaluating the quality of fruits in the canopy of a multi-angle remote sensing fruit tree according to an embodiment of the present invention;

[0026] Figure 2 It is a schematic diagram of spectral morphological characteristic parameters according to an embodiment of the present invention;

[0027] Figure 3 It is a schematic diagram of the hyperspectral image of fruits and the spectral diagrams of fruits at different angles according to an embodiment of the present invention;

[0028] Figure 4 It is a schematic diagram of the far-field spectrum of a height-uncorrelated scatterer according to an embodiment of the present invention;

[0029] Figure 5 It is a schematic diagram of the far-field spectrum of a height-correlated scatterer according to an embodiment of the present invention;

[0030] Figure 6 It is a schematic diagram of the correlation coefficient and significance analysis of multiple parameters according to an embodiment of the present invention;

[0031] Figure 7 It is a schematic flow chart of the multi-scale multi-angle fruit classification and marking quantitative detection according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] In order to further elaborate a method for evaluating the quality of fruits in the canopy of a multi-angle remote sensing fruit tree according to the present invention and achieve the expected invention purpose, the following, in combination with preferred embodiments, details the specific implementation manner, structure, characteristics, and effects of a method for evaluating the quality of fruits in the canopy of a multi-angle remote sensing fruit tree proposed according to the present invention. In the following description, different "one embodiment" or "embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0033] The following will further introduce in detail an evaluation method for multi-angle remote sensing of fruit quality in the fruit tree canopy of the present invention in combination with specific embodiments:

[0034] In the technical solution of the present invention, through high-precision satellite remote sensing image data and grid processing of temperature and light in the area to be measured, including: classifying and partitioning the temperature and light characteristics of the grid orchard area through ground experimental control points; establishing a multi-angle conformal calibration field according to the fruit tree characteristic parameters, and determining the inversion grid area through spectral multi-angle and image cross-verification; through the spectral response function of the fruit area calibration, combining spectral quantitative detection with the evaluation of the drone spectral image area; among them, the method for grid division of the space to be measured mainly corresponds to the field of view angles of images at different distances of the spectral camera and the field of view angle of the spectrometer, and cross-verifies, etc., to solve the problem of how to improve the detection accuracy when the spectrometer with low spectral resolution is used. The technical solution of the present invention is as follows:

[0035] An evaluation method for multi-angle remote sensing of fruit quality in the fruit tree canopy, as Figure 1 shown, includes the following steps:

[0036] S10: Establish a multi-angle spectral response function model for fruits: The fruit is equivalent to a random scattering medium, and the scattering body scales and refractive indices of fruits at different maturity stages are different, represented by kσ, and measure the spectral quality of the fruits. According to the theory of partial coherence and spectral transmission, numerically calculate the far-field spectra of fruits at different angles, quantify the differences in spectral changes with the spectral morphological characteristics, and establish a multi-angle spectral response function model for fruits through spectral morphological parameters.

[0037] The extraction of spectral morphological characteristics is respectively: 7 characteristic parameters such as peak height, peak area, full width at half maximum, left slope of full width at half maximum, right slope of full width at half maximum, left shoulder width, and right shoulder width. Accurately calibrate the angle and polarization, select the polarization characteristics of the fruit reflectivity at the characteristic angle, and establish a multi-angle polarization reflectivity database;

[0038] S20: Perform grid processing on the temperature and light in the area to be measured through high-precision satellite remote sensing image data, and classify and partition the temperature and light characteristics of the grid orchard area through ground experimental control points.

[0039] S30: Establish a multi-angle conformal calibration field through the temperature and light characteristics of the grid orchard area, and realize multi-scale multi-angle quantitative detection through the transfer of the quantitative detection model of the multi-angle reflectivity quantitative detection database and the spectral database, and combine spectral quantitative detection with the evaluation of the drone spectral image area.

[0040] S40: Use the drone multi-spectral camera to obtain the variation law of the reflectivity of fruits in the same area at different angles and polarization directions, fit the spectral reflectivity curve model and spectral response function at the regional scale to determine the fruit quality.

[0041] Preferably, the modeling method is to first perform modeling indoors and then perform modeling outdoors.

[0042] Further preferably, when modeling is performed outdoors, it is necessary to establish an angle spectral response function model, a multi-angle and spectral quantitative model transfer in step S10.

[0043] Further preferably, the multi-angle and spectral quantitative model transfer: taking the spatial orientation distance and angle as key parameters, fitting the angle, wavelength, and reflectivity data correlation coefficient model to realize the multi-angle reflectivity database model and spectral database model transfer.

[0044] Preferably, in step S10: the angle reflectivity model of the fruit is established according to the angle calibration of the standard plates in different detection spaces.

[0045] Preferably, in step S20: the gridding method of the space to be measured is carried out by corresponding the field of view angles of the images at different distances of the spectral camera and the field of view angle of the spectrometer, and verifying each other.

[0046] Preferably, step S30 is: correcting the multi-angle reflectance database by interactively verifying the reflectance of fruits at different positions by inverting the reflectance of a single fruit, realizing a one-to-one correspondence between the spectral reflectance and the corresponding position of the spectral image, and establishing a multi-angle spectral and image data model library by spatially segmenting fruits at different angles and distances and matching the angles and orientations in the model library.

[0047] Preferably, in step S40: a multi-angle calibration database is established through experiments on standard calibration plates in different orientations, multi-angle reflectance experiments of fruits are collected for interactive verification and optimization of angle orientation models, a multi-angle spatial orientation spectral model of fruits is established, and a spectral angle reflectance model is established in combination to achieve a refined quantitative description of spatial spectral images in different orientations.

[0048] Example 1.

[0049] Theoretical basis: Grid partitioning of spectral coherent transmission theory

[0050] According to the theory of spectral spatial coherence: the spectral coherence between two points is described and predicted by the specific quantity of partially coherent light. The distance difference between two adjacent points is also the spectral coherence width. The spatial correlation infers the correlation characteristics of different scale areas. Since the spatial coherence is uniform and stable only in a very small field of view or vertical observation (such as Figure 3 3-1 (scattering characteristics of highly correlated scatterer κσs>>1), 3-2, 3-3, 3-4, 3-5, and 3-6) are also the theoretical basis for spatial classification and partitioning marking.

[0051] Space solid angle △Ω 2 / Interval2 The spatial solid angle is related to the wavelength λ and the source size a, and is independent of the distance. Generally, the farther the distance, the larger the coherent area. [1] 。

[0052] The coherence length L = long 2 / △λ and A = 0.063R 2 λ 2 / = 0 2 The coherent area divides the boundary of the spatial region. For the sampling of non-in-situ quantitative detection of fruits, the calibration and prediction inversion regions correspond to different angles. The solid angle is determined by calculating the corresponding characteristic wavelength to determine the coherent region. If it exceeds the range of the spatial solid angle, it is classified and marked. The solid angle is equivalent to the spectral correlation between any two points in space. Therefore, the classification and marking of the coherent region are also important constraints for multi-scale spectral imaging quantitative detection.

[0053] (1) Precise angle calibration of the indoor BRDF device:

[0054] ① Theoretical calculation of immature fruits: When the fruits are immature, the surface is smooth and is equivalent to a highly correlated scatterer. Kσ is the correlation length, corresponding to the refractive index and maturity of the fruits. The scattering angles of fruits at different times determine the spatial detection region and orientation. When kσ >> 1 for highly correlated scatterers, the far-field spectra at different angles are significantly different and are greatly affected by the observation angle. For example Figure 4 、 Figure 5 Analysis of the calculation results when kσ = 7.5. When Kσ << 1 for weakly correlated scatterers, the far-field spectra at different angles are not significantly different. The theoretical calculation formula is as follows:

[0055]

[0056] ② Calculation of mature fruits: When the fruits are mature, the surface has random texture and unevenness, and is equivalent to a weakly correlated scatterer. When kσ << 1 for weakly correlated scatterers, the far-field spectra at different angles are not significantly different and can be considered to be less affected by the observation angle. For example Figure 2 Analysis of the calculation results in 2-1 when kσ = 2.5. When kσ << 1 for weakly correlated scatterers, the far-field spectra at different angles are not significantly different, as shown in Figure 2 Analysis of the calculation results in 2-2.

[0057] After performing far-field calculations using the fruit quality spectrum equivalent to the light source spectrum, the far-field spectra at different angles of the hyperspectral image are angle-calibrated and replaced. Based on the fruit quality spectrum plus the lighting conditions and detection angle method, the spectral angle response function of the fruit is reconstructed. Predict and invert the fruit quality spectrum in the unknown region.

[0058]

[0059] ③Precisely calibrate the indoor BRDF device at different angles, and select the characteristic angles of fruit reflectance to establish an angle database. Calibrate the angles of standard plates in different detection spaces and establish a fruit angle reflectance model. BRDF collects the light intensity reflectance at different angles of red dates in the crisp and ripe period, and adds the spectrum multi-angle polarization collection data. A large number of experiments are performed to fit the polarization spectrum reflectance at different angles to establish an angle polarization spectrum quantitative detection model. The surface of red dates in the crisp and ripe period is smooth and has obvious polarization characteristics. Make some red, green, and red and white red date samples to measure the multi-angle spectral reflectance, and fit the curve equation to quantitatively detect the fruit quality.

[0060] The ring light source is used to light up in sequence to simulate the lighting conditions of the sun at different times, avoiding image registration and realizing a multi-angle reflectivity database of typical lighting in the area to be measured at different times.

[0061] (2) Gridded angle and azimuth calibration spectral model for indoor space to be measured:

[0062] ① Theoretical simulation calculation of different fruit angle feature models:

[0063] According to the multi-scale scattering mechanism, the scattering coefficient quantitatively describes the scattering characteristics through the scattering angle characteristics in different directions, and realizes cross-scale quantitative detection and evaluation. The equivalent scattering medium, particle size and refractive index of the fruit quantitatively describe the different quality characteristics. According to the different periods of fruit maturity, the tissue sections of a single fruit are collected to measure the pore size distribution. The mieplot scattering spectrum software is used to obtain the scattering cross section, scattering coefficient, and scattering angle at the single fruit scale. The information such as the shape of the fruit tree (round head type, open heart type), tree height, and row spacing are collected. The multi-scale scattering characteristics such as the scattering coefficient and absorption coefficient of the fruit tree canopy are calculated. The scattering parameters of different fruits, such as the scattering characteristics of different scales, the scattering coefficient of leaves, the absorption coefficient, and the reflectivity at different angles can be simulated and calculated by the three-dimensional radiation transmission less software at the fruit canopy scale. The scattering coefficient and absorption coefficient of the fruit tree canopy are obtained. The appropriate acquisition angle and gridding are selected for spatial orientation calibration. The scattering coefficient and absorption coefficient of the single fruit and canopy are quantitatively characterized and multi-scale scattering is characterized.

[0064] ② Gridding method for the spatial coherence feature scale to be measured:

[0065] The spatial resolution of the camera is affected by the lens and the detection distance. According to the detector lens diffraction resolution angle θ = 1.22λθ = 1.22λ / D and the detector front field of view FOV = x / y coherence length L = λ 2 / △λ and A=0.063R 2 λ 2 / πa 2The coherent area divides the boundary of the spatial region (D is the lens diameter, λ is the wavelength, △λ is the bandwidth, a is the light source radius, and R is the distance from the light source to the light screen). Considering the MTF (Modulation Transfer Function) to describe the spatial resolution at different angles. As the distance increases, the field of view angle FOV becomes smaller, the spatial cut-off frequency decreases, high-frequency detail information cannot be obtained, and the spatial resolution decreases. By collecting data from multiple angles, frequency stitching compensates for the resolution decrease caused by the spatial cut-off frequency. The iterative algorithm for collecting data from multiple angles improves the spatial resolution. By changing different angles, increasing the high-frequency image details at large angles, and spatial frequency registration improves the spatial resolution. Changing the spatial angle azimuth and image position in sequence for cross-validation improves the accuracy of radiation calibration.

[0066] Perform correlation analysis on the significance factors of reflectance, angle, degree of polarization, and soluble solids content SSC. Under the condition of extremely significant at 0.01, the correlation coefficient characteristics can lead to the conclusion that there is a significant correlation among dolp, band, and ssc. As Figure 6 shown.

[0067] ③ Multi-angle ground profiling calibration method:

[0068] Through grid processing of the spatial coherent feature scale of the space to be measured, select control points to divide the inversion area, and use a fiber optic spectrometer and a multi-spectral camera for fixed-point multi-angle calibration and mutual verification. Specifically, divide by the illumination characteristics of a single fruit, multiple fruits are combined in a single row ROI to form a single-row reflectance feature, and multiple fruits are combined in a column to form the row-column feature of the overall fruit image. Invert the reflectance characteristics of the fruit quality in the entire area through the combination of the regions of interest (ROIs) of different rows and columns of fruits. Establish a multi-angle profiling ground calibration field for fruit trees based on the structural parameters such as the growth tree shape, tree height, row spacing, and plant spacing of the fruit trees in the orchard. Establish a multi-angle profiling calibration field through the grid of temperature and illumination characteristics in the orchard area, and perform cross-validation on the grid space spectrum and image of the multi-angle ground profiling calibration field to establish a multi-angle polarization spectral response function model for the fruit tree canopy. Combine spectral quantitative detection with the evaluation of the spectral image area of the unmanned aerial vehicle.

[0069] Modify the multi-angle reflectance database by the method of cross-validating the reflectance of fruits at different positions inverted from the reflectance of a single fruit, and realize the one-to-one correspondence between the spectral reflectance and the corresponding position of the spectral image. Through spatial segmentation of fruits at different angles and distances and matching of angles and azimuths in the model library. Establish a multi-angle spectral and image data model library to realize multi-angle quantitative detection.

[0070] (3) Azimuth calibration spectral image calibration model:

[0071] The standard board establishes a calibration indoor field at different angles, conducts polarization calibration and spatial orientation calibration of the standard board. The spectrum calibrates a certain point of the image, and the geometrically calibrated image is then calibrated in different orientations through the spectral coherence law, and the spectrum and the image are mutually calibrated and verified. The spectrum synthesizes the image, and spectral and angular reflectance information is extracted from the image.

[0072] The experiment with standard calibration boards in different orientations establishes a multi-angle calibration database, collects experiments on the multi-angle reflectance of fruits and interacts, verifies and optimizes the angular orientation model, and establishes a multi-angle spatial orientation spectral model of fruits. A spectral angular reflectance model is established to achieve a refined quantitative description of the spatial spectral images in different orientations. Specifically:

[0073] ① Establish an angular calibration model for the standard white board

[0074] Since there are differences in the reflectance of the standard white board at different angles during the actual experimental process, which has a certain impact on the detection accuracy, it is necessary to calibrate the reflectance of the standard white board at different angles. The influence brought by the angle of the standard white board is greater than that of the fruit quality. By establishing a functional relationship for the optimal angle of the standard white board, the correlation coefficients at different angles of the standard white board are calculated to realize the prediction of the placement angle of the standard white board under different detection conditions.

[0075] ② Establish a distance calibration model for the standard white board

[0076] The lateral coherence degree of the spectrum is different in different directions. The relevant induced spectrum generated by the coherent superposition of sub-waves during the transmission of the spectrum will change the spectral density. Different-angle polarization calibration is carried out to establish a polarization database at different angles; secondly, the lateral coherence degree of the sample spectrum is different at different distances. The spectrum is corrected using standard white boards at different distances from the sample, and a calibration model of the standard white board at different distances is established to realize the calibration of the sample spectrum at different distances.

[0077] ③ Polarization spectral morphological parameters and non-polarization spectral morphological parameters:

[0078] The complementarity between the polarization and spectral reflectance curves is mainly reflected in that polarization is sensitive to surface microstructures (such as roughness) and observation geometries (incident angle, azimuth angle), and can distinguish surface reflection types (specular reflection and diffuse reflection); while the spectral reflectance is sensitive to material compositions and can identify material compositions.

[0079] Although the names of the polarization spectral morphological parameters and the unpolarized spectral morphological parameters are the same, their meanings are different. The difference stems from the sensitivity of polarization information to the surface structure and anisotropy of the target, while the unpolarized spectrum is more directly related to the material composition. Calculate the Pearson correlation coefficient between the parameters. If the polarization morphological characteristic parameters and the unpolarized morphological characteristic parameters show a strong positive correlation (r > 0.8), it indicates that the composition dominates the reflection characteristics; if the correlation is weak (r < 0.3), the surface structure has a more significant impact, that is, polarization information needs to be introduced, showing a complementary relationship.

[0080] Perform Pearson correlation analysis on the seven morphological characteristic parameters under unpolarized light and the morphological characteristics under 0°, 45°, 90°, and 135° polarization. The results are as follows:

[0081] Table 1 Summary table of correlation analysis results

[0082] Full width at half maximum Peak height Peak area Left shoulder width Right shoulder width Left slope Right slope 0p 0.27 0.85 0.75 -0.01 0.41 -0.18 0.87 45p 0.32 0.33 0.34 0.01 0.41 -0.29 0.57 90p 0.12 0.52 0.45 0.17 0.28 -0.22 0.64 135p 0.22 0.74 0.70 0.05 0.33 -0.41 0.72

[0083] According to the above correlation analysis results, under the three parameters of full width at half maximum, left shoulder width, and left slope, the correlation between the polarization and unpolarized spectral morphological characteristics is less than 0.3, that is, they are complementary.

[0084] As shown in the following table, by selecting and comparing seven spectral morphological parameters in two characteristic bands of 665 nm and 695 nm, there are obvious differences in the characteristic parameters related to the detection of jujube moisture and SSC soluble solids. Obviously, the spectral morphological parameters can improve the accuracy of the quantitative detection model.

[0085] Table 2 Correlation analysis of soluble solids content and jujube spectral morphological characteristics

[0086]

[0087] Table 3 Correlation analysis of jujube moisture content and jujube spectral morphological characteristics

[0088]

[0089] (4) Establish a multi-angle fruit spectral response function model:

[0090] Multi - spectral camera multi - angle polarization outdoor angle calibration, spatial position calibration, geometric calibration and radiometric calibration. Select the characteristic azimuth spatial background spectrum to establish the spectral response function and the angle response function. The multi - angle experiment equivalently improves the spectral resolution and detection accuracy. And when there are large differences in reflectivity at different angles, multi - angle differences in spectrum and light intensity, and the spectral camera that combines spectrum and imaging gives the spatial distribution characteristics of fruit quality. Correlation analysis of the correlation coefficients related to angle, wavelength, polarization, and reflectivity is fitted into an angle function model. Place a standard plate during imaging for radiometric calibration, geometric calibration and ambient light field calibration. Refined quantitative description of ambient light and angle, construct a spatial angle azimuth spectral response function, as Figure 2 shown in Figure 2 - 2 in

[0091] (5) Multi - scale multi - angle and spectral quantitative detection model transfer:

[0092] On the basis of consistent fruit quality, select the spectral reflectivity, angle, polarization, and reflectivity collected under the same conditions, and establish a statistical regression relationship model. Take the spatial azimuth distance, angle, and polarization direction as key parameters, fit the experimental data to the angle, wavelength, reflectivity, and polarization characteristic data to establish a correlation coefficient model, and realize multi - scale multi - angle quantitative detection through the transfer of the quantitative detection model of the multi - angle reflectivity quantitative detection database and the spectral database.

[0093] (6) Spatial grid division of the outdoor area to be measured:

[0094] Select the regional orchard area image through satellite images, obtain the orchard with obvious color differences in the image through differential processing as the research area, and carry out the contrast calibration experiment of the near - ground multi - angle of the unmanned aerial vehicle and the multi - angle reflectivity image on the ground. Use a ground illuminometer and a ground thermometer to obtain the ground temperature and the temperature field distribution experiment of the thermal imager. The thermal imager and the spectrometer are used for spatial classification and zoning calibration. Through the sampling calibration area and the prediction inversion area, reconstruct the typical environmental temperature, light and azimuth calibration model of the orchard. Carry out the prediction and inversion work on the unknown area.

[0095] ① Spatial temperature - light field grid model of the area to be measured:

[0096] Based on the differences in temperature and light reflectivity at different test points, and taking the difference between each point being less than a certain specific value as a constraint condition according to the actual orchard situation, select typical environmental control points to carry out grid processing of the geographical azimuth, temperature and light conditions of the area to be measured. Extract the typical environmental azimuth, temperature and light characteristics of the area through the spatial grid processing of the outdoor orchard. Establish a multi - angle profiling calibration field through the temperature and light characteristics of the grid orchard area, and combine the spectral quantitative detection with the regional evaluation of the unmanned aerial vehicle spectral image.

[0097] ② Spatial temperature - light field grid model of the area to be measured at different time periods:

[0098] As the solar altitude and azimuth angles change at different times, the above spatial grid model will change. The above grid spatial area can be corrected by simulating and calculating the solar altitude and azimuth angles at different times to determine the changing area and angle. For example, about 1 degree in half an hour, and the illumination needs to be corrected at the same time.

[0099] ③Reconstruction of typical environment orientation model

[0100] By simulating and calculating the geographical information coordinates (GPS) of the orchard fruit trees in the determined geographical orientation, the solar altitude angle and azimuth angle at different times of the morning, noon and evening, and reconstructing and reproducing the fruit quality spectrum through typical environmental illumination, the characteristic spectrum of the fruit area such as geographical coordinates and growth orientation is accurately and quantitatively described. A typical environment and fruit orientation calibration spectral model for actual fruit trees is established.

[0101] (7) Experiment on multi-angle polarization reflectivity of fruits near the ground by drone:

[0102] ① UAV polarization azimuth experiment at different altitudes and angles:

[0103] The drone multispectral camera collects data at different heights of 3 meters, 5 meters, and 8 meters. At each height, the drone hovers and the spectral camera horizontally adds a polarizer in four polarization directions (0p, 45p, 90p, 135p) and rotates in 8 directions (0, 45, 90, 135, 180, 225, 270, 315) every 45 degrees to obtain multi-angle spectral images. The longitude and latitude coordinates of Zaoyuan are modified in the header file of each picture, and the longitude and latitude coordinates of the area to be measured are entered, such as E81°31'14"N 40°31'42".

[0104] This embodiment is specifically arranged in the order of the following four polarization directions: 0p (0, 45, 90, 135, 180, 225, 270, 315), 45p (0, 45, 90, 135, 180, 225, 270, 315), 90p (0, 45, 90, 135, 180, 225, 270, 315), 135p (0, 45, 90, 135, 180, 225, 270, 315), one scene has 4*8*6=192 reflection band images, different angles and polarization directions are used as multi-angle polarization reflectivity horizontal coordinates, forming a data matrix in which the angle polarization is arranged in sequence. Envi software synthesizes 192 reflectivity files, establishes a multi-angle reflectivity database of fruits in different directions with classification marks, and establishes an angle spectrum spatial response function for different angle resolutions.

[0105] ② Experiment on collecting physical and chemical indicators of artificially marked fruits and spectroscopic determination of classification marks:

[0106] The spectrometer is used to collect the spectra of in-situ artificially marked fruits, record the fruit shape characteristics such as color and texture, and measure the physical and chemical indexes such as fruit moisture and sugar content. Through the spectral database of fruit characteristics in different directions, a quantitative detection model of fruit spectra in different directions is established.

[0107] ③ Cross-validation model of spectral reflectance and multi-angle reflectance of classified and marked fruits

[0108] The ROI characteristic regions of the multi-spectral and multi-angle images of the unmanned aerial vehicle are selected, and the red and green fruits are classified and marked, such as the reflectance of red dates in green and red according to color. Further azimuth marking and single-quality marking detection are carried out to realize the quantitative characterization of fruits. It provides a reference for quality classification and pricing.

[0109] Example 2. Thematic mapping and quantitative evaluation of fruit regions:

[0110] Unmanned aerial vehicle regional quantitative evaluation: The orchard area is identified for the results of the second and third flowerings, and regional quality classification and zoning quantitative evaluation are carried out. Further, the unmanned aerial vehicle adds the fruit azimuth, multi-angle and spatial spectra to the geographic coordinate information for thematic mapping, providing an important reference for improving the quality and efficiency of the fruit industry. The multi-scale and multi-angle fruit classification and marking quantitative detection of the apricots in the 11th Regiment of the First Division is shown in the flowcharts as Figure 7 shown in Figures 7-1 and 7-2.

[0111] The above are only the preferred embodiments of the embodiments of the present invention, and do not impose any form of limitation on the embodiments of the present invention. Any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the embodiments of the present invention still fall within the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An evaluation method for remotely measuring the fruit quality of fruit tree canopies from multiple angles, characterized in that, The method includes: S10: Establish a far-field spectral response function model for fruits: Equivalent the fruit to a scattering medium to quantitatively describe the quality parameters, calculate the far-field spectra of fruits at different angles, and establish a multi-angle spectral response function model through the variation law of morphological parameters; perform precise angle calibration, select the characteristic angles of fruit reflectivity, and establish a multi-angle reflectivity database; S20: Perform grid processing on the temperature and light of the area to be measured through high-precision satellite remote sensing image data, and classify and partition the temperature and light characteristics of the grid orchard area through ground experimental control points; S30: Establish a multi-angle profiling calibration field based on the temperature and light characteristics of the grid orchard area, and realize multi-scale multi-angle quantitative detection through the transfer of the multi-angle reflectivity quantitative detection database and the spectral database quantitative detection model. Combine spectral quantitative detection with the evaluation of the drone spectral image area; S40: Use a drone multi-spectral camera to obtain the variation law of the reflectivity of fruits in the same area at different angles and polarization directions, and use the morphological parameters of the polarization spectrum to fit the spectral reflectivity curve model and spectral response function at the regional scale to determine the fruit quality.

2. The modeling method according to claim 1, wherein The method further includes: First, perform modeling indoors, and then perform modeling outdoors.

3. The modeling method according to claim 2, wherein When performing modeling outdoors, in step S10, it is necessary to establish an angle spectral response function model and transfer of the multi-angle and spectral quantitative models.

4. The modeling method according to claim 3, wherein The transfer of the multi-angle and spectral quantitative models includes: Taking the spatial azimuth distance and angle as key parameters, fitting the correlation coefficient model of angle, wavelength, and reflectivity data to realize the transfer of the multi-angle reflectivity database model and the spectral database model.

5. The modeling method according to claim 1, characterized in that, In step S10: Calibrate according to the angles of the standard plates in different detection spaces, and establish a fruit angle reflectivity model.

6. The modeling method according to claim 1, wherein In step S20: The method of grid division of the space to be measured is verified by corresponding the field of view angles of the images at different distances of the spectral camera and the field of view angle of the spectrometer.

7. The modeling method according to claim 1, characterized in that, Step S30 is: Correct the multi-angle reflectivity database in the way of cross-verifying the reflectivities of fruits at different positions inversely derived from the reflectivity of a single fruit, realize the one-to-one correspondence between the spectral reflectivity and the corresponding position of the spectral image, and establish a multi-angle spectral and image data model library by performing spatial segmentation on fruits at different angles and distances and matching the angles and azimuths in the model library.

8. The modeling method according to claim 1, characterized in that, In step S40: Establish a multi-angle calibration database through experiments with standard calibration plates in different azimuths, collect cross-verification and optimization of the multi-angle reflectivity experiments of fruits and the angle-azimuth model, establish a multi-angle spatial azimuth spectral model for fruits, and combine the establishment of a spectral angle reflectivity model to realize the refined quantitative description of the spatial spectra of different azimuths.

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