Vegetable crop growth information acquisition method and system based on spectrogram fusion

Abstract

The invention provides a vegetable crop growth information method and system based on spectrogram fusion, and relates to the technical field of crop growth monitoring. The method comprises the following steps: acquiring remote sensing images of vegetable crops at different growth stages in multiple wavebands, correcting the remote sensing images to generate a first image set, acquiring moisture content, chlorophyll content and nitrogen content of the vegetable crops, firstly extracting reflectivity data of near-infrared and short-wave infrared wavebands in the first image set, and generating a normalized differential moisture index; then generating a normalized difference chlorophyll index by using red edge and red light wave band data; and generating a normalized differential nitrogen index at specific bands of 1510 nm and 1680 nm. And establishing a relation curve between the normalized difference indexes of the vegetable crops in different growth stages and the moisture content, the chlorophyll content and the nitrogen content, determining the growth stages of the vegetable crops to be identified, collecting multispectral images of the vegetable crops, generating the normalized difference indexes, and determining the moisture content, the chlorophyll content and the nitrogen content according to the relation curve.

Description

Technical Field

[0001] The present invention relates to the technical field of crop growth monitoring, and in particular to a method and system for acquiring vegetable crop growth information based on spectrum fusion. Background Art

[0002] In modern agriculture, accurate monitoring of crop growth is crucial to improving agricultural production efficiency and crop quality. Traditional crop growth monitoring methods mostly rely on manual sampling and ground measurement, which is not only time-consuming and labor-intensive, but also difficult to fully reflect the health of crops in large areas of farmland. With the gradual popularization of drones and the development of remote sensing technology, hyperspectral imaging provides new possibilities for obtaining crop growth information. However, existing technologies have certain limitations in the comprehensive utilization of multi-band information and data processing, and it is difficult to effectively fuse spectral information from different bands to accurately judge the growth status of crops. Therefore, there is an urgent need for a method that can effectively combine different spectral information to achieve real-time acquisition and accurate analysis of crop growth information.

[0003] In the prior art, publication number CN110647781B discloses a method and device for acquiring crop growth information based on spectral fusion, which acquires a first spectral image of a crop canopy in a target area at a first wavelength, and calculates the difference between the reflectivity of each pixel in the first spectral image and the spectral reflectivity of the crop canopy at the first wavelength. When the difference is less than a preset threshold, the leaf area index of the crops in the target area is acquired according to the reflectivity of the pixel, and the vegetation index of the crops is acquired according to the spectral reflectivity of the crop canopy at a second wavelength. The vegetation index is corrected by the leaf area index, and the chlorophyll content and nitrogen content of the crops are acquired according to the corrected vegetation index.

[0004] The main problems with the above method are: only the spectra of the specific first wavelength and the second wavelength are used to estimate the leaf area index and the vegetation index, and the information of other bands is not fully utilized, which may result in the omission of other valuable spectral information, and the evaluation of plant growth information is not comprehensive and accurate enough; in the above scheme, the leaf area index is estimated based on the difference between the reflectance and the preset threshold. The spectral reflectance of crops will be affected by many factors, such as light intensity, soil moisture and air humidity. The preset fixed threshold cannot adapt to crops under different conditions, which will cause deviations in the results; and it mainly relies on static spectral data to evaluate the growth status of crops, lacks analysis of historical data, and the growth of crops is a dynamic process related to time. Considering only a certain growth period cannot accurately reflect the changing trend of crop growth information.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for acquiring vegetable crop growth information based on spectrum fusion to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: A method for acquiring vegetable crop growth information based on spectral fusion, the specific steps comprising: Step 1: Collect remote sensing images of vegetable crops at different growth stages, generate a first identification image set after correcting the remote sensing images, and simultaneously collect the moisture content, chlorophyll content and nitrogen content of the vegetable crops, and the remote sensing images are multi-band remote sensing images, and the bands include visible light band, near infrared band and short-wave infrared band; Step 2: Select spectral images in the near infrared band and the short-wave infrared band from the first image set, extract spectral reflectance data, and generate a normalized difference moisture index; Step 3: Select the spectral image of the red edge band and the red light band in the visible light band from the first image set, extract the reflectance data of the red edge and red light bands, and generate a normalized difference chlorophyll index; Step 4: Select spectral images of 1510 nm and 1680 nm in the near-infrared band from the first image set, extract spectral reflectance data, and generate a normalized difference nitrogen index; Step 5: Based on the normalized difference water index and water content at the same growth stage, establish the water relationship curve at the growth stage, and similarly establish the chlorophyll relationship curve and the nitrogen content relationship curve; Step 6: Determine the growth stage of the vegetable crops to be identified and collect their multispectral images, process the multispectral images to obtain the normalized difference moisture index, the normalized difference chlorophyll index and the normalized difference nitrogen index, and determine the moisture content, chlorophyll content and nitrogen content of the vegetable crops to be identified based on the relationship curves between the data obtained by the above processing and the corresponding growth stage.

[0008] Furthermore, the visible light band ranges from 490 to 750 nm, the near infrared band ranges from 750 to 900 nm, and the short-wave infrared band ranges from 1400 to 3000 nm.

[0009] Furthermore, the formula for generating the normalized difference moisture index is: ; in, represents the normalized difference moisture index, Represents the reflectivity of vegetable crops in the near-infrared band, Indicates the reflectivity of vegetable crops in the shortwave infrared band.

[0010] Furthermore, the formula for generating the normalized difference chlorophyll index is: ; in, represents the normalized difference chlorophyll index, Indicates the reflectivity of vegetable crops in the red edge band, Indicates the reflectivity of vegetable crops in the red light band; The red edge band range is: 680~750nm; The red light band ranges from 640 to 680 nm.

[0011] Furthermore, the formula for generating the normalized difference nitrogen index is: ; in, represents the normalized difference nitrogen index, Indicates the reflectivity of vegetable crops in the 1510nm band. Indicates the reflectivity of vegetable crops in the 1680nm band.

[0012] Furthermore, the principle for establishing the water relationship curve under this growth stage is: The growth stages include: seedling stage, vegetative growth stage, reproductive growth stage, and mature stage; A plane rectangular coordinate system is established with the normalized difference moisture index as the horizontal coordinate and the moisture content as the vertical coordinate. The normalized difference moisture index and moisture content of vegetable crops at the same growth stage are marked with coordinate points in the coordinate system, and all coordinate points are connected to generate a moisture relationship curve at that stage. According to the method, a moisture relationship curve is generated for each growth stage.

[0013] The present invention also provides a vegetable crop growth information acquisition system based on spectral fusion, the system is used to implement the above-mentioned vegetable crop growth information acquisition method based on spectral fusion, specifically comprising: The data acquisition module is used to collect remote sensing images of vegetable crops at different growth stages, generate a first identification image set after correcting the remote sensing images, and simultaneously collect the moisture content, chlorophyll content and nitrogen content of the vegetable crops, and the remote sensing images are multi-band remote sensing images, and the bands include visible light band, near infrared band and short-wave infrared band; A moisture calculation module is used to select spectral images in the near infrared band and the short-wave infrared band from the first image set, extract spectral reflectance data, and generate a normalized difference moisture index; A chlorophyll calculation module is used to select a spectral image of a red light band in a red edge band and a visible light band from the first image set, extract reflectance data of the red edge and red light bands, and generate a normalized difference chlorophyll index; A nitrogen calculation module is used to select spectral images of 1510nm and 1680nm bands in the near-infrared band from the first image set, extract reflectance data of the spectrum, and generate a normalized difference nitrogen index; The curve integration module is used to establish the water relationship curve under the same growth stage based on the normalized difference water index and water content under the same growth stage, and similarly establish the chlorophyll relationship curve and the nitrogen content relationship curve; The comprehensive judgment module is used to determine the growth stage of the vegetable crops to be identified and collect their multispectral images, process the multispectral images to obtain the normalized difference moisture index, the normalized difference chlorophyll index and the normalized difference nitrogen index, and determine the moisture content, chlorophyll content and nitrogen content of the vegetable crops to be identified based on the relationship curves between the data obtained by the above processing and the corresponding growth stage.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention collects remote sensing images of vegetable crops at different stages in visible light bands, near infrared bands and short-wave infrared bands, generates normalized difference moisture index, normalized difference chlorophyll index and normalized difference nitrogen index respectively according to the spectral images of corresponding bands according to the different sensitivity of water, chlorophyll and nitrogen in vegetable crops in different bands, adopts multi-band fusion analysis, selects the spectral graph of corresponding bands according to the sensitivity of different substances to different bands, avoids the limitation of single band evaluation; generating normalized index can also remove the interference of environmental factors, and provides accurate data for obtaining growth information of vegetable crops. The moisture content, chlorophyll content and nitrogen content of vegetable crops are collected, and a relationship curve between the normalized difference index and the moisture content, chlorophyll content and nitrogen content is established. The relationship curve established by historical data is more scientific, can reflect the relationship between the growth information of crops at different growth stages and remote sensing data, obtains the growth stage and multi-spectral image of the vegetable crops to be identified in real time, determines the growth information of the vegetable crops through the relationship curve, reduces the interference to the crops, and realizes the dynamic monitoring of the growth information of crops at different stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of a method flow chart of an embodiment of the present invention; Figure 2 Schematic diagram of system modules according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0017] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] Example: See also Figure 1 , the present invention provides a technical solution: A method for acquiring vegetable crop growth information based on spectral fusion, the specific steps comprising: Step 1: Collect remote sensing images of vegetable crops at different growth stages, generate a first identification image set after correcting the remote sensing images, and simultaneously collect the moisture content, chlorophyll content and nitrogen content of the vegetable crops, and the remote sensing images are multi-band remote sensing images, and the bands include visible light band, near infrared band and short-wave infrared band; In this embodiment, the visible light band ranges from 490 to 750 nm, the near infrared band ranges from 750 to 900 nm, and the short-wave infrared band ranges from 1400 to 3000 nm.

[0019] After collecting remote sensing images, atmospheric correction and geometric correction are performed on them. The purpose of atmospheric correction is to reduce the impact of the atmosphere on remote sensing images so that the radiation values ​​in the images can more truly reflect the spectral characteristics of vegetable crops. According to the specific image conditions, a suitable model is selected from atmospheric radiation transmission models such as 6S, MODTRAN, and RTTOV. The atmospheric correction calculation is combined with the atmospheric model and meteorological parameters to output the surface radiation brightness. The atmospherically corrected radiation brightness is normalized to a reflectivity value between 0 and 1. The purpose of geometric correction is to correct the geometric distortion caused by factors such as sensor viewing angle, earth curvature and camera position in remote sensing images, so that the image can accurately reflect the position and shape of surface features. When performing geometric correction, a set of control points with known positions that are evenly distributed in the image and cover the entire image area are selected, and the pixel coordinates of the control points are matched with their corresponding geographic coordinates. According to the distribution of control points and the nature of image distortion, a suitable geometric transformation model is selected from linear transformation, quadratic polynomial transformation, high-order polynomial transformation and affine transformation. The transformation parameters are calculated based on the control points through an optimization algorithm to describe the relationship between image coordinates and geographic coordinates. The calculated transformation parameters are used to perform a geometric transformation on the entire image, convert pixel positions into geographic coordinates, and compare the corrected image with other known accurate images to verify the effectiveness of the geometric correction.

[0020] Step 2: Select spectral images in the near infrared band and the short-wave infrared band from the first image set, extract spectral reflectance data, and generate a normalized difference moisture index; In this embodiment, the formula for generating the normalized difference moisture index is: ; in, represents the normalized difference moisture index, represents the reflectivity of the image in the near-infrared band, Represents the reflectivity of the image in the shortwave infrared band.

[0021] The normalized difference moisture index reflects the moisture content in vegetable crops. The absorption of moisture in the shortwave infrared band is strong and the reflectivity is low, while the absorption in the near-infrared band is weak and the reflectivity is high. Therefore, the higher the moisture content, the larger the normalized difference moisture index. The normalized difference moisture index is directly proportional to the moisture content of vegetable crops, directly proportional to the reflectivity of vegetable crops in the near-infrared band, and inversely proportional to the reflectivity of vegetable crops in the shortwave infrared band.

[0022] Step 3: Select the spectral image of the red edge band and the red light band in the visible light band from the first image set, extract the reflectance data of the red edge and red light bands, and generate a normalized difference chlorophyll index; In this embodiment, the formula for generating the normalized difference chlorophyll index is: ; in, represents the normalized difference chlorophyll index, represents the reflectivity of the image in the red edge band, Indicates the reflectivity of the image in the red light band; the red edge band range is: 680~750nm; The red light band ranges from 640 to 680 nm.

[0023] The red light band of 640~680nm is the main band for plants to carry out photosynthesis. In the red light band, the absorption rate of chlorophyll is high and the reflectivity is low. In the red edge band of 680~750nm, the absorption of chlorophyll is significantly weakened and the reflectivity increases. Therefore, the higher the chlorophyll content, the higher the reflectivity of vegetable crops in the red edge band, and the higher the normalized difference chlorophyll index. The normalized difference chlorophyll index is proportional to the chlorophyll content of vegetable crops, proportional to the reflectivity of vegetable crops in the red edge band, and inversely proportional to the reflectivity of vegetable crops in the red light band.

[0024] Step 4: Select spectral images of 1510 nm and 1680 nm in the near-infrared band from the first image set, extract spectral reflectance data, and generate a normalized difference nitrogen index; In this embodiment, the formula for generating the normalized difference nitrogen index is: ; in, represents the normalized difference nitrogen index, Indicates the reflectivity of the image in the 1510nm band, Indicates the reflectivity of the image in the 1680nm band.

[0025] Both 1510nm and 1680nm bands belong to the near-infrared band. In the 1510nm band, nitrogen compounds in vegetable crops, including proteins, amino acids, etc., have special absorption characteristics. The higher the nitrogen content in vegetable crops, the higher the absorption of light in this band and the lower the reflectivity. In the 1680nm band, the reflectivity is mainly affected by water and other organic matter. Therefore, the 1680nm band is used as a control band for nitrogen content. The normalized difference nitrogen index is proportional to the reflectivity of the 1510nm band, inversely proportional to the nitrogen content of vegetable crops, and inversely proportional to the reflectivity of the 1680nm band.

[0026] Step 5: Based on the normalized difference water index and water content at the same growth stage, establish the water relationship curve at the growth stage, and similarly establish the chlorophyll relationship curve and the nitrogen content relationship curve; In this embodiment, the principle for establishing the moisture relationship curve under this growth stage is: The growth stages include: seedling stage, vegetative growth stage, reproductive growth stage, and mature stage; A plane rectangular coordinate system is established with the normalized difference moisture index as the horizontal coordinate and the moisture content as the vertical coordinate, the normalized difference moisture index and moisture content of vegetable crops at the same growth stage are marked with coordinate points in the coordinate system, all coordinate points are connected to generate a moisture relationship curve at the stage, a moisture relationship curve is generated for each growth stage according to the method, a regression equation can be fitted according to the relationship curve to reflect the change of moisture content with the normalized difference moisture index, the normalized difference chlorophyll index and the normalized difference nitrogen index are processed by the same method, and a regression curve reflecting the change of chlorophyll content with the normalized difference chlorophyll index and a regression curve reflecting the change of nitrogen content with the normalized difference nitrogen index are generated.

[0027] Step 6: Determine the growth stage of the vegetable crops to be identified and collect their multispectral images, process the multispectral images to obtain the normalized difference moisture index, the normalized difference chlorophyll index and the normalized difference nitrogen index, and determine the moisture content, chlorophyll content and nitrogen content of the vegetable crops to be identified based on the relationship curves between the data obtained by the above processing and the corresponding growth stage.

[0028] In this embodiment, the normalized difference moisture index, normalized difference chlorophyll index and normalized difference nitrogen index of the vegetable crops to be identified are generated based on the multispectral image, and the three normalized difference indices are substituted into the relationship curves of the corresponding stages to generate the moisture content, chlorophyll content and nitrogen content of the vegetable crops.

[0029] See also Figure 2 The present invention also provides a vegetable crop growth information acquisition system based on spectral fusion, the system is used to implement the above-mentioned vegetable crop growth information acquisition method based on spectral fusion, specifically comprising: The data acquisition module is used to collect remote sensing images of vegetable crops at different growth stages, generate a first identification image set after correcting the remote sensing images, and simultaneously collect the moisture content, chlorophyll content and nitrogen content of the vegetable crops, and the remote sensing images are multi-band remote sensing images, and the bands include visible light band, near infrared band and short-wave infrared band; A moisture calculation module is used to select spectral images in the near infrared band and the short-wave infrared band from the first image set, extract spectral reflectance data, and generate a normalized difference moisture index; A chlorophyll calculation module is used to select a spectral image of a red light band in a red edge band and a visible light band from the first image set, extract reflectance data of the red edge and red light bands, and generate a normalized difference chlorophyll index; A nitrogen calculation module is used to select spectral images of 1510nm and 1680nm bands in the near-infrared band from the first image set, extract reflectance data of the spectrum, and generate a normalized difference nitrogen index; The curve integration module is used to establish the water relationship curve under the same growth stage based on the normalized difference water index and water content under the same growth stage, and similarly establish the chlorophyll relationship curve and the nitrogen content relationship curve; The comprehensive judgment module is used to determine the growth stage of the vegetable crops to be identified and collect their multispectral images, process the multispectral images to obtain the normalized difference moisture index, the normalized difference chlorophyll index and the normalized difference nitrogen index, and determine the moisture content, chlorophyll content and nitrogen content of the vegetable crops to be identified based on the relationship curves between the data obtained by the above processing and the corresponding growth stage.

[0030] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0031] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0032] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0033] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for acquiring vegetable crop growth information based on spectral fusion, characterized in that: The specific steps include: Step 1: Collect remote sensing images of vegetable crops at different growth stages, generate a first identification image set after correcting the remote sensing images, and simultaneously collect the moisture content, chlorophyll content and nitrogen content of the vegetable crops, and the remote sensing images are multi-band remote sensing images, and the bands include visible light band, near infrared band and short-wave infrared band; Step 2: Select spectral images in the near infrared band and the short-wave infrared band from the first image set, extract spectral reflectance data, and generate a normalized difference moisture index; Step 3: Select the spectral image of the red edge band and the red light band in the visible light band from the first image set, extract the reflectance data of the red edge and red light bands, and generate a normalized difference chlorophyll index; Step 4: Select spectral images of 1510 nm and 1680 nm in the near-infrared band from the first image set, extract spectral reflectance data, and generate a normalized difference nitrogen index; Step 5: Based on the normalized difference water index and water content at the same growth stage, establish the water relationship curve at the growth stage, and similarly establish the chlorophyll relationship curve and the nitrogen content relationship curve; Step 6: Determine the growth stage of the vegetable crops to be identified and collect their multispectral images, process the multispectral images to obtain the normalized difference moisture index, the normalized difference chlorophyll index and the normalized difference nitrogen index, and determine the moisture content, chlorophyll content and nitrogen content of the vegetable crops to be identified based on the relationship curves between the data obtained by the above processing and the corresponding growth stage.

2. The method for acquiring vegetable crop growth information based on spectrum fusion according to claim 1, characterized in that: The visible light band ranges from 490 to 750 nm, the near infrared band ranges from 750 to 900 nm, and the short-wave infrared band ranges from 1400 to 3000 nm.

3. The method for acquiring vegetable crop growth information based on spectrum fusion according to claim 1, characterized in that: The formula for generating the normalized difference moisture index in step 2 is: ； in, represents the normalized difference moisture index, Represents the reflectivity of vegetable crops in the near-infrared band, Indicates the reflectivity of vegetable crops in the shortwave infrared band.

4. The method for acquiring vegetable crop growth information based on spectrum fusion according to claim 1, characterized in that: The formula for generating the normalized difference chlorophyll index in step 3 is: ； in, represents the normalized difference chlorophyll index, Indicates the reflectivity of vegetable crops in the red edge band, Indicates the reflectivity of vegetable crops in the red light band; The red edge band range is: 680~750nm; The red light band ranges from 640 to 680 nm.

5. The method for acquiring vegetable crop growth information based on spectrum fusion according to claim 1, characterized in that: The formula for generating the normalized difference nitrogen index in step 4 is: ； in, represents the normalized difference nitrogen index, Indicates the reflectivity of vegetable crops in the 1510nm band. Indicates the reflectivity of vegetable crops in the 1680nm band.

6. The method for acquiring vegetable crop growth information based on spectrum fusion according to claim 1, characterized in that: The principle for establishing the moisture relationship curve at this growth stage in step 5 is: The growth stages include: seedling stage, vegetative growth stage, reproductive growth stage, and mature stage; A plane rectangular coordinate system is established with the normalized difference moisture index as the horizontal coordinate and the moisture content as the vertical coordinate. The normalized difference moisture index and moisture content of vegetable crops at the same growth stage are marked with coordinate points in the coordinate system, and all coordinate points are connected to generate a moisture relationship curve at that stage. According to the method, a moisture relationship curve is generated for each growth stage.

7. A vegetable crop growth information acquisition system based on spectrum fusion, characterized in that: The system is used to implement the method for acquiring vegetable crop growth information based on spectral fusion according to any one of claims 1 to 6, specifically comprising: The data acquisition module is used to collect remote sensing images of vegetable crops at different growth stages, generate a first identification image set after correcting the remote sensing images, and simultaneously collect the moisture content, chlorophyll content and nitrogen content of the vegetable crops, and the remote sensing images are multi-band remote sensing images, and the bands include visible light band, near infrared band and short-wave infrared band; A moisture calculation module is used to select spectral images in the near infrared band and the short-wave infrared band from the first image set, extract spectral reflectance data, and generate a normalized difference moisture index; A chlorophyll calculation module is used to select a spectral image of a red light band in a red edge band and a visible light band from the first image set, extract reflectance data of the red edge and red light bands, and generate a normalized difference chlorophyll index; A nitrogen calculation module is used to select spectral images of 1510nm and 1680nm bands in the near-infrared band from the first image set, extract reflectance data of the spectrum, and generate a normalized difference nitrogen index; The curve integration module is used to establish the water relationship curve under the same growth stage based on the normalized difference water index and water content under the same growth stage, and similarly establish the chlorophyll relationship curve and the nitrogen content relationship curve; The comprehensive judgment module is used to determine the growth stage of the vegetable crops to be identified and collect their multispectral images, process the multispectral images to obtain the normalized difference moisture index, the normalized difference chlorophyll index and the normalized difference nitrogen index, and determine the moisture content, chlorophyll content and nitrogen content of the vegetable crops to be identified based on the relationship curves between the data obtained by the above processing and the corresponding growth stage.

Images