CN113008890A - Cotton leaf nitrogen content monitoring method and system based on hyperspectrum of unmanned aerial vehicle - Google Patents
Cotton leaf nitrogen content monitoring method and system based on hyperspectrum of unmanned aerial vehicle Download PDFInfo
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- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 title claims abstract description 302
- 229910052757 nitrogen Inorganic materials 0.000 title claims abstract description 151
- 238000012544 monitoring process Methods 0.000 title claims abstract description 126
- 229920000742 Cotton Polymers 0.000 title claims abstract description 79
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- 238000000354 decomposition reaction Methods 0.000 claims abstract description 17
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- 238000009826 distribution Methods 0.000 claims description 33
- 239000002689 soil Substances 0.000 claims description 27
- 238000012545 processing Methods 0.000 claims description 22
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 20
- 238000002310 reflectometry Methods 0.000 claims description 19
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- 238000011161 development Methods 0.000 description 2
- 239000000618 nitrogen fertilizer Substances 0.000 description 2
- 241000196324 Embryophyta Species 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 241000209140 Triticum Species 0.000 description 1
- 235000021307 Triticum Nutrition 0.000 description 1
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Abstract
The invention discloses a cotton leaf nitrogen content monitoring method and system based on hyperspectrum of an unmanned aerial vehicle, and relates to the technical field of crop biochemical component monitoring, wherein the technical scheme is as follows: classifying the sensitive wavelength bands to obtain a peak sensitive set and a trough sensitive set; acquiring hyperspectral image information of a target area and corresponding nitrogen content of cotton leaves; decomposing the mixed pixels by a mixed spectrum decomposition method to obtain a multi-hyperspectral data set; intercepting a first multi-hyperspectral feature discrete set and a second multi-hyperspectral feature discrete set from a hyperspectral data set; obtaining a first nitrogen content monitoring model and a second nitrogen content monitoring model through a linear regression training model; and respectively inputting the estimated wavelength and the multi-hyperspectral characteristic estimation group into a first nitrogen content monitoring model and a second nitrogen content monitoring model for matching, and preferentially selecting the monitoring models according to matching results to estimate and monitor the nitrogen content of the target cotton leaf. The invention can effectively improve the accuracy of the monitoring result.
Description
Technical Field
The invention relates to the technical field of crop biochemical component monitoring, in particular to a cotton leaf nitrogen content monitoring method and system based on hyperspectral of an unmanned aerial vehicle.
Background
The nitrogen is a main nutrient element required by most crops, for example, the crops such as cotton, wheat, rice and the like, the nitrogen content of the crops directly influences the growth and development of the crops and the formation of the yield and quality, the nitrogen content of leaves of the crops is detected in real time, quickly and accurately, the nitrogen fertilizer application is guided scientifically and reasonably, and the nitrogen fertilizer application method has important practical significance and application value for reducing environmental pollution caused by excessive nitrogen application and improving the yield and quality of the crops. In recent years, with the rapid development of hyperspectral technology, the application of hyperspectral nondestructive detection technology to nondestructive monitoring and estimation of crop biochemical components including nitrogen has become an important method for researching crop growth and nutrition diagnosis in fields at present.
At present, there are two main methods for detecting the nitrogen content of crops by a hyperspectral technology. First, various spectral vegetation indices generated using a combination of reflectivities of sensitive wavelengths; and secondly, a response model is directly established by applying the spectral characteristic variables and the crop nitrogen content, so that the spectral nondestructive estimation of the nitrogen content is realized. However, the hyperspectral image is not only single hyperspectral data of the crop leaves, but also hyperspectral data of various other ground objects such as soil, moisture, weeds and the like during imaging, and the method for detecting the nitrogen content of the crop through the hyperspectral technology takes the influence of the other ground objects on the monitoring of the nitrogen content of the crop into consideration, so that a larger error exists in the result of the monitoring of the nitrogen content of the actual crop.
Therefore, how to research and design an accurate and reasonable cotton leaf nitrogen content monitoring method and system based on hyperspectrum of an unmanned aerial vehicle is a problem which is urgently needed to be solved at present.
Disclosure of Invention
In order to solve the problem of low accuracy of crop content monitoring in the existing hyperspectral technology, the invention aims to provide a cotton leaf nitrogen content monitoring method and system based on hyperspectral of an unmanned aerial vehicle.
The technical purpose of the invention is realized by the following technical scheme:
in a first aspect, a cotton leaf nitrogen content monitoring method based on hyperspectrum of an unmanned aerial vehicle is provided, and the method comprises the following steps:
s101: acquiring high spectral reflectance data of nitrogen of cotton leaves, intercepting sensitive wavelength bands from the high spectral reflectance data, and classifying the sensitive wavelength bands according to reflection peak characteristic quantity and absorption valley characteristic quantity to obtain a peak sensitive set and a valley sensitive set;
s102: acquiring hyperspectral image information of a target area and corresponding nitrogen content of cotton leaves;
s103: the method comprises the steps of dividing hyperspectral image information into a plurality of mixed pixels, decomposing the mixed pixels by a mixed spectrum decomposition method, and performing classification and integration on decomposition processing results to obtain a multi-hyperspectral data group consisting of soil hyperspectral data, moisture hyperspectral data and blade hyperspectral data;
s104: intercepting a first multi-hyperspectral feature group with corresponding wavelength from a multi-hyperspectral data group according to a peak sensitive set, wherein the first multi-hyperspectral feature group with multiple wavelengths forms a first multi-hyperspectral feature discrete set of the same mixed pixel; intercepting a second multi-hyperspectral feature group with corresponding wavelength from the multi-hyperspectral data group according to the valley sensitive set, wherein the second multi-hyperspectral feature group with multiple wavelengths forms a second multi-hyperspectral feature discrete set of the same mixed pixel;
s105: inputting a first multi-hyperspectral characteristic discrete set of a plurality of mixed pixels and the nitrogen content of cotton leaves into a linear regression training model for training to obtain a first nitrogen content monitoring model; inputting the second multi-hyperspectral characteristic discrete set of the multiple mixed pixels and the nitrogen content of the cotton leaf into a linear regression training model for training to obtain a second nitrogen content monitoring model;
s106: acquiring hyperspectral data information of a target cotton leaf, and randomly selecting an estimation wavelength and a hyperspectral feature estimation group under the corresponding wavelength from the hyperspectral data information;
s107: and respectively inputting the estimated wavelength and the multi-hyperspectral characteristic estimation group into a first nitrogen content monitoring model and a second nitrogen content monitoring model for matching, and preferentially selecting the monitoring models according to matching results to estimate and monitor the nitrogen content of the target cotton leaf to obtain estimated and monitored results.
Further, the classification processing process of the peak sensitive set and the valley sensitive set specifically includes:
after the high spectral reflectivity data are subjected to smoothing processing, establishing a high spectral reflectivity curve;
solving the slope of the hyperspectral reflectivity curve, and performing calculus processing on the slope in a continuous wave band in the hyperspectral reflectivity curve to obtain a wave band slope;
screening the nth wave band slope which is simultaneously larger than the (N-1) th wave band slope and the (N + 1) th wave band slope, and then taking the nth wave band as a first sensitive wavelength band, wherein a plurality of first sensitive wavelength bands form a wave peak sensitive set;
and after the Nth wave band slope is simultaneously smaller than the (N-1) th wave band slope and the (N + 1) th wave band slope, screening, taking the Nth wave band as a second sensitive wavelength band, and forming a wave trough sensitive set by a plurality of second sensitive wavelength bands.
Further, the specific process of decomposing and processing the hyperspectral image information to obtain the hyperspectral data sets is as follows:
respectively extracting hyperspectral data in each mixed pixel in the hyperspectral image information to obtain hyperspectral distribution data;
dividing the hyperspectral distribution data into soil distribution data, water distribution data and leaf distribution data according to the categories of soil, water and leaf ground objects;
the soil distribution data, the water distribution data and the leaf distribution data are integrated into a multi-hyperspectral data group which is continuously distributed and simultaneously contains the soil distribution data, the water distribution data and the leaf distribution data under a single wavelength.
Further, the establishing process of the first nitrogen content monitoring model and the second nitrogen content monitoring model specifically comprises the following steps:
performing regression training on a hyperspectral feature group and cotton leaf nitrogen content under different wavelengths in a hyperspectral feature discrete set through a linear regression training model to obtain a quaternary linear regression training model;
the quaternary linear regression training model takes soil, water and leaf hyperspectral data as independent variables and takes nitrogen content as dependent variables;
and performing optimization correction training on the quaternary linear regression training models under different mixed pixels to obtain a corresponding first nitrogen content monitoring model or a corresponding second nitrogen content monitoring model.
Further, the matching process of the first nitrogen content monitoring model and the second nitrogen content monitoring model specifically comprises the following steps:
randomly determining an estimated wavelength, and selecting a multi-hyperspectral characteristic estimation group with corresponding wavelength from hyperspectral data information of a target cotton leaf according to the estimated wavelength;
respectively inputting the multi-hyperspectral characteristic estimation groups into a first nitrogen content monitoring model and a second nitrogen content monitoring model for matching degree calculation to respectively obtain a first matching degree and a second matching degree;
and carrying out nitrogen content estimation monitoring on the target cotton leaf by using the monitoring model corresponding to the maximum value in the first matching degree and the second matching degree to obtain a nitrogen content monitoring result in the target cotton leaf.
Further, the calculation process of the first matching degree and the second matching degree specifically includes:
calculating the correlation between the soil, the water and the leaf hyperspectral features in the hyperspectral feature estimation group respectively to obtain three correlation coefficients;
calculating a first uniform correlation of the multi-hyperspectral feature estimation group according to the three correlation coefficients;
and taking the ratio of the first uniform correlation of the multi-hyperspectral feature estimation group to the second uniform correlation calculated by the monitoring model as the matching degree.
Further, the hyperspectral data is collected at low altitude within the height of 10-50mm by an unmanned aerial vehicle loaded with MVD, and the collection wave band range is 400-1200 nm.
In a second aspect, a cotton leaf nitrogen content monitoring system based on unmanned aerial vehicle hyperspectrum is provided, including:
the data classification module is used for acquiring hyperspectral reflectivity data of cotton leaf nitrogen, intercepting a sensitive wavelength band from the hyperspectral reflectivity data, and classifying the sensitive wavelength band according to reflection peak characteristic quantity and absorption valley characteristic quantity to obtain a wave crest sensitive set and a wave trough sensitive set;
the data acquisition module is used for acquiring hyperspectral image information of a target area and corresponding nitrogen content of cotton leaves;
the data decomposition module is used for dividing the hyperspectral image information into a plurality of mixed pixels, decomposing the mixed pixels by a mixed spectrum decomposition method, and classifying and integrating decomposition processing results to obtain a multi-hyperspectral data group consisting of soil hyperspectral data, moisture hyperspectral data and blade hyperspectral data;
the data intercepting module is used for intercepting a first multi-hyperspectral feature group with corresponding wavelength from a multi-hyperspectral data group according to a wave crest sensitive set, and the first multi-hyperspectral feature group with multiple wavelengths forms a first multi-hyperspectral feature discrete set of the same mixed pixel; intercepting a second multi-hyperspectral feature group with corresponding wavelength from the multi-hyperspectral data group according to the valley sensitive set, wherein the second multi-hyperspectral feature group with multiple wavelengths forms a second multi-hyperspectral feature discrete set of the same mixed pixel;
the model building module is used for inputting the first multi-hyperspectral characteristic discrete set of the multiple mixed pixels and the nitrogen content of the cotton leaf into a linear regression training model for training to obtain a first nitrogen content monitoring model; inputting the second multi-hyperspectral characteristic discrete set of the multiple mixed pixels and the nitrogen content of the cotton leaf into a linear regression training model for training to obtain a second nitrogen content monitoring model;
the data acquisition module is used for acquiring hyperspectral data information of a target cotton leaf and randomly selecting an estimation wavelength and a hyperspectral feature estimation group under the corresponding wavelength from the hyperspectral data information;
and the monitoring module is used for inputting the estimated wavelength and the multi-hyperspectral characteristic estimation group into the first nitrogen content monitoring model and the second nitrogen content monitoring model respectively for matching, and preferentially selecting the monitoring models according to matching results to estimate and monitor the nitrogen content of the target cotton leaves so as to obtain estimated and monitored results.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, the wave crests and the wave troughs of the sensitive wavelength section of the cotton leaf nitrogen are divided, corresponding hyperspectral feature groups are intercepted, a first nitrogen content monitoring model and a second nitrogen content monitoring model are established, the cotton leaf nitrogen content monitoring is carried out independently through the first nitrogen content monitoring model and the second nitrogen content monitoring model which are independent of each other, the whole error range of monitoring results can be effectively reduced, and the accuracy of the cotton leaf nitrogen content monitoring is improved;
2. the first nitrogen content monitoring model or the second nitrogen content monitoring model established by comprehensively considering the incidence relation of soil, water, leaf series ground objects and the nitrogen content of the cotton leaves can better accord with the real situation of the nitrogen content of the cotton leaves;
3. and a proper first nitrogen content monitoring model or a proper second nitrogen content monitoring model is selected for monitoring through calculating the matching degree, so that accurate positioning monitoring is realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a flow chart in an embodiment of the invention;
fig. 2 is a system architecture diagram in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1: a cotton leaf nitrogen content monitoring method based on hyperspectrum of an unmanned aerial vehicle is specifically realized by the following steps as shown in figure 1.
S101: acquiring high spectral reflectance data of nitrogen of cotton leaves, intercepting sensitive wavelength bands from the high spectral reflectance data, and classifying the sensitive wavelength bands according to reflection peak characteristic quantity and absorption valley characteristic quantity to obtain a peak sensitive set and a valley sensitive set.
The classification processing process of the wave crest sensitive set and the wave trough sensitive set specifically comprises the following steps: after the high spectral reflectivity data are subjected to smoothing processing, establishing a high spectral reflectivity curve; solving the slope of the hyperspectral reflectivity curve, and performing calculus processing on the slope in a continuous wave band in the hyperspectral reflectivity curve to obtain a wave band slope; screening the nth wave band slope which is simultaneously larger than the (N-1) th wave band slope and the (N + 1) th wave band slope, and then taking the nth wave band as a first sensitive wavelength band, wherein a plurality of first sensitive wavelength bands form a wave peak sensitive set; and after the Nth wave band slope is simultaneously smaller than the (N-1) th wave band slope and the (N + 1) th wave band slope, screening, taking the Nth wave band as a second sensitive wavelength band, and forming a wave trough sensitive set by a plurality of second sensitive wavelength bands.
S102: and acquiring hyperspectral image information of the target area and corresponding nitrogen content of the cotton leaf.
S103: the hyperspectral image information is divided into a plurality of mixed pixels, the mixed pixels are decomposed through a mixed spectrum decomposition method, and a multi-hyperspectral data group consisting of soil hyperspectral data, moisture hyperspectral data and blade hyperspectral data is obtained after decomposition processing results are classified and integrated.
The specific process of decomposing and processing the hyperspectral image information to obtain the hyperspectral data sets is as follows: respectively extracting hyperspectral data in each mixed pixel in the hyperspectral image information to obtain hyperspectral distribution data; dividing the hyperspectral distribution data into soil distribution data, water distribution data and leaf distribution data according to the categories of soil, water and leaf ground objects; the soil distribution data, the water distribution data and the leaf distribution data are integrated into a multi-hyperspectral data group which is continuously distributed and simultaneously contains the soil distribution data, the water distribution data and the leaf distribution data under a single wavelength.
S104: intercepting a first multi-hyperspectral feature group with corresponding wavelength from a multi-hyperspectral data group according to a peak sensitive set, wherein the first multi-hyperspectral feature group with multiple wavelengths forms a first multi-hyperspectral feature discrete set of the same mixed pixel; and intercepting a second multi-hyperspectral feature group of corresponding wavelengths from the multi-hyperspectral data group according to the valley sensitive set, wherein the second multi-hyperspectral feature group of the multiple wavelengths forms a second multi-hyperspectral feature discrete set of the same mixed pixel.
S105: inputting a first multi-hyperspectral characteristic discrete set of a plurality of mixed pixels and the nitrogen content of cotton leaves into a linear regression training model for training to obtain a first nitrogen content monitoring model; and inputting the second multi-hyperspectral characteristic discrete set of the multiple mixed pixels and the nitrogen content of the cotton leaf into a linear regression training model for training to obtain a second nitrogen content monitoring model.
The establishing process of the first nitrogen content monitoring model and the second nitrogen content monitoring model specifically comprises the following steps: performing regression training on a hyperspectral feature group and cotton leaf nitrogen content under different wavelengths in a hyperspectral feature discrete set through a linear regression training model to obtain a quaternary linear regression training model; the quaternary linear regression training model takes soil, water and leaf hyperspectral data as independent variables and takes nitrogen content as dependent variables; and performing optimization correction training on the quaternary linear regression training models under different mixed pixels to obtain a corresponding first nitrogen content monitoring model or a corresponding second nitrogen content monitoring model.
S106: the hyperspectral data information of the target cotton leaf is obtained, and an estimation wavelength and a hyperspectral feature estimation group under the corresponding wavelength are randomly selected from the hyperspectral data information.
S107: and respectively inputting the estimated wavelength and the multi-hyperspectral characteristic estimation group into a first nitrogen content monitoring model and a second nitrogen content monitoring model for matching, and preferentially selecting the monitoring models according to matching results to estimate and monitor the nitrogen content of the target cotton leaf to obtain estimated and monitored results.
The matching process of the first nitrogen content monitoring model and the second nitrogen content monitoring model specifically comprises the following steps: randomly determining an estimated wavelength, and selecting a multi-hyperspectral characteristic estimation group with corresponding wavelength from hyperspectral data information of a target cotton leaf according to the estimated wavelength; respectively inputting the multi-hyperspectral characteristic estimation groups into a first nitrogen content monitoring model and a second nitrogen content monitoring model for matching degree calculation to respectively obtain a first matching degree and a second matching degree; and carrying out nitrogen content estimation monitoring on the target cotton leaf by using the monitoring model corresponding to the maximum value in the first matching degree and the second matching degree to obtain a nitrogen content monitoring result in the target cotton leaf.
In addition, the calculation process of the first matching degree and the second matching degree specifically includes: calculating the correlation between the soil, the water and the leaf hyperspectral features in the hyperspectral feature estimation group respectively to obtain three correlation coefficients; calculating a first uniform correlation of the multi-hyperspectral feature estimation group according to the three correlation coefficients; and taking the ratio of the first uniform correlation of the multi-hyperspectral feature estimation group to the second uniform correlation calculated by the monitoring model as the matching degree.
In the embodiment, the hyperspectral data are collected at low altitude within the height of 10-50mm by the unmanned aerial vehicle loaded with the MVD, and the collection wave band range is 400-1200 nm.
Example 2: cotton blade nitrogen content monitoring system based on unmanned aerial vehicle hyperspectral as shown in figure 2, including data classification module, data acquisition module, data decomposition module, data interception module, model construction module, data acquisition module, monitoring module.
And the data classification module is used for acquiring the hyperspectral reflectivity data of the cotton leaf nitrogen, intercepting the sensitive wavelength band from the hyperspectral reflectivity data, and classifying the sensitive wavelength band according to the reflection peak characteristic quantity and the absorption valley characteristic quantity to obtain a peak sensitive set and a valley sensitive set.
And the data acquisition module is used for acquiring hyperspectral image information of the target area and corresponding nitrogen content of the cotton leaves.
And the data decomposition module is used for dividing the hyperspectral image information into a plurality of mixed pixels, decomposing the mixed pixels by a mixed spectrum decomposition method, and obtaining a multi-hyperspectral data group consisting of soil hyperspectral data, moisture hyperspectral data and blade hyperspectral data after classifying and integrating decomposition processing results.
The data intercepting module is used for intercepting a first multi-hyperspectral feature group with corresponding wavelength from a multi-hyperspectral data group according to a wave crest sensitive set, and the first multi-hyperspectral feature group with multiple wavelengths forms a first multi-hyperspectral feature discrete set of the same mixed pixel; and intercepting a second multi-hyperspectral feature group of corresponding wavelengths from the multi-hyperspectral data group according to the valley sensitive set, wherein the second multi-hyperspectral feature group of the multiple wavelengths forms a second multi-hyperspectral feature discrete set of the same mixed pixel.
The model building module is used for inputting the first multi-hyperspectral characteristic discrete set of the multiple mixed pixels and the nitrogen content of the cotton leaf into a linear regression training model for training to obtain a first nitrogen content monitoring model; and inputting the second multi-hyperspectral characteristic discrete set of the multiple mixed pixels and the nitrogen content of the cotton leaf into a linear regression training model for training to obtain a second nitrogen content monitoring model.
And the data acquisition module is used for acquiring hyperspectral data information of the target cotton leaf and randomly selecting an estimation wavelength and a hyperspectral characteristic estimation group under the corresponding wavelength from the hyperspectral data information.
And the monitoring module is used for inputting the estimated wavelength and the multi-hyperspectral characteristic estimation group into the first nitrogen content monitoring model and the second nitrogen content monitoring model respectively for matching, and preferentially selecting the monitoring models according to matching results to estimate and monitor the nitrogen content of the target cotton leaves so as to obtain estimated and monitored results.
The working principle is as follows: according to the method, the wave crests and the wave troughs of the sensitive wavelength section of the cotton leaf nitrogen are divided, corresponding hyperspectral feature groups are intercepted, a first nitrogen content monitoring model and a second nitrogen content monitoring model are established, the cotton leaf nitrogen content monitoring is carried out independently through the first nitrogen content monitoring model and the second nitrogen content monitoring model which are independent of each other, the whole error range of monitoring results can be effectively reduced, and the accuracy of the cotton leaf nitrogen content monitoring is improved; in addition, the first nitrogen content monitoring model or the second nitrogen content monitoring model established by comprehensively considering the incidence relation between soil, water, leaf series ground objects and the nitrogen content of the cotton leaves can better accord with the real situation of the nitrogen content of the cotton leaves; in addition, a proper first nitrogen content monitoring model or a proper second nitrogen content monitoring model is selected for monitoring through calculating the matching degree, and accurate positioning monitoring is achieved.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A cotton leaf nitrogen content monitoring method based on unmanned aerial vehicle hyperspectrum is characterized by comprising the following steps:
s101: acquiring high spectral reflectance data of nitrogen of cotton leaves, intercepting sensitive wavelength bands from the high spectral reflectance data, and classifying the sensitive wavelength bands according to reflection peak characteristic quantity and absorption valley characteristic quantity to obtain a peak sensitive set and a valley sensitive set;
s102: acquiring hyperspectral image information of a target area and corresponding nitrogen content of cotton leaves;
s103: the method comprises the steps of dividing hyperspectral image information into a plurality of mixed pixels, decomposing the mixed pixels by a mixed spectrum decomposition method, and performing classification and integration on decomposition processing results to obtain a multi-hyperspectral data group consisting of soil hyperspectral data, moisture hyperspectral data and blade hyperspectral data;
s104: intercepting a first multi-hyperspectral feature group with corresponding wavelength from a multi-hyperspectral data group according to a peak sensitive set, wherein the first multi-hyperspectral feature group with multiple wavelengths forms a first multi-hyperspectral feature discrete set of the same mixed pixel; intercepting a second multi-hyperspectral feature group with corresponding wavelength from the multi-hyperspectral data group according to the valley sensitive set, wherein the second multi-hyperspectral feature group with multiple wavelengths forms a second multi-hyperspectral feature discrete set of the same mixed pixel;
s105: inputting a first multi-hyperspectral characteristic discrete set of a plurality of mixed pixels and the nitrogen content of cotton leaves into a linear regression training model for training to obtain a first nitrogen content monitoring model; inputting the second multi-hyperspectral characteristic discrete set of the multiple mixed pixels and the nitrogen content of the cotton leaf into a linear regression training model for training to obtain a second nitrogen content monitoring model;
s106: acquiring hyperspectral data information of a target cotton leaf, and randomly selecting an estimation wavelength and a hyperspectral feature estimation group under the corresponding wavelength from the hyperspectral data information;
s107: and respectively inputting the estimated wavelength and the multi-hyperspectral characteristic estimation group into a first nitrogen content monitoring model and a second nitrogen content monitoring model for matching, and preferentially selecting the monitoring models according to matching results to estimate and monitor the nitrogen content of the target cotton leaf to obtain estimated and monitored results.
2. The cotton blade nitrogen content monitoring method based on unmanned aerial vehicle hyperspectrum according to claim 1, wherein the classification processing process of the wave crest sensitive set and the wave trough sensitive set is specifically as follows:
after the high spectral reflectivity data are subjected to smoothing processing, establishing a high spectral reflectivity curve;
solving the slope of the hyperspectral reflectivity curve, and performing calculus processing on the slope in a continuous wave band in the hyperspectral reflectivity curve to obtain a wave band slope;
screening the nth wave band slope which is simultaneously larger than the (N-1) th wave band slope and the (N + 1) th wave band slope, and then taking the nth wave band as a first sensitive wavelength band, wherein a plurality of first sensitive wavelength bands form a wave peak sensitive set;
and after the Nth wave band slope is simultaneously smaller than the (N-1) th wave band slope and the (N + 1) th wave band slope, screening, taking the Nth wave band as a second sensitive wavelength band, and forming a wave trough sensitive set by a plurality of second sensitive wavelength bands.
3. The cotton blade nitrogen content monitoring method based on unmanned aerial vehicle hyperspectrum as claimed in claim 1, wherein the specific process of decomposing and processing hyperspectral image information to obtain a hyperspectral data set is as follows:
respectively extracting hyperspectral data in each mixed pixel in the hyperspectral image information to obtain hyperspectral distribution data;
dividing the hyperspectral distribution data into soil distribution data, water distribution data and leaf distribution data according to the categories of soil, water and leaf ground objects;
the soil distribution data, the water distribution data and the leaf distribution data are integrated into a multi-hyperspectral data group which is continuously distributed and simultaneously contains the soil distribution data, the water distribution data and the leaf distribution data under a single wavelength.
4. The cotton blade nitrogen content monitoring method based on unmanned aerial vehicle hyperspectrum as claimed in claim 1, wherein the establishing process of the first nitrogen content monitoring model and the second nitrogen content monitoring model is specifically as follows:
performing regression training on a hyperspectral feature group and cotton leaf nitrogen content under different wavelengths in a hyperspectral feature discrete set through a linear regression training model to obtain a quaternary linear regression training model;
the quaternary linear regression training model takes soil, water and leaf hyperspectral data as independent variables and takes nitrogen content as dependent variables;
and performing optimization correction training on the quaternary linear regression training models under different mixed pixels to obtain a corresponding first nitrogen content monitoring model or a corresponding second nitrogen content monitoring model.
5. The cotton blade nitrogen content monitoring method based on unmanned aerial vehicle hyperspectrum as claimed in claim 1, wherein the matching process of the first nitrogen content monitoring model and the second nitrogen content monitoring model specifically comprises:
randomly determining an estimated wavelength, and selecting a multi-hyperspectral characteristic estimation group with corresponding wavelength from hyperspectral data information of a target cotton leaf according to the estimated wavelength;
respectively inputting the multi-hyperspectral characteristic estimation groups into a first nitrogen content monitoring model and a second nitrogen content monitoring model for matching degree calculation to respectively obtain a first matching degree and a second matching degree;
and carrying out nitrogen content estimation monitoring on the target cotton leaf by using the monitoring model corresponding to the maximum value in the first matching degree and the second matching degree to obtain a nitrogen content monitoring result in the target cotton leaf.
6. The cotton blade nitrogen content monitoring method based on unmanned aerial vehicle hyperspectral measurement, according to claim 5, is characterized in that the calculation process of the first matching degree and the second matching degree is specifically as follows:
calculating the correlation between the soil, the water and the leaf hyperspectral features in the hyperspectral feature estimation group respectively to obtain three correlation coefficients;
calculating a first uniform correlation of the multi-hyperspectral feature estimation group according to the three correlation coefficients;
and taking the ratio of the first uniform correlation of the multi-hyperspectral feature estimation group to the second uniform correlation calculated by the monitoring model as the matching degree.
7. The method for monitoring the nitrogen content of the cotton leaves based on the hyperspectral representation of the unmanned aerial vehicle as claimed in claim 1, wherein the hyperspectral data is collected at low altitude by the unmanned aerial vehicle loaded with MVD within the height of 10-50mm, and the collection wave band range is 400-1200 nm.
8. Cotton leaf nitrogen content monitoring system based on unmanned aerial vehicle hyperspectral, characterized by includes:
the data classification module is used for acquiring hyperspectral reflectivity data of cotton leaf nitrogen, intercepting a sensitive wavelength band from the hyperspectral reflectivity data, and classifying the sensitive wavelength band according to reflection peak characteristic quantity and absorption valley characteristic quantity to obtain a wave crest sensitive set and a wave trough sensitive set;
the data acquisition module is used for acquiring hyperspectral image information of a target area and corresponding nitrogen content of cotton leaves;
the data decomposition module is used for dividing the hyperspectral image information into a plurality of mixed pixels, decomposing the mixed pixels by a mixed spectrum decomposition method, and classifying and integrating decomposition processing results to obtain a multi-hyperspectral data group consisting of soil hyperspectral data, moisture hyperspectral data and blade hyperspectral data;
the data intercepting module is used for intercepting a first multi-hyperspectral feature group with corresponding wavelength from a multi-hyperspectral data group according to a wave crest sensitive set, and the first multi-hyperspectral feature group with multiple wavelengths forms a first multi-hyperspectral feature discrete set of the same mixed pixel; intercepting a second multi-hyperspectral feature group with corresponding wavelength from the multi-hyperspectral data group according to the valley sensitive set, wherein the second multi-hyperspectral feature group with multiple wavelengths forms a second multi-hyperspectral feature discrete set of the same mixed pixel;
the model building module is used for inputting the first multi-hyperspectral characteristic discrete set of the multiple mixed pixels and the nitrogen content of the cotton leaf into a linear regression training model for training to obtain a first nitrogen content monitoring model; inputting the second multi-hyperspectral characteristic discrete set of the multiple mixed pixels and the nitrogen content of the cotton leaf into a linear regression training model for training to obtain a second nitrogen content monitoring model;
the data acquisition module is used for acquiring hyperspectral data information of a target cotton leaf and randomly selecting an estimation wavelength and a hyperspectral feature estimation group under the corresponding wavelength from the hyperspectral data information;
and the monitoring module is used for inputting the estimated wavelength and the multi-hyperspectral characteristic estimation group into the first nitrogen content monitoring model and the second nitrogen content monitoring model respectively for matching, and preferentially selecting the monitoring models according to matching results to estimate and monitor the nitrogen content of the target cotton leaves so as to obtain estimated and monitored results.
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