CN115931774A - Method, storage medium and equipment for monitoring crop moisture condition based on unmanned aerial vehicle - Google Patents
Method, storage medium and equipment for monitoring crop moisture condition based on unmanned aerial vehicle Download PDFInfo
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Abstract
The application provides a method, a storage medium and equipment for monitoring crop moisture conditions based on an unmanned aerial vehicle, which are used for accurately monitoring the crop moisture conditions in the field, on one hand, reliable description can be carried out on the crop growth conditions, and on the other hand, effective support is provided for formulation of moisture management and irrigation systems in the field. The traditional field sensor measuring method needs to invest a large amount of manpower and material resources, the accuracy is influenced by subjective factors, and the method for carrying out high-flux monitoring by utilizing the unmanned aerial vehicle not only can liberate labor force, but also can effectively improve monitoring efficiency. The agricultural water saving has great significance for the current agricultural production in China, the accurate and efficient monitoring of the crop moisture condition is a part for realizing accurate irrigation and agricultural water saving, and the method has great effect on the monitoring of the crop moisture condition. The invention aims to provide a vegetation index for accurately describing the moisture condition of crops, which can quickly evaluate the moisture condition of crops in fields at low cost and high throughput.
Description
Technical Field
The invention relates to the technical field of remote sensing monitoring of crop drought, in particular to a method, a storage medium and equipment for monitoring crop moisture condition based on an unmanned aerial vehicle.
Background
Crop moisture status (CWS) is a key indicator of crop growth and is typically characterized using Vegetation Moisture Content (VMC) or Soil Moisture Content (SMC). The CWS can efficiently describe the water supply and demand in the field and can be further used to formulate irrigation programs. Accurate monitoring of the CWS has important significance for improving agricultural water efficiency and improving agricultural production. Over the past few decades, researchers have proposed many methods to evaluate CWS, such as time domain, frequency domain reflectometers, neutron measurements, negative pressure gauges, etc. However, these field measurement methods are typically sensitive to the manner and location of field installation. These measurement approaches are inefficient when they involve monitoring large areas and are difficult to capture the spatial heterogeneity of the field CWS.
With the development of various types of sensors in recent years, students have proposed hundreds of drought indexes for characterizing the moisture status of regional or field crops based on the observation of the sensors, and the indexes can be generally divided into site-based indexes and remote sensing-based indexes. Site-based indices are typically constructed using information such as air temperature, precipitation, etc. observed at meteorological sites, such as a normalized precipitation index (SPI), a Parmer Drought Severity Index (PDSI), and a normalized precipitation evaporation index (SPEI). Remote sensing-based indices typically use vegetation indices calculated from sensors deployed in satellites, airplanes, or Unmanned Aerial Vehicles (UAVs) to indirectly characterize a field CWS, these sensors mainly include RGB, multispectral, hyperspectral, thermal infrared, and radar sensors. Typical vegetation indices are, for example, a Vegetation Condition Index (VCI) calculated using the red and near infrared bands of a multispectral sensor, a normalized differential moisture index (NDWI) calculated using the near and short infrared bands of a multispectral remote sensor, a normalized multiband drought index (NMWI) calculated using the bands of the hyperspectral sensors 1640nm and 2130nm, and the like. The two indexes based on the site and the remote sensing have certain reliability for representing the CWS and monitoring the drought and are widely used, particularly the vegetation index based on the remote sensing can provide spatial distribution of field moisture conditions, and compared with a field measurement method, the method has higher efficiency and lower cost.
However, these indexes have certain limitations, which are mainly reflected in the following aspects:
(1) The accuracy of the site-based index depends mainly on the density of the observed sites and the method of spatial interpolation. For the field scale with small area and small climate difference, the spatial heterogeneity of the field CWS cannot be captured based on the index of the station.
(2) Remote sensing-based indices are less capable of characterizing CWS timing characteristics and are susceptible to spectral saturation effects.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method, a storage medium and equipment for monitoring the crop moisture condition based on an unmanned aerial vehicle, which can accurately represent the crop moisture condition in the field and the space-time distribution characteristics of the crop moisture condition.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for monitoring crop moisture conditions based on an unmanned aerial vehicle comprises the following steps:
s1, acquiring a long-time sequence multispectral image and a thermal infrared image of a target field by using a multispectral and thermal infrared sensor carried by an unmanned aerial vehicle; simultaneously acquiring the air temperature Ta of the target field observed by the meteorological station;
s2, geographic registration is carried out on the multispectral image and the thermal infrared image acquired by the unmanned aerial vehicle by using a geographic registration tool of ArcGIS software, namely, the position deviation in the two images is corrected; then, performing radiometric calibration on the geographical registered multispectral image by using a gray board and an ASD (automatic document distribution) to convert DN (number of digits) values in the multispectral image into reflectivity;
s3, utilizing a resampling tool in ArcGIS software to down-sample the multispectral image and the thermal infrared image to the same spatial resolution;
s4, calculating the normalized vegetation index NDVI of the long-time sequence of the target field block by utilizing the multispectral image to obtain an NDVI image, and calculating the earth surface temperature Ts of the long-time sequence of the target field block by utilizing the thermal infrared image to obtain a Ts image;
s5, putting the NDVI image and the Ts image of each period into a three-dimensional rectangular coordinate system pixel by pixel to construct a three-dimensional characteristic space of Ts-NDVI-Ta;
s6, selecting a certain NDVI value and a certain Ta value in a Ts-NDVI-Ta three-dimensional characteristic space, and extracting the maximum surface temperature Ts value under the corresponding condition, namely Ts max (ii) a Meanwhile, under the three-dimensional characteristic space of Ts-NDVI-Ta, a certain NDVI value and a certain Ta value are selected, and the value Ts of the minimum earth surface temperature under the corresponding condition is extracted, namely Ts min ；
S7, extracting the selected NDVI value, the selected Ta value and Ts under the corresponding conditions max Performing corresponding fitting on the values to obtain a dry surface; selecting NDVI value, ta value and Ts under corresponding conditions min Performing corresponding fitting on the values to obtain a wet surface; the fitting method is realized by a sklern library of Python language based on a random forest regression algorithm;
and S8, determining a three-dimensional drought index TDDI by using the Ts image and the corresponding dry surface and wet surface, and judging the moisture condition of the crops based on the three-dimensional drought index TDDI.
In order to optimize the technical scheme, the specific measures adopted further comprise:
further, in step S2, the calculation formula for radiometric calibration of the geo-registered multispectral image by using the gray board and the ASD is as follows:
in the formula, R i The reflectivity of the ith wave band of the multispectral image; DN i The gray value of the ith wave band of the multispectral image is obtained; DN i_Board 、R i_Board The gray value of the ith wave band of the multispectral image measured by the gray plate and the actual reflectivity of the ith wave band of the multispectral image measured by the ASD are measured respectively.
Further, in step S4, the specific calculation formula of the normalized vegetation index NDVI of the target field long-time sequence by using the multispectral image is as follows:
in the formula, NIR is a near infrared wave band in the multispectral image; r is the reflectivity of red wave band in multispectral image, and is represented by data R i To obtain the compound.
Further, in step S4, the specific calculation formula of the surface temperature Ts of the target field long-time sequence using the thermal infrared image is:
in the formula, DN' is the gray value of the thermal infrared image; DN Board The thermal infrared image gray value of a blackboard arranged on the ground of the target field is obtained; ts Board Observing the temperature of a blackboard arranged on the ground of the target field block for a temperature sensor; and removing the maximum 5% and minimum 5% of pixels of each stage of thermal infrared image as abnormal values.
Further, in step S8, the specific calculation formula for determining the three-dimensional drought index TDDI by using the Ts image and the corresponding dry surface and wet surface is as follows:
in the formula, ts i The earth surface temperature value of the Ts image pixel i; ts maxi Aiming at the Ts image pixel i, the NDVI value and the maximum earth surface temperature value corresponding to the Ta value are obtained; ts mini The minimum earth surface temperature value corresponding to the NDVI value and the Ta value of the Ts image pixel i is obtained.
Further, in step S8, the specific content of determining the crop moisture condition based on the three-dimensional drought index TDDI is as follows:
the value of the three-dimensional drought index TDDI is between 0 and 1, and the value is closer to 1, which means that the higher the water content of the crop is, the better the water state is; conversely, the poorer the moisture state.
A computer readable storage medium storing a computer program for causing a computer to perform a method of monitoring moisture status of a crop as claimed in any one of the preceding claims.
An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program, implementing the method for monitoring the moisture status of a crop as described in any one of the above.
The beneficial effects of the invention are:
the method mainly comprises the step of constructing a method for monitoring the crop moisture condition based on the unmanned aerial vehicle, and mainly provides a novel vegetation index based on unmanned aerial vehicle observation, so that the crop moisture condition in the field and the space-time distribution characteristics of the crop moisture condition can be accurately characterized. Compared with the traditional ground observation, the method greatly improves the monitoring efficiency in the field scale; compared with the traditional remote sensing vegetation index and the index based on the site, the method can more accurately describe the space-time distribution characteristics of the field moisture condition.
Drawings
FIG. 1 is a schematic flow diagram of the overall scheme of the present invention.
FIG. 2 is a schematic representation of the blackboard for thermal infrared targeting of the target field ground arrangement of the present invention.
FIG. 3 is a schematic diagram of a surface temperature image and an outlier rejection diagram according to the present invention.
FIG. 4 is a schematic representation of the Ts-NDVI-Ta three-dimensional space of the present invention, wherein (a) in FIG. 4 is a Ts-NDVI-Ta three-dimensional scattergram; FIG. 4 (b) is a schematic representation of the Ts-NDVI-Ta feature space based on a three-dimensional scattergram, i.e., a schematic representation of a dry surface and a wet surface; FIG. 4 (c) is a Ts-NDVI two-dimensional scattergram, i.e., a side view projection in (a); FIG. 4 (d) is a Ts-Ta two-dimensional scattergram, i.e., the elevation projection in (a); FIG. 4 (e) is a Ta-NDVI two-dimensional scatterplot, i.e., the top projection in (a).
FIG. 5 is a schematic diagram of the TDDI calculation results of target fields at different times.
FIG. 6 is a schematic representation of the correlation of TDDI of the present invention with vegetation water content.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
Referring to fig. 1, the overall scheme of the present invention is schematically illustrated, and comprises the following steps:
A. multispectral and thermal infrared sensors carried by the unmanned aerial vehicle are used for acquiring multispectral and thermal infrared multi-temporal images of the long-time sequence of the target field.
B. And (4) carrying out geographic registration on the images acquired by the unmanned aerial vehicle by using a geographic registration tool of ArcGIS software, namely correcting the deviation of the image positions.
C. And (3) performing radiometric calibration (formula 1) on the multispectral image acquired by the unmanned aerial vehicle by using a gray board and an ASD (automatic detection system), namely converting the DN value of the original image into an actual reflectivity.
In the formula: r i 、DN i The reflectivity and DN value of the ith wave band of the image are taken as the index of refraction and the DN value of the ith wave band of the image; DN i_Borad 、R i_Borad The DN value for the gray panel and the actual reflectance of the gray panel measured using the ASD.
D. And acquiring the air temperature Ta of the target field observed by the weather station.
E. And (3) utilizing a resampling tool in ArcGIS to downsample the multispectral image and the thermal infrared image to the same spatial resolution, and selecting a nearest neighbor method by using a downsampling method.
F. Calculating the normalized vegetation index NDVI image of the long-time sequence of the target field block by utilizing the multispectral image, and carrying out batch calculation on a plurality of images through a GDAL (generalized likelihood analysis) library of Python language, wherein the calculation method is shown as a formula 2:
in the formula: NIR and R are the reflectivities of the near infrared band and the red band, respectively, in the multi-spectral image.
G. The surface temperature image Ts of the long-time sequence of the target field is calculated by utilizing the thermal infrared image, and the batch calculation of a plurality of images is carried out through a GDAL (graphics hardware analysis) library of Python language, wherein the calculation mode is shown as formula 3:
in the formula: DN is the gray value of the thermal infrared image; DN Board Arranging thermal infrared image gray values of a blackboard (refer to fig. 2) for the ground; ts Board The temperature of the floor-disposed blackboard observed by the temperature sensor. Wherein the blackboard is characterized in that: the blackboard is made of metal and black in color, and is provided with a temperature sensor for monitoring the temperature of the blackboard in real time. And the maximum 5% and minimum 5% of the pixels of each phase image are removed as abnormal values (refer to fig. 3).
H. The NDVI and Ts of the long-time sequence are put into a three-dimensional rectangular coordinate system pixel by pixel to construct a three-dimensional feature space of Ts-NDVI-Ta, and the three-dimensional feature space is realized through a GDAL library of Python language (referring to FIG. 4, in the coordinate system, the horizontal axis can be set as Ta, the vertical axis can be set as NDVI, and the vertical axis can be set as Ts).
I. Extracting different NDVI and Ta and corresponding maximum Ts and minimum Ts according to the three-dimensional characteristic space of Ts-NDVI-Ta, namely Ts max And Ts min (refer to FIG. 4 in which a vertical line is drawn by selecting a point value on the horizontal axis and a vertical line is drawn by selecting a point value on the vertical axis in FIG. 4 (a), thereby forming an intersection in the plane of NDVI-Ta, and a line is drawn in the direction of the vertical axis based on the intersection, the line corresponding to a plurality of point values, refer to FIG. 4 in which a maximum value falls on the dry surface and a minimum value falls on the wet surface, and this gives a plurality of maximum values and minimum values in the same manner as fitting to the dry surface and the wet surface)
J. Fitting Ts max Relation with NDVI, ta, i.e. dry surface; fitting Ts min And NDVI, ta, i.e. wet surface; the fitting method selects a random forest regression algorithm, and is realized through a skleran library of Python language, the parameter n _ estimator is set to be 15, and the rest parameters are defaults (refer to FIG. 4).
K. Three-dimensional drought index TDDI (refer to fig. 5) is calculated by using Ts image and corresponding dry and wet surfaces, and the calculation method is formula 4:
in the formula, T si Is the surface temperature Ts of pixel i; ts maxi And Ts mini Maximum and minimum surface temperatures, ts, for NDVI and Ta, respectively, for pixel i max And Ts min And calculating and obtaining the forest information through the random forest model established in the last step. The value of TDDI lies between 0 and 1, the closer the value is to 1, the higher the moisture content is, the better the moisture state is; conversely, the worse the moisture state (refer to fig. 6).
Further, the present application also provides: a computer readable storage medium storing a computer program for causing a computer to perform a method of monitoring moisture status of a crop as claimed in any one of the preceding claims. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program implementing a method of monitoring moisture status of a crop as claimed in any one of the preceding claims.
It should be noted that the terms "upper", "lower", "left", "right", "front", "back", etc. used in the present invention are for clarity of description only, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not limited by the technical contents of the essential changes.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (8)
1. A method for monitoring crop moisture conditions based on an unmanned aerial vehicle is characterized by comprising the following steps:
s1, acquiring a long-time sequence multispectral image and a thermal infrared image of a target field by using a multispectral and thermal infrared sensor carried by an unmanned aerial vehicle; simultaneously acquiring the air temperature Ta of the target field observed by the meteorological station;
s2, geographic registration is carried out on the multispectral image and the thermal infrared image acquired by the unmanned aerial vehicle by using a geographic registration tool of ArcGIS software, namely, the position deviation in the two images is corrected; then, performing radiometric calibration on the geographic registered multispectral image by using a gray board and an ASD (automatic document detection) to convert DN values in the multispectral image into reflectivity;
s3, utilizing a resampling tool in ArcGIS software to downsample the multispectral image and the thermal infrared image to the same spatial resolution;
s4, calculating the normalized vegetation index NDVI of the long-time sequence of the target field by using the multispectral image to obtain an NDVI image, and calculating the earth surface temperature Ts of the long-time sequence of the target field by using the thermal infrared image to obtain a Ts image;
s5, putting the NDVI image and the Ts image of each period into a three-dimensional rectangular coordinate system pixel by pixel to construct a three-dimensional characteristic space of Ts-NDVI-Ta;
s6, selecting a certain NDVI value and a certain Ta value in a three-dimensional characteristic space of Ts-NDVI-Ta, and extracting the maximum earth surface temperature Ts value under the corresponding condition, namely Ts max (ii) a Meanwhile, under the three-dimensional characteristic space of Ts-NDVI-Ta, a certain NDVI value and a certain Ta value are selected, and the Ts value, namely the minimum earth surface temperature Ts value under the corresponding condition is extracted min ；
S7, extracting the selected NDVI value, the selected Ta value and the Ts extracted under the corresponding conditions max Performing corresponding fitting on the values to obtain a dry surface; selecting NDVI value, ta value and Ts under corresponding conditions min Performing corresponding fitting on the values to obtain a wet surface; the fitting method is based on a random forest regression algorithm and is realized through a sklern library of Python language;
and S8, determining a three-dimensional drought index TDDI by using the Ts image and the corresponding dry surface and wet surface, and judging the moisture condition of the crops based on the three-dimensional drought index TDDI.
2. The method for monitoring moisture status of crops by unmanned aerial vehicle according to claim 1, wherein in step S2, the calculation formula for radiometric calibration of the geo-registered multispectral image by using gray board and ASD is as follows:
in the formula, R i The reflectivity of the ith wave band of the multispectral image is obtained; DN i The gray value of the ith wave band of the multispectral image is obtained; DN i_Board 、R i_Board The gray value of the ith wave band of the multispectral image measured by the gray plate and the actual reflectivity of the ith wave band of the multispectral image measured by the ASD are measured respectively.
3. The method for monitoring the moisture condition of the crops based on the unmanned aerial vehicle as claimed in claim 2, wherein in step S4, the specific calculation formula for calculating the normalized vegetation index NDVI of the target field block long-time sequence by using the multispectral image is as follows:
in the formula, NIR is a near infrared wave band in the multispectral image; r is the reflectivity of red wave band in multispectral image and is represented by data R i To obtain the compound.
4. The method for monitoring the moisture condition of crops based on the unmanned aerial vehicle as claimed in claim 3, wherein in step S4, the surface temperature Ts of the target field is calculated by using the thermal infrared image for a long time sequence according to a specific calculation formula:
in the formula, DN' is the gray value of the thermal infrared image; DN Board The thermal infrared image gray value of a blackboard arranged on the ground of the target field is obtained; ts Board Observing the temperature of a blackboard arranged on the ground of the target field block for a temperature sensor; and removing the maximum 5% and minimum 5% of pixels of each stage of thermal infrared image as abnormal values.
5. The method for monitoring the moisture condition of crops based on unmanned aerial vehicle as claimed in claim 4, wherein in step S8, the specific calculation formula for determining the three-dimensional drought index TDDI by using the Ts image and the corresponding dry surface and wet surface is as follows:
in the formula, ts i The earth surface temperature value of the Ts image pixel i; ts maxi Aiming at the Ts image pixel i, the NDVI value and the Ta value of the Ts image pixel i correspond to the maximum earth surface temperature value; ts mini The minimum earth surface temperature value corresponding to the NDVI value and the Ta value of the Ts image pixel i is obtained.
6. The method for monitoring the moisture condition of the crop based on the unmanned aerial vehicle as claimed in claim 1, wherein in step S8, the specific content of the determination of the moisture condition of the crop based on the three-dimensional drought index TDDI is:
the value of the three-dimensional drought index TDDI is between 0 and 1, and the value is closer to 1, which means that the higher the water content of the crop is, the better the water state is; conversely, the poorer the moisture state.
7. A computer-readable storage medium storing a computer program, wherein the computer program causes a computer to perform the method of monitoring moisture status of a crop as claimed in any one of claims 1 to 6.
8. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program performing the method of monitoring moisture status of a crop as claimed in any one of claims 1 to 6.
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| CN117413815A (en) * | 2023-12-19 | 2024-01-19 | 山东源泉机械有限公司 | Forestry plant diseases and insect pests unmanned aerial vehicle sprays treatment system |
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| CN117413815A (en) * | 2023-12-19 | 2024-01-19 | 山东源泉机械有限公司 | Forestry plant diseases and insect pests unmanned aerial vehicle sprays treatment system |
| CN117413815B (en) * | 2023-12-19 | 2024-03-01 | 山东源泉机械有限公司 | Forestry plant diseases and insect pests unmanned aerial vehicle sprays treatment system |
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