Daily-scale corn water stress diagnosis method based on unmanned aerial vehicle technology

Abstract

The invention discloses a daily-scale corn water stress diagnosis method based on an unmanned aerial vehicle technology, and relates to the technical field of agriculture, the method is based on the definition of a water stress coefficient, introduces a Jarvis stomatal conductance model, combines canopy and plant height data obtained by an unmanned aerial vehicle, accurately estimates the aerodynamic impedance and the canopy impedance of a canopy, and improves the accuracy of the canopy stress. The actual evapotranspiration and the potential evapotranspiration are obtained through a multispectral leaf area index inversion model, then a daily scale moisture stress index is calculated, a plurality of groups of unmanned aerial vehicle thermal images are obtained according to four important growth periods of corn, and the moisture stress index is calculated and counted. And comparing the characterization capabilities of the daily scale moisture stress index and the moisture stress index in different time periods in one day on the actually measured moisture index, so as to improve the problems that the instantaneous moisture stress coefficient is insufficient in time representativeness and is easily influenced by the environment, and more accurately characterize the crop moisture stress. A monitoring effect close to a daily-scale moisture stress index is obtained by a easier method, and the practicability of the index is improved.

Description

Technical Field

[0001] The present invention relates to the field of agricultural technology, and in particular to a daily-scale corn water stress diagnosis method based on unmanned aerial vehicle technology. Background Art

[0002] Crop water stress inevitably exists in various agricultural production environments. Water shortage will affect crop growth and thus reduce crop yields. Crop water shortage is usually described by soil moisture and crop stem / leaf water potential, stomatal conductance, transpiration rate and other indicators. However, these methods are time-consuming and labor-intensive, requiring relatively expensive professional equipment for point-scale measurements, and cannot represent the spatial variability of crop water conditions. Therefore, these methods are not suitable for large-scale farmland scenarios. With the development of drone technology, drones can now carry different types of sensors for crop monitoring. Combining data from different sensors can provide a more comprehensive understanding of the real-time situation of crops. Drone remote sensing has the advantages of low cost, short measurement cycle, and flexible operation, which facilitates the study of crop water stress at the canopy scale.

[0003] At present, the most commonly used method for crop water stress monitoring based on UAV platforms is to use the crop water stress index (CWSI) to reflect crop water shortage. The principle is the inverse relationship between canopy temperature (Tc) and leaf transpiration and gs. When plants are under water stress, stomata close, resulting in reduced transpiration cooling, thereby increasing Tc. CWSI is currently widely recognized as an appropriate indicator for characterizing plant water status.

[0004] The research on CWSI calculation methods based on UAV platforms is mainly divided into three types:

[0005] The first is the theoretical CWSI, which is defined based on the ratio of actual evapotranspiration (ETa) to potential evapotranspiration (ETp) and derived from the crop canopy energy balance equation. The theoretical CWSI uses an energy balance method based on crop canopy-air temperature difference (dT), net radiation (Rn), aerodynamic impedance (ra), and canopy impedance (rc) to determine the upper and lower baselines. It has a strong theoretical basis, but the parameters involved in this method are extremely complex and are easily affected by the values ​​of ra and rc, resulting in incorrect diagnosis of whether the crop is under water stress.

[0006] The second is the empirical CWSI (CWSIe) obtained by combining the upper and lower baselines established by the empirical relationship between dT and saturated vapor pressure deficit (VPD). CWSIe can also be calculated by directly measuring the canopy temperature of water-stressed crops (Tdry) and the canopy temperature of well-irrigated crops (Twet), or by using an artificial reference surface to measure the temperature in these two situations. However, the upper and lower baselines of CWSIe need to be determined according to different climate environments, different crops and even different observation equipment. These reasons limit the application scenarios of empirical methods.

[0007] The third type is a statistical CWSI (CWSIs) calculated based on the canopy temperature statistical histogram obtained from the thermal infrared image of the drone to obtain Tdry and Twet. In this method, Twet is estimated by the lowest part of the canopy temperature histogram, and Tdry is obtained from the highest part of the histogram. The calculation of CWSIs does not require meteorological parameters and ground references, which greatly reduces the calculation complexity of CWSI.

[0008] Although the current research on using UAV remote sensing platforms to calculate CWSI for crop water stress diagnosis has shown good results in specific environments and specific times, it is still impossible to avoid the strong time variability of UAV observations under changing climate conditions. Using daily-scale CWSI to reflect crop water stress is an effective method to improve the temporal representativeness of CWSI. Currently, this type of CWSI uses the definition method of CWSIt to observe daily-scale CWSI, but the ETa used to calculate this CWSI is often obtained by equipment such as the eddy covariance system, and ETp is mostly estimated by models or arranged by ground-fixed thermal infrared sensors for continuous observations at equal time intervals, and then combined with the calculation method of CWSIe or CWSIt to obtain the daily-scale CWSI.

[0009] These methods can achieve continuous, automatic and accurate monitoring of plant water status, but expensive equipment, frequent maintenance and limited monitoring range limit their application to scientific research. At the same time, drones are limited by battery life and cannot perform continuous and uninterrupted observations like ground equipment. In order to overcome this shortcoming of drones, some studies have enlarged the daily "single shot" images obtained by drone systems and used the instantaneous CWSI at a certain moment as a benchmark for judging crop water stress, but this may lead to different results due to different times of the day and instantaneous meteorological conditions, or by using the best observation time of the day for CWSI instead of full-day observation, it was found that noon (12:00-15:00) is the best time to obtain dT data to diagnose plant water stress. However, these methods all have the disadvantage of insufficient representativeness. It is not comprehensive enough to describe the water stress of crops using only the crop status at an instant.

[0010] In view of the above-mentioned technical defects, a solution is now proposed. Summary of the invention

[0011] The purpose of the present invention is to compare the characterization capabilities of the daily-scale water stress index and the water stress index at different time periods of a day on the measured water index, so as to improve the problems that the instantaneous water stress coefficient is insufficiently representative in time and is easily affected by the environment, more accurately characterize crop water stress, and obtain a monitoring effect close to the daily-scale water stress index in an easier way, thereby improving the practicability of the index.

[0012] In order to achieve the above object, the present invention adopts the following technical scheme: a daily scale corn water stress diagnosis method based on drone technology, comprising the following steps:

[0013] Step 1: Set up several corn planting experimental areas for group management. Set up several ground control points in each corn planting experimental area at the corners of the field ridges to promote on-site image registration and obtain field data, including soil moisture content, stomatal conductance, meteorological data, and leaf area index;

[0014] The specific process of setting up several corn planting experimental areas is as follows:

[0015] Twelve experimental plots of 8m×8m were set up, corresponding to four irrigation treatments, including severe water shortage (W1), moderate water shortage (W2), mild water shortage (W3) and local experience irrigation quota of 25mm (W4). Two replicate plots were set up for each treatment. Each plot was equipped with a turbine flowmeter for independent water control, and a 1.5m separation zone was set between the plots.

[0016] In order to solve the problem of corn suffering from water stress due to irrigation management, W1, W2, and W3 were set to 40%, 60%, and 80% of the irrigation quota of W4 treatment, respectively, and 6 irrigations were carried out respectively. In addition, each treatment was irrigated with a high quota before planting, and the irrigation amount was 225 mm, which was the irrigation amount experienced by local farmers.

[0017] The spring corn planted in the corn planting experimental area was sown on April 24 (DOY=114), the variety was Simon 3358, and the growing period was 156 days. To avoid soil nutrient deficiency, basal fertilizers including urea (CH4N2O, 46%, 200kg / hm2) and diammonium phosphate fertilizer ((NH4)2HPO4, 14%, 90kg / hm2) were applied in the field before sowing for all treatments. No additional fertilizer was applied during the growth of corn.

[0018] Step 2: Based on the definition of theoretical water stress coefficient, i.e. CWSIt=1-ETa / ETp, where ETa is actual evapotranspiration and ETp is potential evapotranspiration, the soil moisture influence function is introduced and the Jarvis stomatal conductance model is established in combination with field data;

[0019] Step 3: Obtain image data in the corn planting experimental area by using a drone, wherein the image data includes multispectral images, visible light images, and thermal infrared images; establish a leaf area index inversion model based on the multispectral images and the leaf area index; calculate air impedance and canopy distribution maps based on visible light images; and calculate the instantaneous water stress coefficient by combining the canopy distribution maps and thermal infrared images;

[0020] The drone shown is specifically the Phantom 4 multi-spectral version integrated drone produced by Shenzhen DJI Innovations Technology Co., Ltd., model Phantom4-M, P4M. The drone is equipped with six 2.9-inch CMOS image sensors, including one color sensor for conventional visible light imaging, and five monochrome sensors for multi-spectral imaging, blue (B, wavelength 450nm±16nm), green (G, wavelength 560nm±16nm), red (R, 650nm±16nm), red edge (RE, 730nm±16nm), near infrared (NIR, 840nm±26nm). In addition, the P4M is equipped with an integrated light intensity sensor that can capture the intensity of sunlight to perform illumination compensation for image shooting, thereby improving the accuracy of multi-spectral data collection.

[0021] The P4M acquisition time is the same day as the thermal image acquisition, and the weather must be clear and cloudless, with a wind speed of less than level 3. The time is generally selected immediately after the thermal infrared data acquisition. To ensure the accuracy of the image, the flight altitude is set to 20m, the flight speed is 5m / s, the photo shooting method is hovering photography, the pixel resolution is 1.06cm / pixel, and the overlap of the heading and lateral directions is 80%;

[0022] According to the Jarvis stomatal conductance model combined with the canopy leaf area index inversion results, the leaf stomatal conductance was upscaled to the canopy conductance, and the canopy stomatal impedance of each irrigation treatment in different growth stages was calculated. The daily changes of canopy stomatal impedance of each treatment in the four growth stages (Rn>0) were statistically plotted. It can be seen that the canopy stomatal impedance of the four days showed a trend of gentle changes in the morning and noon, and a gradual increase in the afternoon, and a sharp increase after 15:00, which is related to the increase of Ta in the afternoon, the increase of VPD, and the decrease of water supply capacity of the surface soil due to evaporation.

[0023] At this time, it is close to sunset, the solar altitude angle is small, the incident radiation is reduced, the extinction coefficient of the canopy increases, the light energy reaching the leaves is reduced, the stomata shrink, and the canopy resistance increases. In addition, there are some differences in the canopy stomatal impedance at different growth stages. The rc in V9 and R3 is greater than that in VT and R1. This is because the corn canopy LAI is low in V9, so the canopy stomatal impedance is high at this time. The corn leaves grow vigorously in VT and R1, and the LAI is at its peak. At this time, the canopy stomatal impedance is low. The R3 period belongs to the late growth period of corn, the canopy leaves are aging, and the stomatal conductance decreases, which leads to an increase in the canopy stomatal impedance.

[0024] From the perspective of different water control treatments, in the four growth stages, the canopy stomatal impedance showed a trend of W1>W2>W3>W4. The canopy stomatal impedance of corn continued to increase with the reduction of irrigation water. The root system could not obtain enough water from the dry soil to meet the carbon metabolism and assimilation needs of the plant, resulting in a decrease in leaf water potential and cell tension, thereby reducing stomatal conductance, thus causing the canopy stomatal impedance to increase with the reduction of irrigation water.

[0025] Step 4: Establish a canopy impedance model based on the Jarvis stomatal conductance model and the leaf area index inversion model. The canopy impedance model is combined with air impedance to calculate the actual evapotranspiration on a daily scale, and meteorological data is obtained to calculate the potential evapotranspiration on a daily scale. Then, the water stress index on a daily scale is calculated based on the actual evapotranspiration and the potential evapotranspiration.

[0026] Step 5. According to the four important growth periods of corn, several groups of drone thermal images are acquired and the statistical water stress index is calculated. The characterization ability of the daily scale water stress index and the statistical water stress index for the measured water index at different times of the day is compared. The training set and the test set are divided into 70% and 30% respectively, and they are put into the RF regression model in order of importance for modeling, and the results are output.

[0027] The temperature Ta, relative humidity RH, emissivity ε and flight altitude measured by the weather station at the time of the UAV flight were obtained, and the above data were input into the DJIThermalSDK1.4 software to obtain the actual temperature Ts. The image format was batch converted from R-JPEG to RAW, and the RAW format was converted to TIF format using the Python Numpy module. The shooting position and direction system information were then exported using the piexif module. The extracted direction system information and TIF files were imported into the Pix4D software for orthorectification and stitching, and GCPs were used for optimization to make the ground error <3cm.

[0028] Before each flight, a black-and-white plate on the ground was photographed and its temperature was measured with a handheld infrared thermometer to calibrate Tc.

[0029] Furthermore, based on the soil moisture influence function (f θ The specific process of constructing the stomatal conductance model is as follows:

[0030] Preset f θ When the term (θ) is 1, the crop reaches a non-water stress state, and the canopy impedance at this time is rcp. It can be seen that the form of the model is: g s =f p (PAR)·f v (VPD)·f t (T a )·f c (C) f θ (θ)#(13), where meteorological factors include photosynthetically active radiation f p (PAR), saturated vapor pressure difference f v (VPD), air temperature f t (T a ), CO2 concentration in the atmosphere c (C) Soil moisture content of the cultivated layer f θ (θ);

[0031] The model formula for meteorological factors and stomatal conductance is as follows:

[0032] The light and effective radiation f p (PAR):

[0033]

[0034] The saturated vapor pressure difference f v (VPD):

[0035]

[0036] The air temperature f t (T a ):

[0037]

[0038] The soil moisture content f θ (θ):

[0039] Since the CO2 concentration in the environment changes very little, the effect on stomatal conductance can be ignored, where f c (C) takes the value of 1, θ is the average soil moisture content of the root water absorption layer, and the average soil moisture content of 0-60cm is selected; θ w is the wilting moisture content; θ cm is the field water holding capacity;

[0040] f p (PAR), f v (VPD), f t (T a ), f θ (θ) can be combined to obtain N different Jarvis stomatal conductance models. The measured stomatal conductance data of several groups are divided into training set and validation set according to the ratio of 7:3. 2 The optimal stomatal conductance model was obtained by screening with RMSE.

[0041] Furthermore, the specific process of obtaining visible light data and collecting multispectral images is as follows:

[0042] The plane images of the corn planting experimental area were obtained by drone. The plane images taken by the drone were reconstructed and spliced ​​using DJI Terra 3.6.8 software, and ground black-and-white plate photos were added for radiation correction. The MS orthophoto map and digital surface image of the corn planting experimental area were obtained, and ground control points were added for optimization. The ground error was less than 3 cm, and 1081-1092 images were obtained for each mission.

[0043] Furthermore, the specific process of calculating the canopy leaf area index and plant height data is as follows:

[0044] Through direct measurement, three representative corn plants with uniform growth were selected from each corn planting experimental area, and the leaf length Lij and maximum leaf width Bij of each leaf were measured with a tape measure. The canopy leaf area index LAI was calculated according to the following:

[0045] Where n is the total number of leaves of the jth plant, m is the number of plants; ρ is the planting density; 0.75 is the correction factor;

[0046] In each corn planting experimental area, a corn plant was selected to measure the plant height with a tape measure and the longitude and latitude of the measured plant were recorded to calibrate the plant height data obtained by the drone.

[0047] Furthermore, the specific process of calculating canopy impedance data is as follows:

[0048] The plane images of the corn planting experimental area were obtained by drone, and the LCPro T fully automatic portable photosynthetic meter produced by AT Company of the United Kingdom was used to measure the transpiration rate, stomatal conductance and photosynthetic rate.

[0049] In each corn planting experimental area, three typical leaves of corn were randomly selected to measure the stomatal conductance at the 1 / 3 of the leaf surface close to the leaf tip, and the average value was taken as the stomatal conductance value of the sampling point;

[0050] Soil sampling was carried out 5-7 days after irrigation in each corn planting experimental area. Three sampling points near the center of the sample plot were randomly selected, and soil samples at three depths of 0-20cm, 20-40cm, and 40-60cm were obtained using a soil drill. The soil samples from the three sampling points at each depth were mixed and placed in an aluminum box. The soil moisture content at each depth was measured after drying in an oven at 105°C for 8 hours. This was taken as a group of soil moisture sample values. A total of 48 groups of measured soil moisture data were obtained in the experiment.

[0051] The leaf stomatal conductance g is calculated according to the following formula s Converted to pore impedance r s :

[0052] The default Jarvis stomatal conductance model is f θ (θ)=1, that is, soil moisture content is equal to field water holding capacityθ=θ c = 0.32, potential evapotranspiration occurs, and then r is obtained sp ;

[0053] Canopy impedance data were calculated according to the following formula: Among them LAI c To remove the soil background value, r c is the canopy stomatal impedance, and r can be obtained by the same logic. cp .

[0054] Furthermore, the actual evapotranspiration and potential evapotranspiration are calculated by the PM method as follows:

[0055]

[0056] Where R n is the net radiation MJm / 2day, G is the soil heat flux MJm / 2day, r a is the aerodynamic drag sm-1, r c is the canopy resistance sm-1, r cp is the potential canopy resistance during potential evapotranspiration sm-1, γ is the dry-wet constant, ρ is the air density kgm-3, VPD is the saturated water vapor pressure difference kPa, T is the daily average temperature at 2m, Δ is the slope of the water vapor pressure curve, C p is the heat capacity of air, specifically 1.013MJ / kg-1℃-1)

[0057] The air density is calculated according to the following formula: ρ = 1.2837-0.039T a #(6), where T a is the air temperature;

[0058] The dry and wet constant γ is calculated according to the following formula: Where Z is the altitude;

[0059] The slope of the water vapor pressure curve Δ is calculated according to the following formula:

[0060] The saturated vapor pressure difference VPD is calculated according to the following formula: In the formula, RH represents the relative humidity of air;

[0061] The aerodynamic resistance r a Calculated according to the following formula: Where d represents the zero plane displacement m, which is 0.63h; z0 represents the roughness m, which is 0.13h, where h represents the crop height m; k represents the Kalman coefficient, which is 0.41; μ represents the wind speed at the reference height m / s, and z represents the reference height of the meteorological station m, that is, the wind speed measurement height.

[0062] Furthermore, the specific process of calculating the daily scale water stress index is as follows:

[0063] S101. Calculate the water stress index according to the following formula: Where T c is the average canopy temperature of each treatment, T wet and T dry Corresponds to canopy temperatures when corn plants are transpiring at maximum rates and when they are not transpiring at all;

[0064] The canopy temperature frequency histogram used to calculate each treatment was drawn using the ROIComputeStatistics function of ENVI5.6. The temperature distribution of the corn canopy and soil in the image is close to a bimodal form, and the temperature distribution of the pure corn canopy is a Gaussian distribution;

[0065] T wet and T dry The 0.5% and 99.5% means of the pure corn canopy temperature histogram were taken, respectively.

[0066] S102, calculate the theoretical water stress index t according to the following formula: Where ET a is the actual evapotranspiration, ET p is the potential evapotranspiration;

[0067] The theoretical water stress index and the statistical water stress index are both dimensionless, and their value range is 0-1, where 0 and 1 represent no water stress and the most severe water stress, respectively.

[0068] S103. The observation frequency of meteorological data is preset to 30 minutes / time, that is, the average meteorological data is obtained every 30 minutes. The unit of the actual evapotranspiration and potential evapotranspiration calculated is mm / 30 minutes. Then ETa_day =∑ET a_i #(12), where ET a_i is the ET of the i-th 30 minutes in a day a ,According to the analysis of daytime conditions, Rn≥0.

[0069] Furthermore, the calculation method of the plant height h of the crop at each growth stage is as follows:

[0070] The digital surface model DSM0 of the farmland before seedling emergence obtained by the drone is used as the base value, and ground landmarks are selected to calibrate the DSM of each growth period. i , and then get h i =DSM i -DSM0(i=1,2,3,...,i)#(11);

[0071] After correcting hi with the actual plant height measured on the ground, the final plant height h of crops at each growth stage was obtained using ENVI5.6 software.

[0072] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0073] This daily-scale corn water stress diagnosis method based on drone technology is based on the definition of water stress coefficient, introduces the Jarvis stomatal conductance model and combines the canopy and plant height data obtained by drone to accurately estimate the aerodynamic impedance and canopy impedance of the canopy, obtains the actual evapotranspiration and potential evapotranspiration through the multispectral leaf area index inversion model, and then calculates the daily-scale water stress index. According to the four important growth periods of corn, several groups of drone thermal images are collected and the statistical water stress index is calculated. The characterization ability of the daily-scale water stress index and the water stress index at different times of the day for the measured water indicators is compared, which is used to improve the problem that the instantaneous water stress coefficient is not representative enough in time and is easily affected by the environment, more accurately characterize crop water stress, and obtain a monitoring effect close to the daily-scale water stress index in an easier way, thereby improving the practicality of the index. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 The schematic diagram of the daily scale corn water stress calculation process of the present invention is shown;

[0075] Figure 2 A schematic diagram of the daily scale corn water stress calculation process of the present invention is shown;

[0076] Figure 3 The schematic diagram of classification of the Jarvis stomatal conductance model of the present invention is shown;

[0077] Figure 4 The schematic diagram of daily variation data of canopy impedance during the growth period of corn according to the present invention is shown;

[0078] Figure 5 A schematic diagram of the output results of the RF regression model of the present invention is shown. DETAILED DESCRIPTION

[0079] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0080] Embodiment 1:

[0081] like Figure 1-5 As shown in the figure, the daily scale corn water stress diagnosis method based on drone technology includes the following steps:

[0082] Step 1: Set up several corn planting experimental areas for group management. Set up several ground control points in each corn planting experimental area at the corners of the field ridges to promote on-site image registration and obtain field data, including soil moisture content, stomatal conductance, meteorological data, and leaf area index;

[0083] The specific process of setting up several corn planting experimental areas is as follows:

[0084] Twelve experimental plots of 8m×8m were set up, corresponding to four irrigation treatments, including severe water shortage (W1), moderate water shortage (W2), mild water shortage (W3) and local experience irrigation quota of 25mm (W4). Two replicate plots were set up for each treatment. Each plot was equipped with a turbine flowmeter for independent water control, and a 1.5m separation zone was set between the plots.

[0085] In order to solve the problem of corn suffering from water stress due to irrigation management, W1, W2, and W3 were set to 40%, 60%, and 80% of the irrigation quota of W4 treatment, respectively, and 6 irrigations were carried out respectively. In addition, each treatment was irrigated with a high quota before planting, and the irrigation amount was 225 mm, which was the irrigation amount experienced by local farmers.

[0086] The spring corn planted in the corn planting experimental area was sown on April 24 (DOY=114), the variety was Simon 3358, and the growing period was 156 days. To avoid soil nutrient deficiency, basal fertilizers including urea (CH4N2O, 46%, 200kg / hm2) and diammonium phosphate fertilizer ((NH4)2HPO4, 14%, 90kg / hm2) were applied in the field before sowing for all treatments. No additional fertilizer was applied during the growth of corn.

[0087] Step 2: Based on the definition of theoretical water stress coefficient, i.e. CWSIt=1-ETa / ETp, where ETa is actual evapotranspiration and ETp is potential evapotranspiration, the soil moisture influence function is introduced and the Jarvis stomatal conductance model is established in combination with field data;

[0088] The temperature Ta, relative humidity RH, emissivity ε and flight altitude measured by the weather station at the time of the UAV flight were obtained, and the above data were input into the DJIThermalSDK1.4 software to obtain the actual temperature Ts. The image format was batch converted from R-JPEG to RAW, and the RAW format was converted to TIF format using the Python Numpy module. The shooting position and direction system information were then exported using the piexif module. The extracted direction system information and TIF files were imported into the Pix4D software for orthorectification and stitching, and GCPs were used for optimization to make the ground error <3cm.

[0089] Before each flight, a black-and-white plate on the ground was photographed and its temperature was measured with a handheld infrared thermometer to calibrate Tc.

[0090] Based on the soil moisture influence function (f θ The specific process of constructing the stomatal conductance model is as follows:

[0091] Preset f θ When the term (θ) is 1, the crop reaches a non-water stress state, and the canopy impedance at this time is rcp. It can be seen that the form of the model is: g s =f p (PAR)·f v (VPD)·f t (T a )·f c (C) f θ (θ)#(13), where meteorological factors include photosynthetically active radiation f p (PAR), saturated vapor pressure difference f v (VPD), air temperature f t (T a ), CO2 concentration in the atmosphere c (C) Soil moisture content of the cultivated layer f θ (θ);

[0092] The model formula for meteorological factors and stomatal conductance is as follows:

[0093] Light and effective radiation f p (PAR):

[0094]

[0095] Saturated vapor pressure difference f v (VPD):

[0096]

[0097] Temperature t (T a ):

[0098]

[0099] Soil moisture content f θ (θ):

[0100] Since the CO2 concentration in the environment changes very little, the effect on stomatal conductance can be ignored, where f c (C) takes the value of 1, θ is the average soil moisture content of the root water absorption layer, and the average soil moisture content of 0-60cm is selected; θ w is the wilting moisture content; θ cm is the field water capacity;

[0101] f p (PAR), f v (VPD), f t (T a ), f θ (θ) combination can obtain N different Jarvis stomatal conductance models, which are 1-1-1-3-3-2, such as Figure 3 As shown in the figure, the measured stomatal conductance data of several groups were divided into training set and validation set in a ratio of 7:3. 2 The optimal stomatal conductance model was obtained by screening with RMSE.

[0102] Step 3: Obtain image data in the corn planting experimental area by using a drone, wherein the image data includes multispectral images, visible light images, and thermal infrared images; establish a leaf area index inversion model based on the multispectral images and the leaf area index; calculate air impedance and canopy distribution maps based on visible light images; and calculate the instantaneous water stress coefficient by combining the canopy distribution maps and thermal infrared images;

[0103] The drone shown is specifically the Phantom 4 multi-spectral version integrated drone produced by Shenzhen DJI Innovations Technology Co., Ltd., model Phantom4-M, P4M. The drone is equipped with six 2.9-inch CMOS image sensors, including one color sensor for conventional visible light imaging, and five monochrome sensors for multi-spectral imaging, blue (B, wavelength 450nm±16nm), green (G, wavelength 560nm±16nm), red (R, 650nm±16nm), red edge (RE, 730nm±16nm), near infrared (NIR, 840nm±26nm). In addition, the P4M is equipped with an integrated light intensity sensor that can capture the intensity of sunlight to perform illumination compensation for image shooting, thereby improving the accuracy of multi-spectral data collection.

[0104] The P4M acquisition time is the same day as the thermal image acquisition, and the weather must be clear and cloudless, with a wind speed of less than level 3. The time is generally selected immediately after the thermal infrared data acquisition. To ensure the accuracy of the image, the flight altitude is set to 20m, the flight speed is 5m / s, the photo shooting method is hovering photography, the pixel resolution is 1.06cm / pixel, and the overlap of the heading and lateral directions is 80%;

[0105] According to the Jarvis stomatal conductance model combined with the canopy leaf area index inversion results, the leaf stomatal conductance was upscaled to the canopy conductance, and the canopy stomatal impedance of each irrigation treatment at different growth stages was calculated. The daily changes of canopy stomatal impedance of each treatment in the four growth stages were statistically plotted (Rn>0). Figure 4 As shown, it can be seen that the canopy stomatal impedance showed a trend of gentle change in the morning and noon, and gradually increased in the afternoon for the four days, and a sharp increase after 15:00, which is related to the increase of Ta in the afternoon, the increase of VPD, and the decrease of water supply capacity of surface soil due to evaporation.

[0106] At this time, it is close to sunset, the solar altitude angle is small, the incident radiation is reduced, the extinction coefficient of the canopy increases, the light energy reaching the leaves is reduced, the stomata shrink, and the canopy resistance increases. In addition, there are some differences in the canopy stomatal impedance at different growth stages. The rc in V9 and R3 is greater than that in VT and R1. This is because the corn canopy LAI is low in V9, so the canopy stomatal impedance is high at this time. The corn leaves grow vigorously in VT and R1, and the LAI is at its peak. At this time, the canopy stomatal impedance is low. The R3 period belongs to the late growth period of corn, the canopy leaves are aging, and the stomatal conductance decreases, which leads to an increase in the canopy stomatal impedance.

[0107] From the perspective of different water control treatments, in the four growth stages, the canopy stomatal impedance showed a trend of W1>W2>W3>W4. The canopy stomatal impedance of corn continued to increase with the reduction of irrigation water. The root system could not obtain enough water from the dry soil to meet the carbon metabolism and assimilation needs of the plant, resulting in a decrease in leaf water potential and cell tension, thereby reducing stomatal conductance, thus causing the canopy stomatal impedance to increase with the reduction of irrigation water.

[0108] The specific process of acquiring visible light data and collecting multispectral images is as follows:

[0109] The plane images of the corn planting experimental area were obtained by drone. The plane images taken by the drone were reconstructed and spliced ​​using DJI Terra 3.6.8 software, and ground black-and-white plate photos were added for radiation correction. The MS orthophoto map and digital surface image of the corn planting experimental area were obtained, and ground control points were added for optimization. The ground error was less than 3 cm, and 1081-1092 images were obtained for each mission.

[0110] The specific process of calculating the canopy leaf area index and plant height data is as follows:

[0111] Through direct measurement, three representative corn plants with uniform growth were selected from each corn planting experimental area, and the leaf length Lij and maximum leaf width Bij of each leaf were measured with a tape measure. The canopy leaf area index LAI was calculated according to the following:

[0112] Where n is the total number of leaves of the jth plant, m is the number of plants; ρ is the planting density; 0.75 is the correction factor;

[0113] In each corn planting experimental area, a corn plant was selected to measure the plant height with a tape measure and the longitude and latitude of the measured plant were recorded to calibrate the plant height data obtained by the drone.

[0114] The specific process of calculating canopy impedance data is as follows:

[0115] The plane images of the corn planting experimental area were obtained by drone, and the LCPro T fully automatic portable photosynthetic meter produced by AT Company of the United Kingdom was used to measure the transpiration rate, stomatal conductance and photosynthetic rate.

[0116] In each corn planting experimental area, three typical leaves of corn were randomly selected to measure the stomatal conductance at the 1 / 3 of the leaf surface close to the leaf tip, and the average value was taken as the stomatal conductance value of the sampling point;

[0117] Soil sampling was carried out 5-7 days after irrigation in each corn planting experimental area. Three sampling points near the center of the sample plot were randomly selected, and soil samples at three depths of 0-20cm, 20-40cm, and 40-60cm were obtained using a soil drill. The soil samples from the three sampling points at each depth were mixed and placed in an aluminum box. The soil moisture content at each depth was measured after drying in an oven at 105°C for 8 hours. This was taken as a group of soil moisture sample values. A total of 48 groups of measured soil moisture data were obtained in the experiment.

[0118] The leaf stomatal conductance g is calculated according to the following formula s Converted to pore impedance r s :

[0119] The default Jarvis stomatal conductance model is f θ (θ)=1, that is, soil moisture content is equal to field water holding capacityθ=θ c = 0.32, potential evapotranspiration occurs, and then r is obtained sp ;

[0120] Canopy impedance data were calculated according to the following formula: Among them LAI c To remove the soil background value, r c is the canopy stomatal impedance, and r can be obtained by the same logic. cp .

[0121] The calculation process of actual evapotranspiration and potential evapotranspiration using the PM method is as follows:

[0122]

[0123] Where R n is the net radiation MJm / 2day, G is the soil heat flux MJm / 2day, r a is the aerodynamic drag sm-1, r c is the canopy resistance sm-1, r cp is the potential canopy resistance during potential evapotranspiration sm-1, γ is the dry-wet constant, ρ is the air density kgm-3, VPD is the saturated water vapor pressure difference kPa, T is the daily average temperature at 2m, Δ is the slope of the water vapor pressure curve, C p is the heat capacity of air, specifically 1.013MJ / kg-1℃-1)

[0124] The air density is calculated according to the following formula: ρ = 1.2837-0.039T a #(6), where T a is the air temperature;

[0125] The dry and wet constant γ is calculated according to the following formula: Where Z is the altitude;

[0126] The slope of the water vapor pressure curve Δ is calculated according to the following formula:

[0127] The saturated vapor pressure difference VPD is calculated according to the following formula: In the formula, RH represents the relative humidity of air;

[0128] The aerodynamic resistance r a Calculated according to the following formula: Where d represents the zero plane displacement m, which is 0.63h; z0 represents the roughness m, which is 0.13h, where h represents the crop height m; k represents the Kalman coefficient, which is 0.41; μ represents the wind speed at the reference height m / s, and z represents the reference height of the meteorological station m, that is, the wind speed measurement height.

[0129] Step 4: Establish a canopy impedance model based on the Jarvis stomatal conductance model and the leaf area index inversion model. The canopy impedance model is combined with air impedance to calculate the actual evapotranspiration on a daily scale, and meteorological data is obtained to calculate the potential evapotranspiration on a daily scale. Then, the water stress index on a daily scale is calculated based on the actual evapotranspiration and the potential evapotranspiration.

[0130] The specific process of calculating the daily scale water stress index is as follows:

[0131] S101. Calculate the water stress index according to the following formula: Where T c is the average canopy temperature of each treatment, T wet and T dry Corresponds to canopy temperatures when corn plants are transpiring at maximum rates and when they are not transpiring at all;

[0132] The canopy temperature frequency histogram used to calculate each treatment was drawn using the ROIComputeStatistics function of ENVI5.6. The temperature distribution of the corn canopy and soil in the image is close to a bimodal form, and the temperature distribution of the pure corn canopy is a Gaussian distribution;

[0133] T wet and T dry The 0.5% and 99.5% means of the pure corn canopy temperature histogram were taken, respectively.

[0134] S102, calculate the theoretical water stress index t according to the following formula: Where ET a is the actual evapotranspiration, ET p is the potential evapotranspiration;

[0135] Both the theoretical water stress index and the statistical water stress index are dimensionless, and their value range is 0-1, where 0 and 1 represent no water stress and the most severe water stress, respectively.

[0136] S103. The observation frequency of meteorological data is preset to 30 minutes / time, that is, the average meteorological data is obtained every 30 minutes. The unit of the actual evapotranspiration and potential evapotranspiration calculated is mm / 30 minutes. Then ET a_day =∑ET a_i #(12), where ET a_i is the ET of the i-th 30 minutes in a day a ,According to the analysis of daytime conditions, Rn≥0.

[0137] The calculation method of plant height h at each growth stage of crops is as follows:

[0138] The digital surface model DSM0 of the farmland before seedling emergence obtained by the drone is used as the base value, and ground landmarks are selected to calibrate the DSM of each growth period. i , and then get h i =DSM i -DSM0(i=1,2,3,...,i)#(11);

[0139] After correcting hi with the actual plant height measured on the ground, the final plant height h of crops at each growth stage was obtained using ENVI5.6 software.

[0140] Step 5. According to the four important growth periods of corn, several groups of drone thermal images are acquired and the statistical water stress index is calculated. The characterization ability of the daily scale water stress index and the statistical water stress index for the measured water index at different times of the day is compared. The training set and the test set are divided into 70% and 30% respectively, and they are put into the RF regression model in order of importance for modeling, and the results are output.

[0141] The water stress index values ​​at different growth stages were used as independent variables at different times of the day, and the daily water stress index was used as the dependent variable to evaluate the importance of the RF model. The training set and the test set were divided into 70% and 30% respectively, and they were put into the RF regression model in order of importance. The results showed that Figure 5 As shown;

[0142] The model prediction accuracy of the data set with all-day observations in 6 time periods is the highest, with an R2 of 0.81 and an RMSE of 0.039. The R2 reaches 0.79 and the RMSE reaches 0.04, which is very close to the accuracy of all-day observations in 6 time periods. This shows that observations in three time periods from noon to early afternoon can replace all-day observations and achieve similar results, thereby improving the cost-effectiveness of daily water stress index observations.

[0143] The present invention is based on the definition of water stress coefficient, introduces the Jarvis stomatal conductance model and combines the canopy and plant height data obtained by the UAV, accurately estimates the aerodynamic impedance and canopy impedance of the canopy, obtains the actual evapotranspiration and potential evapotranspiration through the multispectral leaf area index inversion model, and then calculates the daily scale water stress index, obtains several groups of UAV thermal image acquisition according to the four important growth periods of corn, calculates and statistics the water stress index, compares the characterization ability of the daily scale water stress index and the water stress index for the measured water index at different time periods of a day, and is used to improve the problems that the instantaneous water stress coefficient is insufficiently representative in time and is easily affected by the environment, more accurately characterizes the water stress of crops, obtains the monitoring effect close to the daily scale water stress index in a more relaxed way, and improves the practicability of the index.

[0144] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0145] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A daily-scale corn water stress diagnosis method based on drone technology, characterized in that: The steps include: Step 1: Set up several corn planting experimental areas and manage them in groups. Set up several ground control points in each corn planting experimental area at the corners of the field ridges to obtain field data, including soil moisture content, stomatal conductance, meteorological data, and leaf area index; Step 2: Based on the definition of theoretical water stress coefficient, i.e. CWSIt=1-ETa / ETp, where ETa is actual evapotranspiration and ETp is potential evapotranspiration, the soil moisture influence function is introduced and the Jarvis stomatal conductance model is established in combination with field data; Step 3: Obtain image data in the corn planting experimental area by using a drone, wherein the image data includes multispectral images, visible light images, and thermal infrared images; establish a leaf area index inversion model based on the multispectral images and the leaf area index; calculate air impedance and canopy distribution maps based on visible light images; and calculate the instantaneous water stress coefficient by combining the canopy distribution maps and thermal infrared images; Step 4: Establish a canopy impedance model based on the Jarvis stomatal conductance model and the leaf area index inversion model. The canopy impedance model is combined with air impedance to calculate the actual evapotranspiration on a daily scale, and meteorological data is obtained to calculate the potential evapotranspiration on a daily scale. Then, the water stress index on a daily scale is calculated based on the actual evapotranspiration and the potential evapotranspiration. Step 5. According to the four important growth periods of corn, several groups of drone thermal images are acquired and the statistical water stress index is calculated. The characterization ability of the daily scale water stress index and the statistical water stress index for the measured water index at different times of the day is compared. The training set and the test set are divided into 70% and 30% respectively, and they are put into the RF regression model in order of importance for modeling, and the results are output.

2. The daily scale corn water stress diagnosis method based on drone technology according to claim 1 is characterized in that: The specific process of constructing the stomatal conductance model based on the soil moisture influence function (fθ(θ)) is as follows: Preset f θ When the term (θ) is 1, the crop reaches a non-water stress state, and the canopy impedance at this time is rcp. It can be seen that the form of the model is: g s =f p (PAR)·f v (VPD)·f t (T a )·f c (C) f θ (θ)#(13), where meteorological factors include photosynthetically active radiation f p (PAR), saturated vapor pressure difference f v (VPD), air temperature f t (T a ), CO2 concentration in the atmosphere c (C) Soil moisture content of the cultivated layer f θ (θ); The model formula for meteorological factors and stomatal conductance is as follows: The light and effective radiation f p (PAR): The saturated vapor pressure difference f v (VPD): The air temperature f t (T a ): The soil moisture content f θ (θ): where f c (C) is taken as 1, θ is the average soil moisture content in the root water absorption layer, θ w is the wilting moisture content; θ cm is the water holding rate of the field; f p (PAR), f v (VPD), f t (T a ), f θ (θ) can be combined to obtain N different Jarvis stomatal conductance models. The measured stomatal conductance data of several groups are divided into training set and validation set according to the ratio of 7:

3. 2 The optimal stomatal conductance model was obtained by screening with RMSE.

3. The daily scale corn water stress diagnosis method based on drone technology according to claim 1 is characterized in that: The specific process of acquiring visible light data and collecting multispectral images is as follows: The plane images of the corn planting experimental area were obtained by drone. The plane images taken by the drone were reconstructed and spliced ​​using DJI Terra 3.6.8 software, and ground black-and-white plate photos were added for radiation correction. The MS orthophoto map and digital surface image of the corn planting experimental area were obtained, and ground control points were added for optimization. The ground error was less than 3 cm, and 1081-1092 images were obtained for each mission.

4. The daily scale corn water stress diagnosis method based on drone technology according to claim 1 is characterized in that: The specific process of calculating the canopy leaf area index and plant height data is as follows: Through direct measurement, three representative corn plants with uniform growth were selected from each corn planting experimental area, and the leaf length Lij and maximum leaf width Bij of each leaf were measured with a tape measure. The canopy leaf area index LAI was calculated according to the following: Where n is the total number of leaves of the jth plant, m is the number of plants; ρ is the planting density; 0.75 is the correction factor; In each corn planting experimental area, a corn plant was selected to measure the plant height with a tape measure and the longitude and latitude of the measured plant were recorded to calibrate the plant height data obtained by the drone.

5. The daily scale corn water stress diagnosis method based on drone technology according to claim 1 is characterized in that: The specific process of calculating canopy impedance data is as follows: The plane images of the corn planting experimental area were obtained by drone, and the LCProT fully automatic portable photosynthetic meter produced by AT Company of the United Kingdom was used to measure the transpiration rate, stomatal conductance and photosynthetic rate. The leaf stomatal conductance g is calculated according to the following formula s Converted to pore impedance r s : The default Jarvis stomatal conductance model is f θ (θ)=1, that is, soil moisture content is equal to field water holding capacityθ=θ c = 0.32, potential evapotranspiration occurs, and then r is obtained sp ; Canopy impedance data were calculated according to the following formula: Among them LAI c To remove the soil background value, r c is the canopy stomatal impedance, and r can be obtained by the same logic. cp .

6. The daily scale corn water stress diagnosis method based on drone technology according to claim 1 is characterized in that: The calculation process of the actual evapotranspiration and potential evapotranspiration by PM method is as follows: Where R n is the net radiation MJm / 2day, G is the soil heat flux MJm / 2day, r a is the aerodynamic drag sm -1 , r c is the canopy resistance sm -1 , r cp is the potential canopy resistance when potential evapotranspiration is sm -1 , γ is the dry and wet constant, ρ is the air density kgm -3 , VPD is the saturated water vapor pressure difference kPa, T is the daily average temperature at 2m, Δ is the slope of the water vapor pressure curve, C p is the heat capacity of air, specifically 1.013MJ / kg -1 ℃ -1 ) The air density is calculated according to the following formula: ρ = 1.2837-0.039T a #(6), where T a is the air temperature; The dry and wet constant r is calculated according to the following formula: Where Z is the altitude; The slope of the water vapor pressure curve Δ is calculated according to the following formula: The saturated vapor pressure difference VPD is calculated according to the following formula: In the formula, RH represents the relative humidity of air; The aerodynamic resistance r a Calculated according to the following formula: Where d represents the zero plane displacement m, which is 0.63h; z0 represents the roughness m, which is 0.13h, where h represents the crop height m; k represents the Kalman coefficient, which is 0.41; μ represents the wind speed at the reference height m / s, and z represents the reference height of the meteorological station m, that is, the wind speed measurement height.

7. The daily scale corn water stress diagnosis method based on drone technology according to claim 1 is characterized in that: The specific process of calculating the daily scale water stress index is as follows: S101. Calculate the water stress index according to the following formula: Where T C is the average canopy temperature of each treatment, T wet and T dry Corresponds to canopy temperatures when corn plants are transpiring at maximum rates and when they are not transpiring at all; T wet and T dry The 0.5% and 99.5% means of the pure corn canopy temperature histogram were taken, respectively; S102. Calculate the theoretical water stress index t according to the following formula: Where ET a is the actual evapotranspiration, ET p is the potential evapotranspiration; S103. The observation frequency of meteorological data is preset to 30 minutes / time, that is, the average meteorological data is obtained every 30 minutes. The unit of the actual evapotranspiration and potential evapotranspiration calculated is mm / 30 minutes. Then ET a_day =∑ET a_ #(12), where ET a_i is the ET of the i-th 30 minutes in a day a , according to the analysis of daytime conditions, Rn≥0.

8. The method for diagnosing corn water stress on a daily basis based on drone technology according to claim 1, characterized in that: The calculation method of the plant height h of the crop at each growth stage is as follows: The digital surface model DSM0 of the farmland before seedling emergence obtained by the drone is used as the base value, and ground landmarks are selected to calibrate the DSM of each growth period. i , and then get h i =DSM i -DSM0(i=1,2,3,...,i)#(11); After correcting hi with the actual plant height measured on the ground, the final plant height h of crops at each growth stage was obtained using ENVI5.6 software.

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