CN112345467A - Model for estimating physiological parameters of rice by using remote sensing technology and application thereof - Google Patents

Abstract

The invention relates to a model for estimating physiological parameters in a rice growth cycle by using remote sensing data, which is characterized by being established by the following method: acquiring reflectivity information of a rice planting area at each growth period of a rice growth cycle by using a detector, and calculating a vegetation index of the rice planting area at each growth period; obtaining physiological parameters of the rice planting area at a time point corresponding to the vegetation index; and establishing a vegetation index-physiological parameter estimation model according to the vegetation index and the physiological parameter. Compared with the traditional method, the vegetation index-physiological parameter estimation model established by the method has higher correlation and accuracy between the vegetation index and the physiological parameter, so that the monitoring of the growth condition and the physiological data of the rice by utilizing the reflection spectrum data acquired by the flying of an unmanned aerial vehicle carrying a camera and other aircrafts becomes possible, and the development of intelligent agriculture and precision agriculture is strongly promoted.

Description

Model for estimating physiological parameters of rice by using remote sensing technology and application thereof

Technical Field

The invention relates to the field of intelligent agriculture, in particular to a model for estimating physiological parameters in a rice growth cycle by using a remote sensing technology and a method for estimating the physiological parameters of rice by using the model.

Background

In the agricultural production and research process, the acquisition of physiological parameters of crops, such as biomass, chlorophyll content, nitrogen content and the like, is very important for agricultural production research. The most original method is Element Quantitative Analysis (EQA), which can accurately detect the content of corresponding substances in crops, however, EQA is time-consuming and labor-consuming, and is of a damaging nature, and the detection is realized by destroying part or all of plants. Therefore, EQA can only perform sampling investigation, and there is a systematic error that may have some effect on the estimation of physiological parameters of crops in the entire field or test cell.

For this reason, with the development of remote sensing technology, various Vegetation Indexes (VI) derived from vegetation reflection spectra have been developed for evaluating physiological parameters of crops, and some simple measuring devices have been developed. For example, a plant nitrogen measuring instrument, N-pen N110 meter, is a device capable of rapidly measuring the nitrogen content in plants. However, the equipment still has the problems of time and labor waste and sampling investigation.

With the development of unmanned aerial vehicles and the improvement of camera resolution, the combination of the unmanned aerial vehicles and remote sensing technology is gradually used for agricultural production and research. Lukas Prey et al placed the spectrometer approximately 1m above the wheat canopy, and measured the reflectance of the canopy and used to evaluate the corresponding physiological data. A limitation of this approach is that while fine analysis of the crop canopy is possible, no data is available for the entire field or test plot.

Zheng Heng Biao and so on of the Nanjing agriculture university use an unmanned aerial vehicle carried camera to shoot images of crops, and analyze and evaluate physiological parameters of the crops based on the images, and show that certain correlation exists between some physiological parameters and VI calculated by the images shot by the unmanned aerial vehicle. However, the correlation is low on some physiological parameters.

Therefore, new methods are needed to evaluate physiological parameters of plants (e.g., rice) for agricultural production and research.

Disclosure of Invention

In order to solve the problems, an unmanned aerial vehicle flies in the whole growth cycle of the rice, a camera is carried to shoot images of a rice test cell, a vegetation index is calculated according to the obtained images, sampling is carried out in six typical growth periods (a tillering period (TS), a jointing period (JS), a spike differentiation Period (PIS), a booting period (BS), a heading period (FHS) and a milk maturity period (MRS)), and physiological parameters of the rice plant, such as chlorophyll content, nitrogen content and the like, are measured by a traditional method. And establishing a vegetation index-physiological parameter estimation model according to the vegetation index and the physiological parameter of the corresponding time point.

Based on the research, the invention provides a model for estimating physiological parameters in the rice growth cycle by using a remote sensing technology, which is established by the following method:

s1: acquiring reflectivity information of a rice planting area at each growth period of a rice growth cycle by using a detector, and calculating a vegetation index of the rice planting area at each growth period;

s2: obtaining physiological parameters of the rice planting area at a time point corresponding to the vegetation index;

s3: and establishing a vegetation index-physiological parameter estimation model according to the vegetation index obtained at S1 and the physiological parameter obtained at S2.

In a specific embodiment, in S1, the detector is mounted on an unmanned aerial vehicle, and the detector is located at a height of 50-200m above the rice planting area when acquiring the total reflectance information. In the research process, the image or reflectivity data obtained from 50-200m above the plant canopy is used for calculating vegetation indexes such as NDRE and the like, and can be used for establishing a vegetation index-physiological parameter estimation model.

In a preferred embodiment, the vegetation index is NDRE and the physiological parameter is nitrogen content or chlorophyll.

In a preferred embodiment, in S3, the vegetation index and physiological parameter data used for establishing the estimation model are data in the JS period and later the fertility period of the rice, and do not include data in the TS period. Our studies found that NDRE and chlorophyll are severely affected by TS phase dataCorrelation of content and nitrogen content. Correlation R of NDRE and chlorophyll content after TS period data elimination²Reach 0.8127, the correlation R of NDRE and nitrogen content²Up to over 0.60.

Preferably, the physiological parameter is the percentage of nitrogen, and the estimation model is of formula I:

y＝5.754x²+8.167x+0.5752 I

wherein y represents the percentage of nitrogen and x represents the NDRE value.

In another preferred embodiment, the leaf area index LAI is also incorporated into the estimation model. After incorporation of LAI into the estimation model, the correlation R of NDRE with N% LAI²Is increased to

Preferably, the physiological parameter is the percentage of nitrogen, and the estimation model is formula II:

y＝1.05571e^4.5666x II

wherein y represents the percentage of nitrogen and x represents the NDRE value.

In another preferred embodiment, the vegetation index and physiological parameter data used to establish the estimation model are classified according to the length of the rice' S growth cycle in S3.

Preferably, rice varieties having a growth cycle shorter than 90 days and varieties having a growth cycle longer than 90 days are separated for constructing the model. According to the fact that the growth cycle is longer or shorter than 90 days, the rice is divided into late maturity combined early maturity groups, and the results show that the regression coefficient of each group after grouping is obviously higher than the mixed regression coefficient before grouping.

The invention also provides a method for estimating physiological parameters in the rice growth cycle, which comprises the following steps:

1) acquiring the reflectivity information of a rice planting area by using a detector, and calculating the vegetation index of the rice planting area;

2) and substituting the vegetation index into the method model, and calculating to obtain the physiological parameter.

Compared with the traditional method, the vegetation index-physiological parameter estimation model established by the method has higher correlation and accuracy between the vegetation index and the physiological parameter, so that the monitoring of the growth condition and the physiological data of the rice by utilizing the reflection spectrum data acquired by the flying of an unmanned aerial vehicle carrying a camera and other aircrafts becomes possible, and the development of intelligent agriculture and precision agriculture is strongly promoted.

Drawings

Fig. 1 is RGB and NDRE images of 6 growth periods of 51 rice varieties collected using an unmanned aerial vehicle, wherein: a is RGB image of 6 birth periods, a is TS period, b is JS period, c is PIS period, d is BS period, e is FHS period, f is MRS period; b is an NDRE image of 6 birth periods, a is TS period, B is JS period, c is PIS period, d is BS period, e is FHS period, and f is MRS period.

FIG. 2 is a graph of 6 fertility data relationships for 51 rice varieties and constructed regression models, in which: a is the relationship between SPAD and NDRE in 6 growth periods, and n is 306; b is a regression model between SPAD and NDRE constructed by 5 fertility data except TS stage, and R is²Is more than 0.81, n is 255; c is the relationship between N% and NDRE for 6 growth periods, N306; d is a regression model between SPAD and NDRE constructed by 5 fertility data except TS stage, R²＞0.61，n＝255。

FIG. 3 is a graph of the relationship between SPAD, N% and NDRE during FHS, where: a is the relationship between SPAD and NDRE in all rice varieties (n is 51); b is the relationship between SPAD and NDRE in early-maturing (EM) rice cultivars (n ═ 34); c is the relationship between SPAD and NDRE in late-maturing (LM) rice cultivars (n ═ 17); d is the relationship between N% and NDRE in all rice varieties (N ═ 51); e is the relationship between N% and NDRE in early-maturing (EM) rice varieties (N ═ 34); f is the relationship between N% and NDRE in late-maturing (LM) rice cultivars (N ═ 17). P < 0.001.

Fig. 4 is a non-linear regression model between N% LAI and NDRE constructed from data from 6 growth stages of 42 rice varieties grown in south hainan.

Fig. 5 is a comparison of nitrogen content variation curves measured using EQA method (a) and estimated using model i (b) and model ii (c), respectively, from images acquired by unmanned aerial vehicle and calculated NDRE values for the full growth cycle of 51 rice varieties.

FIG. 6 is the EQA testNitrogen content (N%_-AM) With ModelI (N%_-RS) And ModelII (N%. LAI)_-RS) A comparison between the estimated nitrogen contents, wherein: a is the curve of the change of nitrogen content in 6 fertility periods of LY 9348; b is N% of 51 rice varieties_-AMA statistical block diagram of (1); c is N% of 51 rice varieties_-RSA statistical block diagram of (1); d is N% LAI of 51 rice varieties_-RSStatistical block diagram of (1).

Detailed Description

1. Plant material and planting

From 3000 genome projects, 50 varieties (indica rice, australian rice and varieties between the two) with higher NUE were selected, and 51 rice varieties (table 1) were added, and planted in the experimental and research base of wuhan university rice (30.3756 ° N, 114.7448 ° E) in north hubei. The Huzhou rice is sown in 2017 in 5-month and 10-day rice and transplanted in 5-month and 31-day rice. The rice in the water of the hilly side germinates in 12-month and 10-month period in 2017, and is transplanted in 1-month and 6-month period in 2018.

Variety information of 151 rice varieties in Table

41 indica rice varieties, one purple rice variety and 42 rice varieties (Table 2) are selected from Chinese breeding projects, and are planted in Wuhan university hybrid rice experiment and research bases (18 degrees 03 '147.1' N,110 degrees 03 '34.9' E) in Hainan Ling water. The rice is sown in 2017, 12 and 10 days, and transplanted in 2018, 1 and 5 days.

Variety information of Table 242 Rice varieties

The rice is planted at a density of 225000 plants per hectare, and the total growth time is 6 to 7 months, which varies depending on the variety. 60 plants are planted in each variety, 10 plants are planted in one row, 6 rows are planted in total, the row spacing is 20cm, and the plant spacing is 16 cm. And 1 line is vacant every 6 lines to facilitate variety distinction and UAV information processing.

375Kg of compound fertilizer (the ratio of nitrogen, phosphorus and potassium is 15-15-15) is applied per hectare for conventional paddy field management. At each development stage of each experiment, a UAV drone was arranged to acquire images of all rice fields, and each field was repeatedly measured 5 times.

2. Data collection

The whole growth cycle of rice can be divided into 6 typical developmental stages, including: tillering Stage (TS), Jointing Stage (JS), ear differentiation stage (PIS), Booting Stage (BS), heading stage (FHS) and milk stage (MRS).

At each developmental stage, leaf samples were taken for accurate nitrogen and chlorophyll content measurements. 3 functional leaves were counted down from the top xyphoid leaf for measuring chlorophyll content and nitrogen content. The average number of SPADs and the average number of nitrogen contents (N%) were recorded in 3 replicates per breed.

The nitrogen content was measured using a nitrogen measuring instrument N-Pen N110, and three plants were selected at each developmental stage to collect leaf samples for measurement. Leaves with the age of 1.5 leaves (the length is 2 times of the length of the sword leaves) are taken at the stage before the sword leaves grow out, and the second leaf below the sword leaves is taken at the stage after the sword leaves grow out. NDGI ═ (R780-R560)/(R780-R560). Chlorophyll content was measured using a Soil Plant Analytical Development (SPAD) chlorophyll content meter (SPAD-502).

In this study, total nitrogen content values (three replicates) were measured for each variety at each developmental stage, for a total of 306 total nitrogen value data and chlorophyll content data for 51 rice varieties in ozhou, and for a total of 252 total nitrogen value data and chlorophyll content data for 42 rice varieties in the water of the memorial province.

Elemental Quantitative Analysis (EQA) was also used in this study to determine nitrogen content as follows: selecting 3 plants, collecting functional leaves, oven drying at 80 deg.C to constant weight, grinding, sieving with 100 mesh sieve, and detecting nitrogen content. The average of the 3 plant data was used as the accurate leaf nitrogen content value for the corresponding rice variety.

Leaf Area Index (LAI) was also collected in this study as follows: randomly selected 5 plants were used to measure leaf area index. If more than 50% of the leaves are yellow, the leaves are judged to be yellow and removed. Since this study performed destructive measurements of rice material and multiple growing periods required sampling tests, 2 plants with the most green leaves were selected from the 5 plants described above as representative samples for each rice variety and each growing period. The entire plant of both plants, including all tillers, was rooted. All green leaves were peeled off and scanned for Leaf area (Leaf area meter LI-3100C). The average leaf area of all leaves of these two plants was taken as a representative value of the Leaf Area (LA) of the individual plant. Considering a plant density (d) of 1 square meter, LAI ═ LA × d.

3. Crop canopy diffuse reflectance spectrum collection

Crop diffuse reflectance spectra were measured by an ASD field spec Pro FR spectrometer. Receipts were collected from 1.0m directly above the crop canopy, selected between 10am-2pm on sunny days, once every 5 days. Each test cell was measured 5 times repeatedly, taking the average crop block canopy spectral reflectance. The influence of instrument noise is removed through standard white board paper in time correction, and spectrum data of 1301-2500nm wave band with low signal-to-noise ratio is removed.

4. Unmanned Aerial Vehicle (UAV) flight and image acquisition

Images of the target study area were acquired using the Mini-MCA system installed on the unmanned aerial vehicle (S1000, majiang), and images were collected every five days from the time of transplantation until the crop was mature. The Mini-MCA comprises an array of 12 individual miniature digital cameras. 10bit SXGA data can be generated for each sensor channel, and the image resolution can reach 1m/130 hectare. Each camera is equipped with a custom bandpass filter centered at a wavelength of 490,520,550,570,670,680,700,720,800,850,900 or 950nm, respectively. After UAV image acquisition, corresponding field measurements are made in situ immediately.

The MCA system is connected to the UAV through a gimbal frame to prevent the influence of the movement of the UAV, and the inaccurate matching effect of the cameras is controlled through 12 cameras matched before flying. Each UAV flight is performed in sunny, cloudless sky conditions for a time between 10am and 2pm, which is the minimum change in the solar altitude. In the Hubei experiment, the UAV flight height is 50m above the target block, and the spatial resolution is about 2.7 cm. 42 rice variety experiments, the UAV flight height is 200m above the target block, and the spatial resolution is about 10.8 cm.

The image digital quantization value (DN) is converted to a surface reflectivity (ρ λ) using an empirical linear correction method. Image radiance correction was performed through a standard of 6 ground calibration targets placed in the camera field of view first on each flight. The area of the study and all ground calibration targets are contained in the same photograph. Herein, the ground calibration target provides relatively stable reflectivities of 0.03,0.12,0.24,0.36,0.56and 0.80 for visible to near infrared wavelengths, respectively. Since a linear relationship is assumed between DN and ρ λ, the reflectance equation for a rice variety can be:

ρ_λ＝DN_λ×Gain_λ+offset_λ

(λ＝490，520,550,570,670，680，700,720,800,850,900and 900nm) (1)

where ρ is_λAnd DN_λDigitally quantizes the surface reflectivity at wavelength λ and the image for the given pixel. Gain λ and Offset λ are the camera Gain and Offset of the camera at wavelength λ. Gain_λAnd Offset_λThe p value and DN value may be calculated using a least squares method.

5. Statistical analysis and Vegetative Index calculation

Data analysis and Statistical description were performed by IBM SPSS Statistics (Statistical Product and Service Solutions 22.0, IBM, Armonk, NY, United States). GraphPad software (Version 5.0., Harvey Motulsky) was used&Arthur Christopoulos, San Diego, California, USA). And (3) carrying out statistical evaluation on the data sets of nitrogen content (N%), chlorophyll content (SPAD) and Leaf Area Index (LAI) according to requirements, and displaying normal distribution. Using poisson phasesThe correlation coefficient (r) is taken as the result of the correlation analysis. Analysis and comparison of corrected R²And p-value, regression analysis was performed. The best fit curve is converted to an equation as a regression model to represent the correlation between N%, SPAD, LAI × N% and Normalized Difference Red Edge (NDRE) or other Vegetation Index (VI). The calculation formula of each VI is shown in table 3.

TABLE 3 VI calculation formulas and references

6. Correlation between several VI and Nitrogen content and chlorophyll

The former collected canopy spectral reflectance data using a spectrometer 1m above the rice and analyzed data for 6 key stages in the growth cycle to determine which growth stage was the best stage for the selected VI for assessment of chlorophyll and nitrogen content.

The above 6 stages of 42 rice varieties were evaluated by correlation analysis and regression analysis (table 4). The association of NDRE with chlorophyll is generally better than the association with nitrogen at each growth stage. However, NDRE showed correlation with chlorophyll (R) in the BS phase²0.0798) and nitrogen content (R)²0.0003). Furthermore, for chlorophyll, NDRE is in MRS phase (R)²0.4647) and JS period (R)²0.4627) is better than the PIS phase (R)²0.2748) and TS period (R)²0.1614). For the nitrogen content, the other 5 periods except FHS showed poor correlation with NDRE (R)²Less than 0.4). FHS shows a strong correlation with chlorophyll (0.6557) and nitrogen content (0.4919). As FHS is the period when the mature morphological structure and basic biomass of rice plants are completely established, the rapid morphology in the plant development in the TS period and the JS period can be reasonably inferred for VI analysis in a short-distance range by a chromatograph measuring methodChanges, scion and inflorescence inception in the PIS and BS phases, and canopy and grain color changes in the MRS phase may interfere with reflectance data collection.

TABLE 46 regression analysis of SPAD, N% and NDRE in the growth phases

*：p<0.05；**：p<0.01；***：p<0.001。

To determine the relationship between rice chlorophyll content, nitrogen accumulation and the chromatographic features captured by UAV, correlation analysis was performed on chlorophyll content, nitrogen content and each VI (CIgreen, ciededge, NDRE, NDVI and NDGI) at FHS stage (table 5). The chromatographic features of the VI were calculated from the canopy reflectivities at the different bands (550nm,560nm,670nm,720nm,780nm and 800 nm). Overall, all VI showed stronger correlation with chlorophyll (0.5-0.65) than nitrogen content (0.29-0.49), but for each VI, the correlation pattern with chlorophyll and nitrogen content was the same: NDRE showed the strongest correlation and NDVI showed the weakest correlation. For chlorophyll correlation, NDGI (R)²＝0.6146)＞CIrededge(R²＝0.5953)＞CIgreen(R²0.5171). For nitrogen content, CIrededge (R)²＝0.4634)＞NDGI(R²＝0.4555)＞CIgreen(R²0.4083). Thus, NDRE is the VI that is optimal for assessing chlorophyll and nitrogen content.

TABLE 5 Poisson's correlation coefficient of SPAD and N% with 5 VI, respectively

*：p<0.05；**：p<0.01；***：p<0.001。

7. NDRE real-time mode can be used for reversibly analyzing growth differences among rice varieties

To analyze the entire growth cycle from the transplant period to the harvest period, 51 rice varieties were planted in rectangular cells (1.2m × 1.6m) and image data were collected using UAVs every 5-7 days (as determined by sunlight conditions). The RGB image (fig. 1A) and NDRE (fig. 1B) show images for 6 epochs. A purple rice variety (table 3,: #9Malai Hong) was included as an internal control in the test group, and since it contained higher anthocyanin than chlorophyll, it was advantageous to distinguish it from other varieties in reflectance characteristics at the time of data processing. NDRE values are between 1 for cool blue 0 and warm red, so warmer colors represent higher chlorophyll content, nitrogen accumulation and photosynthetic rate, and colder colors, in contrast. In the birth cycles of all varieties, from TS period, JS period to PIS period, the NDRE value gradually increases, and rapidly decreases after BS period.

The NDRE values for each growth period for 51 rice varieties ranged as follows: TS stage (0.4121-0.5473), JS stage (0.4555-0.6173), PIS stage (0.3762-0.5762), BS stage (0.3506-0.5394), FHS stage (0.1931-0.4134), MRS stage (0.1487-0.3343). Among them, TS, JS, PIS and BS observed the highest NDRE in rice variety #33(Qingtai Ai), FHS and MRS observed the highest NDRE in rice variety #1(LY 9348). The lowest NDRE was observed in #17(ARC11777, TS), #4(Luohong 4B, JS and PIS), #16(MaMaGu, BS), #7(ZuiHou, FHS) and #28(MoMi, MRS).

High NDRE values above 0.5 were observed for all rice varieties in JS, PIS and BS phases, which are related to rice development, since JS phase is a period during vegetative growth when biomass is rapidly accumulated and PIS/BS phase is a period of transformation from vegetative to reproductive growth, which indicates that leaf and stem growth requires more energy than later flower and seed production. However, the exact maximum NDRE value, the time to reach the maximum NDRE value and the time to fall back from the maximum NDRE value vary widely among 51 rice varieties at the same time or at different times for the same variety. This indicates that chlorophyll content, photosynthetic rate, nitrogen uptake, transport, accumulation and ability to maintain nitrogen levels vary among varieties and at different times throughout the reproductive cycle. Thus, NDRE can be used as a parameter to measure and evaluate the real-time changes in chlorophyll and nitrogen accumulation.

8. Optimization of prediction model and establishment of model I

To determine why the association of NDRE with chlorophyll and nitrogen varies during different growth periods, scatter plots were drawn on a total of 306 data from 6 growth periods for 51 rice varieties for analysis (fig. 2A and C). After transplanting, rice plants develop from plantlets (40cm high, 5-6 leaves) to large plants (120cm, 16-18 leaves). Growth of biomass and canopy modification is based on accumulation of chlorophyll and nitrogen.

In general, chlorophyll and nitrogen content is positively correlated with biomass prior to maturation. Thus, changes in chlorophyll and nitrogen content at each time period are expected to be as follows: TS (transport stream)<JS/PIS/BS. However, after maturation, chlorophyll begins to break down and the leaves age rapidly and yellow. Thus, chlorophyll and nitrogen are expected to exhibit a decline pattern from JS, PIS, BS to FHS, MRS. However, unlike expectations, TS was higher in both chlorophyll and nitrogen than other periods (fig. 2A and C). Considering the small biomass, narrow leaves, small plants at this stage, chlorophyll and nitrogen content may be erroneously overestimated. Since the TS stage plants are small, the reflectivity is actually a mixed characteristic of the plants and the paddy field water body. This value is overestimated because NDRE is calculated from the reflectance characteristics at 720nm and 800nm, and the water surface not covered by the plants increases the reflectance. Based on the inference, the data in TS period is removed, a linear regression model is built again, and the NDRE and the chlorophyll have better correlation R²NDRE also has a better linear relationship R with N ═ 0.8127²Above 0.60 (fig. 2B and D). Since N% is measured by the quantitative elemental analysis (EQA) method, we consider the regression model (y ═ 5.754 x)²+8.167x +0.5752) was a predictive model based on actual measurements as model i for further analysis below.

In conclusion, the data of the birth period (JS period and later) after the canopy was sufficiently covered with the water surfaceFor data processing, NDRE has a better correlation with chlorophyll and nitrogen content. R of the established NDRE and nitrogen content model²The improvement is remarkable.

9. Growth cycle length effects NDRE accuracy in estimation of chlorophyll and nitrogen

Due to the difference in the length of the growing cycle of 51 rice varieties, the standard for distinguishing the middle rice from the early rice and the late rice is adopted in the longer growing cycle. The growing cycle of medium rice is generally longer than 100 days from sowing to seed maturity, while early and late rice is generally shorter than 90 days. Therefore, the interval from sowing to maturity was set at 100 days as a cutoff value, and 51 rice varieties were divided into an early maturity group (EM) and a late maturity group (LM). To determine whether the length of the reproductive cycle affects the accuracy of chlorophyll and nitrogen content estimation, a linear regression analysis was performed on NDRE during FHS (fig. 3). The Regression Coefficient (RC) of each group after the grouping is raised. The RC between NDRE and chlorophyll rose from 0.6557 (mixed) to 0.7796(EM) and 0.7301 (LM). The RC between NDRE and nitrogen content ranges from 0.4919 (mixed) to 0.6152(EM) and 0.6282 (LM). This indicates that the maturation time affects the accuracy of the estimation of nitrogen content and chlorophyll in the reflectance profile of the meal.

From an agricultural perspective, the length of the reproductive cycle can create differences in biomass accumulation, leaf color, nitrogen flow and canopy structure, and thus can result in differences in reflectivity, which reduces the correlation between nitrogen content and NDRE (less than 0.5). By grouping varieties with similar growth cycle lengths together for group analysis, the correlation can be improved (above 0.6). From an agricultural perspective and phenomics research, taking into account the length of the growth cycle makes a better analysis when it is necessary to simultaneously estimate the nitrogen accumulation of hundreds or thousands of rice varieties.

10. The influence of LAI on the dependence of NDRE on nitrogen content

To determine whether canopy architecture is a key factor affecting the correlation of nitrogen content with NDRE, a training dataset (2017, table 5) of 42 rice varieties was used for analysis. Leaf Area Index (LAI) and nitrogen content (EQA) were measured and NDRE calculated from UAV data. N% LAI was used instead of N% as a parameter for correlation analysis. The nonlinear model y is obtained as 1.05571e^4.5666x(modelⅡ)，R²Was 0.86 (FIG. 4). The correlation of the Model is better than that of Model I. Thus, the above experiments demonstrate that NDRE appears to be strongly correlated with nitrogen content when canopy structures such as LAI are taken into account.

11. Application of Model I and Model II in monitoring nitrogen content of rice and reliability thereof

To test the performance and accuracy of the selection of high NUE phenotypic assays in a larger population of rice by model i and model ii, we further analyzed 6-season data for 51 rice varieties. Nitrogen content estimation by EQA showed that LY9348 maintained higher nitrogen content in 51 rice varieties for all 6 periods, but was higher than other rice varieties in MRS period (fig. 5A). Both model i and model ii detected LY9348 with the highest nitrogen level during FHS and MRS (fig. 5B and C). However, the nitrogen content change curves of 51 rice varieties TS, JS and PIS obtained by Model I in the period from TS to BS are flatter than that of Model II, so that Model II seems to have better detection sensitivity and accuracy.

To further evaluate which model is more suitable for detecting nitrogen accumulation, the nitrogen content measurement and estimation curves across the entire fertility cycle of LY9348 were placed in one graph (fig. 6A). Nitrogen estimation curve (N% for ModelI)_-RS) Measured with EQA (N%)_-AM) The fit was good, only underestimated during JS period. ModelII (N%. LAI)_-RS) The estimated nitrogen content is 2-4 times that of Model I and the fluctuations between different growth periods are greater than with the Model I and EQA methods. Statistics showed that nitrogen content for all growth periods measured by EQA was evenly distributed around the median (fig. 6B). However, ModelI is more tightly distributed at TS, JS, PIS and BS, and more loosely distributed at FHS and MRS (FIG. 6C); ModelII exhibits the reverse mode to ModelI (FIG. 6D). Thus, ModelII is better suited for the earlier four epochs of detection, while ModelI is better suited for the later two epochs of detection. The nitrogen content in rice can be estimated using different models at different times.

Interestingly, we measured the nitrogen content using a nitrogen meter N-pen N110 meter and plotted the nitrogen content profile, which shows that although the nitrogen content measured by the nitrogen meter has a high correlation with the nitrogen content measured by EQASex (R)²0.68-0.89), but it failed to distinguish LY9348 from other varieties at FHS and MRS. This may be because the saturation of the reflectance signature measured and estimated by a palm nitrogen meter cannot detect nitrogen contents below 2%.

Our experiments also demonstrated that obtaining the height of the reflectivity (50-200m) did not affect the estimation of the nitrogen content. Moreover, in either ModelI or ModelII, we do not differentiate between the leaves and the ear for nitrogen content estimation, and only use the total reflectance characteristics of each variety for a blended estimation of nitrogen content.

It should be noted that although we try to fit the data of modelI and ModelII to the EQA measurement, we should point out that the EQA measurement is a sampling measurement with systematic error, and modelI and II are macroscopic data estimation results, we cannot in fact completely determine whether the error between the modelI and II estimation results and the true nitrogen content is large or the error between the EQA measurement results and the true nitrogen content is large. This systematic error may be the reason why the EQA method fails to distinguish LY9348 of high NUE from the population.

In any case, the rice nitrogen content is successfully estimated by using two estimation models respectively, and the two estimation models can distinguish the NUE rice variety LY9348 from a higher NUE rice variety group, which shows the reliability of the method.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A model for estimating physiological parameters in a rice growth cycle by using a remote sensing technology is characterized by being established by the following method:

s1: acquiring reflectivity information of a rice planting area at each growth period of a rice growth cycle by using a detector, and calculating a vegetation index of the rice planting area at each growth period;

s2: obtaining physiological parameters of the rice planting area at a time point corresponding to the vegetation index;

s3: and establishing a vegetation index-physiological parameter estimation model according to the vegetation index obtained at S1 and the physiological parameter obtained at S2.

2. The model of claim 1, wherein in S1, the probe is mounted on a drone, and the probe is at a height of 50-200m above the rice planting area when acquiring the reflectance information.

3. The model of claim 1, wherein the vegetation index is NDRE and the physiological parameter is nitrogen content or chlorophyll.

4. The model of claim 3, wherein the vegetation index and physiological parameter data used in the construction of the model in S3 are data in the JS period and later the fertility period of rice, and do not include data in the TS period.

5. The model of claim 4, wherein the physiological parameter is percent nitrogen and the estimation model is of formula I:

y＝5.754x²+8.167x+0.5752 I

wherein y represents the percentage of nitrogen and x represents the NDRE value.

6. The model of claim 4, wherein in S3, a leaf area index LAI is also incorporated into the model.

7. The model of claim 6, wherein the physiological parameter is a percentage of nitrogen, and the model is of formula II:

y＝1.05571e^4.5666x II

wherein y represents the percentage of nitrogen and x represents the NDRE value.

8. The model of claim 3, wherein the vegetation index and physiological parameter data used to construct the model are categorized according to the length of the rice' S growth cycle in S3.

9. The model of claim 8, wherein rice varieties having a growth cycle shorter than 90 days and varieties having a growth cycle longer than 90 days are separated for constructing the model.

10. A method for estimating physiological parameters in a rice growth cycle, comprising the steps of:

1) acquiring the reflectivity information of a rice planting area by using a detector, and calculating the vegetation index of the rice planting area;

2) substituting the vegetation index into the model of any one of claims 1-9, calculating the physiological parameter.

Images