CN115062863B - Apple flowering phase prediction method based on crop reference curve and accumulated temperature correction - Google Patents
Apple flowering phase prediction method based on crop reference curve and accumulated temperature correction Download PDFInfo
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Abstract
The invention discloses an initial flowering phase prediction method of apples based on crop reference curves and accumulated temperature correction, which specifically comprises the following steps: step 1: establishing a crop reference curve library, and extracting crop reference curves according to ground sample points; step 2: recording the accumulated temperature before the historical flowering phase; step 3: calculating a high spatial resolution clear pixel vegetation index; step 4: fitting the high-spatial-resolution vegetation index by using a reference curve, and fitting the high-resolution vegetation index in the historical years with a crop reference curve; step 5: and calculating a predicted flowering period. According to the characteristics that the accumulated temperature has an important effect on fruit tree climates and the vegetation index has an obvious indication effect on the climates, the invention provides an apple initial flowering phase prediction method based on a crop reference curve and accumulated temperature correction, which is suitable for predicting the initial flowering phase of an apple tree with high spatial resolution in a region with a complex planting structure.
Description
Technical Field
The invention relates to the technical field of agricultural remote sensing, in particular to an apple flowering phase prediction method based on crop reference curves and accumulated temperature correction, which is suitable for large-scale high-precision flowering phase prediction research.
Background
The planting area and the yield of apples in China are respectively increased to 42.24% and 45.54% of the world, the planting area of apples is first in the world, but the single yield of apples is only 18.94 tons/hectare, and the apples are first in the world. Apples are important cash crops in Shaanxi province, and are one of important prop industries for rural development and income increasing of farmers in Shaanxi province. The loess plateau region in Guanzhong is suitable for the growth of apple trees, so that the region becomes the main production region of Shanxi high-quality apples. The initial flowering period of apples is an important weather index for apple fruit development, and the weather conditions during the period have profound effects on the formation and development of the fruits, but due to the characteristic of temperate continental weather in the area, the loess plateau has complex topography and is fragile in weather conditions, and the weather conditions are extremely easy to be influenced by cold flow in the flowering period of apples, so that low-temperature weather is easy to occur. The low-temperature weather can cause the frost damage of the flowering phase of the fruit tree to a great extent, if the control is not timely carried out, the apple flower buds are frozen or pollination is not affected because the insect activity is not high, so that the condition of poor quality or large-area yield reduction of the apples is caused, and the frost damage of the flowering phase becomes one of main meteorological disasters affecting the yield and quality of the apples. Meanwhile, accurate flowering phase information has important value for field management such as pest control, fertilization and the like, and provides reference information for locally developing tourism agricultural activities. Therefore, the method for accurately monitoring the initial flowering phase of apples and developing the flowering phase forecast service has guiding significance for agricultural production activities of fruit farmers, provides references for preventing and controlling freezing injury for agricultural management departments, ensures the quality and yield of apples, provides data support for futures trading, insurance and the like, and provides guarantee for healthy development of apple industry.
At present, the fruit tree weathers information prediction method mainly comprises a field observation method, a model simulation method and a satellite remote sensing method.
1) The field observation method is a traditional method for monitoring the fruit tree in the period of waiting time, mainly uses eyes for observation, and uses hands for recording, and the obtained observation data is the first-hand data of the fruit tree in the period of waiting time. The field observation method is simple and feasible, the result is accurate and reliable, subjective factors are doped in the observation, the observation coverage area is small, and a large amount of manpower, material resources and time are consumed for carrying out large-area observation.
2) The model simulation method mainly comprises a statistical analysis model and a process mechanism model mainly based on the analysis of the growth rule of the fruit tree. The statistical analysis method is a research method for predicting the fruit tree weathered period by analyzing the change trend of the fruit tree weathered period and the correlation relation of the influence factors of the fruit tree weathered period in a period of time. The process mechanism model is to assume that the growth and development of the fruit trees are mainly controlled by factors such as temperature, illumination and the like, usually consider the dormancy stage and the de-dormancy stage of the fruit trees, and only the accumulated temperature of the fruit trees reaches the critical value required by the occurrence of a climatic event. The temperature and illumination data are often large in scale, and accurate prediction with high resolution is difficult to realize.
3) The satellite remote sensing method is mainly determined by the tissue structure, biochemical components and morphological characteristics of the fruit tree according to the spectral characteristics of the fruit tree, and mainly comprises leaf color, cell structure, plant moisture content and the like, wherein the characteristics are closely related to the fruit tree waiting period. The satellite-borne multispectral sensor can observe vegetation continuously for multiple times, so that the defect of discontinuous observation of traditional vegetation weathers is overcome, and the observation of vegetation weathers in a large scale is possible. The physiological and biochemical characteristics of the fruit trees in different climates can influence the absorption, reflection and transmission of the fruit trees to the spectrum, so that a certain difference is generated in a spectrum response curve, the change of the vegetation spectrum is obtained through different sensors, and the spectrum index of the change of the climates is calculated, so that the climates are monitored and predicted. There are two general methods for the scholars at home and abroad to predict the crop climate based on remote sensing: one is to track the growing process of crops based on remote sensing data (such as vegetation index) of time series, confirm the crop weathers according to the characteristic change in the growing process of crops, for example after fitting or filtering the time series data, judge the time node of the weathers according to the curve form of vegetation index time series in different weathers by using methods such as threshold value, derivation, moving average, etc., realize the monitoring or prediction to the crop weathers; the other is to quantify physiological and biochemical indicators in the crop growth process through remote sensing data, for example, inversion of crop LAI, plant height and the like through remote sensing data, and related researches are carried out through calculation of microwave vegetation optical thickness, sunlight-induced chlorophyll fluorescence and the like, and the physiological and biochemical parameters of fruit trees in different climatic periods are quantified to realize monitoring and prediction of the crop climatic periods.
The satellite remote sensing method is suitable for large-scale physical prediction, but because the change of the vegetation index of apples before the initial flowering period is difficult to accurately reflect on satellite scale and can bring larger error when being used for direct prediction, the method detects historical apple initial flowering period data, and corrects the historical initial flowering period detection result calculated based on the vegetation index by means of predicting the current annual accumulated temperature so as to achieve the aim of accurately predicting the initial flowering period of apples with large scale and high resolution.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for predicting the initial flowering period of apples based on crop reference curves and accumulated temperature correction aiming at the defects in the prior art; the crop reference curve is combined with the multisource remote sensing data, the translation amount of the vegetation index in time is calculated, and the meteorological data is used for improving the prediction result so as to predict the initial flowering period of the apples with high precision.
The technical scheme of the invention is as follows:
in order to solve the technical problems, the invention provides a method for predicting the initial flowering period of apples based on crop reference curve and accumulated temperature correction
An apple flowering phase prediction method based on crop reference curves and accumulation temperature correction comprises the following steps: step 1: establishing a crop reference curve library, establishing a crop extraction crop reference curve according to ground sample points, and recording the initial flowering period t of the sample points 0 The method comprises the steps of carrying out a first treatment on the surface of the Step 2: recording the previous accumulation temperature of the historical flowering period, and selecting an optimal flowering period accumulation temperature correction threshold according to historical meteorological data; step 3: calculating a vegetation index with high spatial resolution, preprocessing a remote sensing image with high resolution, and calculating a vegetation index of a clear sky pixel of the remote sensing image; step 4: fitting the high-spatial-resolution vegetation index by using a reference curve, fitting the high-resolution vegetation index of historical years with a crop reference curve, and establishing a parameter delta t 0 For recording the translation amount of the curve fitted by different pixels relative to the reference curve in time according to the translation amount delta t 0 An initial flowering period t with the reference curve 0 Calculating to obtain initial detection flowering period t 0 +Δt 0 The method comprises the steps of carrying out a first treatment on the surface of the Step 5: calculating a predicted flowering period through accumulation temperature correction, and calculating a date difference delta t when the accumulation temperature is greater than 415 ℃ between two years by using meteorological data, so that the predicted flowering period of the pixel is t 0 +Δt 0 +Δt。
The method, the step 1 comprises the following steps:
step A: the spatial distribution of fruit trees in a field investigation region is characterized in that vector boundaries of the fruit trees are sketched in high-definition satellite images, and the initial flowering period t of the fruit trees in different regions is recorded 0 ；
And (B) step (B): carrying out preprocessing such as re-projection on MODIS data with low spatial resolution to ensure that a geographic coordinate system is consistent with a projection coordinate system and the vector boundary of apples, then calculating to obtain the NDVI of the MODIS data, combining the vector boundary of fruit trees, searching for pure apple pixels in the MODIS data, and extracting the NDVI time sequence curve of the pixels;
step C: extracting a plurality of NDVI time sequence curves, removing abnormal values in the curves, interpolating blank values by adopting a Savitzky-Golay filtering method, and taking the interpolated curves as reference curves, wherein a set of the plurality of reference curves is an apple reference curve library.
The method, the step 2 comprises the following steps:
step D: and counting the accumulated temperature before the initial flowering period of the historical ground sample points, and finding out an optimal accumulated temperature value, so that the average relative error of each sample point is the smallest when the optimal accumulated temperature is used as the initial flowering period accumulated temperature value of the apples in different areas in different years.
The method, the step 3 comprises the following steps:
step E: based on the clear sky Sentinel-2 surface reflectivity data, NDVI data with 10m spatial resolution is obtained through calculation, and the NDVI is masked by using a fruit tree vector boundary, so that only the NDVI data of the fruit tree are reserved.
The method, the step 4 comprises the following steps:
step F: analyzing the determining coefficient R of all image pixels of the Sentinel-2 time sequence NDVI data obtained by the calculation in the step E and all curves in the MODIS crop reference curve library obtained in the step C 2 Will determine the coefficient R 2 The largest curve is used as the best reference curve for the image pixels of the Sentinel-2 time series NDVI data, and then the Sentinel-2 time in step E is further fitted by the following formulaAnd obtaining a sensor-2 fitting curve by the sequence NDVI data and the MODIS optimal reference curve.
L(x)＝a×M(x+Δt 0 )+b
Wherein x represents a day of the year, L (x) represents a Sentinel-2 time series NDVI curve function, M (x) is a MODIS optimal reference curve, Δt 0 Representing the possible time offset between the two curves, ranging between + -30 days, alpha and b being the fitting parameters calculated using the least squares method.
Step G: when the curve is fitted according to the step F, the time offset delta t of each pixel relative to the reference curve is calculated 0 At this time, according to the initial period t of the reference curve 0 Obtaining the detected flowering period delta t of the historical year of the pixel 0 +t 0 。
The method, the step 5 comprises the following steps:
step H: according to the weather forecast data of the current year and the historical weather data of the detected year, calculating the date t when the predicted year reaches the optimal heat accumulation temperature 1 The method comprises the steps of carrying out a first treatment on the surface of the At the same time, calculating the date t when the detection year reaches the optimal heat accumulation temperature 2 If the space range is larger, regional statistics is needed, and the date difference delta t=t of reaching the optimal accumulation temperature in two years is calculated at the moment 1 -t 2 ；
Step I: and C, detecting the flowering period delta t of the initial test obtained in the step G according to the optimal accumulation temperature threshold date difference delta t obtained in the step H 0 +t 0 Correction is performed to obtain a predicted flowering period t=t 0 +Δt 0 +Δt。
According to the characteristics that the accumulated temperature has an important effect on fruit tree climates and the vegetation index has an obvious indication effect on the climates, the invention provides an apple initial flowering phase prediction method based on a crop reference curve and accumulated temperature correction, which is suitable for predicting the initial flowering phase of an apple tree with high spatial resolution in a region with a complex planting structure. Searching pure pixels of MODIS data by using ground survey data, establishing a crop reference curve library, and determining an optimal heat accumulation threshold of an apple tree reaching an initial flowering period through historical data; obtaining an initial fitting curve by comparing the correlation between the MODIS crop reference curve and the Sentinel-2 original NDVI value, reserving the NDVI effective value of the original Sentinel data to the greatest extent by linear interpolation, function fitting and other methods, further reducing the overfitting phenomenon of the initial curve to obtain a final fitting curve, recording fitting parameters, and determining the detection florescence of historical years according to the initial florescence of the selected optimal reference curve and the time offset of the fitting curve; and calculating the time offset of the historical year and the predicted year of the apple tree reaching the optimal accumulation temperature threshold value of the initial flowering period through weather forecast data, and correcting the initial predicted flowering period by using the offset, so as to obtain the final predicted flowering period. According to the method, the indication effect of the vegetation index on the vegetation is comprehensively considered, meanwhile, the accumulated temperature is used for carrying out secondary correction, the time-space resolution advantage of remote sensing data can be fully utilized, meanwhile, the influence of meteorological data on the vegetation is considered, and the generated apple initial flowering phase prediction drawing with high time-space resolution can be applied to apple initial flowering phase prediction of a complex planting structure.
Drawings
FIG. 1 is a flow chart of an algorithm implementation;
FIG. 2 is a schematic diagram of a theory of flowering phase detection based on crop reference curves; solid line: crop reference curves; dots: a vegetation index corresponding to the date on the high resolution image; Δt (delta t) 0 : deviations in the climate from the reference curve and the year of detection after curve fitting.
FIG. 3 is a graph showing the end-of-life prediction results of the study area 2021;
Detailed Description
The present invention will be described in detail with reference to specific examples.
Example 1:
an initial flowering phase prediction method of apples based on crop reference curves and accumulated temperature correction comprises the following steps:
a: the research area of this example is located in Yanan city 6 county, shaanxi province, including: luochuan county, huang Ling county, fu county, change county, yichuan county and Huanglong county, which is the main area for planting Shaanxi apples, and the area of the apple in the Yan Security city in 2019 is 323.65 ten thousand mu. Google high definition satellite combined according to 2018 ground surveyThe image is drawn by a figure, a fruit tree distribution diagram of Yanan city in Shaanxi province in 6 counties is drawn, meanwhile, the initial flowering period of the orchard in different areas in 2018-2020 is recorded, and the initial flowering period t of 18 ground sample points in three years is recorded 0 ；。
B: the remote sensing data with low spatial resolution selects MCD43A4 products in MODIS data, uses MRT (MODIS Reprojection Tool) tools to preprocess the original data such as re-projection, re-projects to UTM (UNIVERSAL TRANSVERSE MERCARTOR GRID STSTEM, universal transverse-axis ink-card-grid system), the time range is 2018-2020, the spatial resolution of the finally obtained data is 500 m, the time resolution is 1 day, and NDVI data is obtained through calculation. And selecting pixels which completely contain apple forest from the NDVI layer of the MODIS as sample points according to an apple space distribution map obtained by ground investigation, wherein the sample points are pure apple pixels.
C: taking the Luochuan county of Yanan city as an example, 5 sample points are selected and the NDVI time sequences of the sample points are extracted as initial reference curves, an S-G filtering method is used for eliminating and filling abnormal values and blank values of the NDVI time sequences, so that the continuity and rationality of the NDVI time sequences are ensured. And B-C steps are repeated for a plurality of times, so that a plurality of reference curves can be obtained, and an apple crop reference curve library is established.
D: according to the apple flowering phase data obtained by ground sampling from 2018 to 2021, the collected historical air temperature data is combined for analysis, so that the aim of monitoring the apple flowering phase is fulfilled. Wherein the historical air temperature data is ERA5, which is a fifth generation global climate atmospheric analysis dataset providing a plurality of weather data including air temperature, a time resolution of 1 day, and a spatial resolution of about 0.1 °. The cumulative values (more than 3 ℃) of the air temperatures before 18 initial flowering periods corresponding to the three years are counted, the initial flowering periods of apples when the accumulated temperature of more than 3 ℃ reaches 415 ℃ are set artificially through observation of the accumulated temperature and the accumulated temperature value of the three years, the actual initial flowering periods are compared with the accumulated temperature of 415 ℃, when the threshold value 415 ℃ is used as the initial flowering period, the good prediction precision (average error is 2.4 days) can be obtained, and the threshold value is accurate and effective and can be suitable for predicting the initial flowering periods of apples in key areas of Shanxi.
E: and D, downloading Sentinel-2 data, calculating the NDVI of the area based on the surface reflectivity of the clear sky pixel, and masking the non-fruit tree area in the research area through the fruit tree distribution map of 6 county in Yanan city of Shanxi province, which is manufactured in the step A.
F: e, analyzing all image pixels of the data and all curve determining coefficients R in the MODIS crop reference curve sample library obtained in the C according to the Sentinel-2 NDVI data obtained by the calculation in the step E 2 And (3) taking the curve with the maximum determining coefficient as the best reference curve for the fit of the sentinel NDVI and the MODIS reference curve to obtain a sentinel fit curve. L (x) =a×m (x+Δt) 0 )+b
Wherein x represents a day of the year, L (x) represents a Sentinel-2 time series NDVI curve function, M (x) is a MODIS optimal reference curve, Δt 0 Representing possible time offsets between the two curves, ranging from + -30 days, a and b are fitting parameters calculated using least squares, and fig. 2 is a schematic diagram of the principle of flowering phase detection based on crop reference curves.
At this time, the initial fitting curve and the sentinel data NDVI value of the target pixel area have a better fitting relation, but partial sentinel NDVI original values still cannot be well fitted to the initial fitting curve, the difference between the initial fitting curve and the original sentinel NDVI value is calculated, the linear interpolation method is adopted to fit the linear interpolation result to the initial curve so as to reduce the difference between the initial fitting curve and the initial fitting curve, and finally, the optimized fitting curve is further smoothed by utilizing a Gaussian function in an IDL programming platform, so that the curve contains all available original sentinel NDVI data as much as possible, and the curve at this time is a final fitting curve. Thus obtaining the final fitting parameter Deltat 0 A and b.
G: according to the offset deltat in time of each picture element in F relative to the optimal reference curve 0 And the value t of the initial flowering phase of the optimal reference curve 0 We can obtain the detection result deltat of the initial flowering phase of apple trees in 2019 every pixel 0 +t 0 And the detection result is verified by using the ground sample, the result is shown in table 1, and the average error is foundThe difference was about 0.83 days. The ground verification result shows that the crop reference curve-based method can well detect the initial flowering period of the apples in the historical years, and the accuracy is high.
TABLE 1 contrast of Shaanxi representative county initial flowering phase detection results with ground data
H: according to the weather forecast data of the current year and the historical weather data of the detected year, we calculate the time difference of 2019 when the accumulated temperature of the study area of 2021 reaches 415 ℃, and record the time difference as delta T, then the predicted value T=t of each pixel in the initial flowering period of 2021 0 +Δt 0 +Δt. In the study area, the temperature accumulation (accumulated temperature) of the temperature reaching more than 3 ℃ is calculated from 1 month and 1 day, and if the initial flowering period of the ground data of Rockwell in 2019 is 4 months and 11 days (101 days), the accumulated temperature is accumulated to 101 days. By calculating the accumulation temperature, the optimal accumulation temperature threshold before the initial flowering period of Rockwell is 415 ℃, meanwhile, the suitable accumulation temperature for north is 420 ℃ and the accumulation temperatures of flowers in other counties are all within 410-425, and the influence on the initial flowering period is within one day, so that the optimal initial flowering period accumulation temperature threshold of 2021 in the research area is taken as the temperature of Rockwell 415 ℃. According to the acquired site meteorological data and combining with meteorological forecast data, the detection result is corrected, and the calculated 2021 Rockwell accumulation temperature reaches 415 ℃ to be 98 days, which is 3 days earlier than 2019, so that the prediction value of the 2021 initial flowering period is obtained by subtracting 3 days from the high-precision initial flowering period detection result based on the reference curve in 2019. Fig. 3 shows the final prediction beginning-flowering period result of 2021 given by way of example of lochuan, huang Ling and Fu county, and the prediction result was evaluated for accuracy based on ground verification data, and the result is shown in table 2, wherein the average error is 2.67 days before the accumulation temperature correction, and the average error is reduced to 1.67 days after the accumulation temperature correction. As can be seen from the overall prediction result, the low latitude area of the whole research area has earlier initial flowering period and the high latitude area has later initial flowering period; meanwhile, the eastern flowering phase is late, the western flowering phase is early, and the method accords with the real situation of field investigation and is totalIn terms of the body, the accuracy is improved after correction by using the optimal heat accumulation threshold value. As can be seen from the detail graph of the right prediction result, under the spatial resolution of 10m, the weather difference exists among different orchards, which indicates that the method not only can accurately monitor the weather of apple trees, but also can identify the weather difference among the orchards. The method for predicting the flowering period of apples based on crop reference curves and accumulated temperature correction is accurate and effective.
Table 2 comparison of 2021 flowering phase predictions with 2021 ground data
It will be understood that modifications and variations will be apparent to those skilled in the art from the foregoing description, and it is intended that all such modifications and variations be included within the scope of the following claims.
Claims (1)
1. A method for predicting the flowering phase of apples based on crop reference curves and heat accumulation correction, comprising:
step 1: selecting a crop reference curve, extracting the crop reference curve from MODIS data according to ground sample points, and recording the initial flowering period t of the sample points 0 The method comprises the steps of carrying out a first treatment on the surface of the The step 1 comprises the following steps:
step A: the apple tree spatial distribution in the investigation area is investigated in the field, the vector boundary of the apple tree is sketched in the high-definition satellite image, and the initial period t of the apple tree in different areas is recorded 0 ；
And (B) step (B): preprocessing MODIS data with low spatial resolution including re-projection to enable a geographic coordinate system and a projection coordinate system of the MODIS data to be consistent with vector boundaries of apples, calculating to obtain NDVI of the MODIS data, combining the vector boundaries of fruit trees, searching pure apple pixels in the MODIS data, and extracting an NDVI time sequence curve of the pixels;
step C: repeatedly extracting a plurality of NDVI time sequence curves, removing abnormal values in the curves, interpolating blank values by adopting a Savitzky-Golay filtering method, wherein the interpolated curves can be used as reference curves, and a set of the plurality of reference curves is the reference curve library of apples;
step 2: recording the accumulation temperature before the historical flowering period, and selecting an optimal accumulation temperature threshold value of the optimal flowering period according to historical meteorological data; the step 2 comprises the following steps:
step D: counting the accumulated temperature before the initial flowering period of the historical ground sample points, and finding out an optimal accumulated temperature threshold value, so that the average relative error of each sample point is the smallest when the optimal accumulated temperature threshold value is used as the initial flowering period accumulated temperature value of apples in different years and different areas;
step 3: calculating a vegetation index of a high-spatial resolution clear pixel, preprocessing a high-resolution remote sensing image, and calculating the vegetation index of the clear pixel; the step 3 comprises the following steps:
step E: calculating NDVI data of a research area under clear sky based on the Sentinel-2 surface reflectivity data in the apple growth cycle time;
step 4: fitting the high spatial resolution vegetation index using the crop reference curve, fitting the high resolution vegetation index of the historical year to the crop reference curve, and establishing a parameter Δt 0 For recording the translation amount of the curve fitted by different pixels relative to the reference curve in time according to the translation amount delta t 0 An initial flowering period t with the reference curve 0 Calculating to obtain initial detection flowering period t 0 +Δt 0 ；
The step 4 comprises the following steps:
step F: analyzing the determining coefficient R of all image pixels of the data and all curves in the MODIS crop reference curve library obtained in the step C according to the Sentinel-2 time sequence NDVI data obtained by the step E 2 Will determine the coefficient R 2 The largest curve is used as the optimal reference curve of the image pixels of the Sentinel-2 time sequence NDVI data, and then the Sentinel-2 time sequence NDVI data and the MODIS optimal reference curve in the step E are further fitted through the following formula to obtain a Sentinel-2 fitting curve;
L(x)＝a×M(x+Δt 0 )+b
wherein x represents a day of the year, L (x) represents a Sentinel-2 time series NDVI curve function, M (x) is a MODIS optimal reference curve, Δt 0 Representing possible time offset between the two curves, wherein the range is +/-30 days, and a and b are fitting parameters calculated by using a least square method;
step G: when the curve is fitted according to the step F, the time offset of each pixel relative to the reference curve is recorded and recorded as deltat 0 At this time, according to the initial period t of the reference curve 0 Obtaining the initial detection florescence delta t of the pixel 0 +t 0 ；
Step 5: calculating the optimal accumulation temperature threshold time difference of the predicted year and the detected year by combining with the meteorological data, correcting the initial detected flowering period obtained in the step 4, calculating to obtain the predicted flowering period, and calculating the date difference delta t reaching the optimal accumulation temperature threshold when the accumulation temperature between two years is greater than 415 ℃ by using the meteorological data, so that the predicted flowering period of the pixel is t 0 +Δt 0 +Δt; said step 5 comprises the steps of:
step H: according to the weather forecast data of the current year and the historical weather data of the detected year, calculating the date t when the predicted year reaches the optimal heat accumulation temperature 1 The method comprises the steps of carrying out a first treatment on the surface of the At the same time, calculating the date t when the detection year reaches the optimal heat accumulation temperature 2 If the space range is larger, regional statistics is needed, and the date difference delta t=t that the apple tree reaches the optimal heat accumulation threshold value in the detected year and the predicted year is calculated at the moment 1 -t 2 ；
Step I: the initial test detection flowering period delta t obtained in the step G is carried out according to the date difference delta t reaching the optimal accumulation temperature threshold value obtained in the step H 0 +t 0 Correction is performed to obtain a predicted flowering period t=t 0 +Δt 0 +Δt。
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