CN115688997A - Accumulated temperature-based tea leaf picking period prediction method and system - Google Patents
Accumulated temperature-based tea leaf picking period prediction method and system Download PDFInfo
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Abstract
The application provides a tea picking period prediction method and system based on accumulated temperature, belongs to the technical field of crop planting, and comprises the following steps: acquiring and integrating the data of the tea leaf picking period of the area to be detected in the past year to obtain picking sequence data; collecting day-by-day temperature-averaging data of a corresponding period according to the picking date data so as to determine first effective accumulated temperature data; dividing a time sequence interval according to the picking date data and collecting the first effective accumulated temperature data in a partitioning mode to obtain interval accumulated temperature data; and acquiring future daily average temperature data of the area to be detected, determining second effective accumulated temperature data according to the daily average temperature data, finally calculating to obtain a maturity index, and analyzing to obtain a prediction result of the tea leaf picking period. According to the technical scheme, the accumulated temperature required by each harvesting period time node is determined according to the multi-year harvesting data and the meteorological data of a certain tea original place, so that a maturity index is constructed for indicating the progress from the next period, and accurate harvesting time is obtained when harvesting is approached through the meteorological forecast data.
Description
Technical Field
The invention belongs to the technical field of crop planting, and particularly relates to a method and a system for predicting a tea leaf picking period based on accumulated temperature.
Background
Different tea leaf picking periods can lead to different tea leaf quality. For example, tea polyphenol and catechin of spring tea increase with the lapse of time, and amino acid and caffeine continuously decrease, thereby causing a difference in sensory evaluation of the finished tea product and affecting the quality of the tea.
The germination of tea leaves is influenced by temperature, and is a main factor influencing the germination and growth of tea buds in spring, and the early and late of the tea buds are determined by the temperature. After dormancy in winter, the overwintering terminal bud and the axillary bud at the top of the branch tip of the tea tree rise again in early spring, the weather turns warm, the overwintering bud in the dormant state begins to expand, the scales of the bud body protected by the bud surface crack, and the bud tip extends out of the scales.
At present, tea germination and picking are mainly determined by lunar calendar solar terms, for example, before the bright and rainy days, two key time points of Qingming and grain rain are referred to for picking tea, and the optimal picking date and the picking period ending date are not accurately given.
A method, a device and a storage medium for predicting the harvest time of crops are disclosed (the patent application number is CN 202110954520.6), and the scheme comprises the following steps: determining a first temperature value from a historical database; determining a second temperature value according to weather forecast data of the future W days; determining a first maturation date according to the first and second temperature values; calculating the grain moisture value of the crop; determining a second ripening date according to the grain moisture value; determining a harvest time for the crop based on the weather forecast data and the second maturity date; wherein W is a positive integer of 1 or more.
According to the scheme, accumulated temperature values are compared, historical accumulated temperature values serve as comparison standards, daily accumulated temperature values within a certain expected time are compared, the day is judged to be a mature period, and on the basis of determining the mature period by the accumulated temperature, harvesting time is determined by calculating a moisture value.
Disclosure of Invention
The application provides a tea leaf picking period prediction method and system based on accumulated temperature, and aims to solve the problems that when the maturity period of crops is determined according to the accumulated temperature, data confusion and comparison errors are prone to occurring, the determined maturity result is inaccurate, and the production degrees of different crops cannot be unified and controlled.
In order to achieve the purpose, the invention adopts the following technical scheme that the method comprises the following steps:
acquiring the data of the tea leaves in the area to be detected in the previous picking period and integrating the data to obtain the data of the picking date;
collecting day-by-day temperature-equalizing data of a corresponding period according to the picking day number data, and determining first effective accumulated temperature data according to the day-by-day temperature-equalizing data;
dividing a time sequence interval according to the picking date data and carrying out partition summarizing on the first effective accumulated temperature data according to the time sequence interval to obtain interval accumulated temperature data;
acquiring daily average temperature data of the area to be detected for a plurality of days in the future, determining second effective accumulated temperature data according to the daily average temperature data, calculating the tea maturity based on the second effective accumulated temperature data and the interval accumulated temperature data, obtaining maturity indexes, and analyzing the maturity indexes to obtain a prediction result of the tea picking period.
Preferably, the determining the first effective accumulated temperature data according to the day-by-day average temperature data includes:
respectively substituting the day-to-day temperature data into a formula W d ＝W r Calculating in-10 to obtain first effective accumulated temperature data, and when W is r When less than 10, W d Is 0, wherein W d Effective accumulated temperature in the same day, W r The data are temperature data.
Preferably, the dividing of the time sequence interval according to the picking day number data comprises:
the picking date sequence data comprises a spring mining date sequence a, a spring ending date sequence b, a summer mining date sequence c, a summer ending date sequence d, an autumn mining date sequence a and an autumn ending date sequence b, and the data are divided into 1-a as a first time sequence interval, a-b as a second time sequence interval, b-c as a third time sequence interval, c-d as a fourth time sequence interval, d-e as a fifth time sequence interval and e-f as a sixth time sequence interval.
Preferably, the partitioning and summarizing the first effective accumulated temperature data according to the time sequence interval to obtain interval accumulated temperature data includes:
and summarizing the first effective accumulated temperature data in a first time sequence interval, a second time sequence interval, a third time sequence interval, a fourth time sequence interval, a fifth time sequence interval and a sixth time sequence interval according to time sequences to sequentially obtain accumulated temperature A, accumulated temperature B, accumulated temperature C, accumulated temperature D, accumulated temperature E and accumulated temperature F, and summarizing the accumulated temperature A, accumulated temperature B, accumulated temperature C, accumulated temperature D, accumulated temperature E and accumulated temperature F to obtain interval accumulated temperature data.
Preferably, the tea leaf maturity is calculated based on the second effective accumulated temperature data and the interval accumulated temperature data to obtain a maturity index, and the maturity index is analyzed to obtain a prediction result of the tea leaf picking period, wherein the method comprises the following steps:
substituting the second effective accumulated temperature data and the interval accumulated temperature data into the following formula for calculation to obtain a maturity index,
wherein y is a maturity index, x is a current accumulated temperature, namely the sum of the historical accumulated temperature of the current year and the effective accumulated temperature of the current day, and A-F are different interval accumulated temperature data;
presetting a growth period when the value of y is 0-1, a mining period when the value of y reaches 1,
when the value of y is 1-2, the picking period is defined,
when the y value is more than 2, the seeds are over-ripe and should not be picked, namely the picking period is finished;
and carrying out comparative analysis on the maturity index according to the threshold interval to obtain a prediction result of the tea leaf picking period.
A accumulated temperature-based tea leaf picking period prediction system comprises:
picking sequence determining module: the system is used for acquiring the data of the tea leaves in the area to be detected in the previous picking period and integrating the data to obtain the data of the number of picking days;
an effective accumulated temperature calculation module: the temperature control system is used for collecting day-to-day temperature data of a corresponding period according to the picking day sequence data and determining first effective accumulated temperature data according to the day-to-day temperature data;
the interval accumulated temperature calculation module: the system is used for dividing time sequence intervals according to the picking date data and collecting the first effective accumulated temperature data in a partitioning mode according to the time sequence intervals to obtain interval accumulated temperature data;
tea leaf picking period prediction module: the system is used for obtaining the daily average temperature data of the area to be detected for a plurality of days in the future, determining second effective accumulated temperature data according to the daily average temperature data, calculating the tea maturity based on the second effective accumulated temperature data and the interval accumulated temperature data, obtaining the maturity index and analyzing the maturity index to obtain the prediction result of the tea picking period.
An accumulated temperature based tea picking period prediction system comprising a memory for storing one or more computer instructions and a processor, wherein the one or more computer instructions are executed by the processor to implement an accumulated temperature based tea picking period prediction method as claimed in any one of the preceding claims.
A computer readable storage medium storing a computer program which, when executed by a computer, implements an accumulated temperature-based tea plucking period prediction method as described in any one of the above.
The invention has the following beneficial effects:
according to the technical scheme, the accumulated temperature required by each time node of a harvesting period is determined according to multi-year harvesting data and meteorological data of a certain tea original place, so that a maturity index is constructed for indicating the progress from the next period, and accurate harvesting time is obtained when harvesting is approached through meteorological forecast data;
the method can also be used for guiding tea growers to harvest the tea leaves to obtain high-quality tea leaves, and meanwhile, when the tea leaves are transplanted, the method can avoid misjudgment of picking periods caused by north-south differences and altitude differences, and improve the harvesting efficiency of the transplanted tea;
the method provides a parameter of maturity index, based on the calculation of accumulated temperature, the maturity index is further calculated through the accumulated temperature, the tea leaf picking period is predicted according to the maturity index, the method is more visual, the method can be used for predicting picking periods of other different crops to obtain maturity indexes of different crops, a commonly applicable maturity value is constructed and is equivalent to data normalization in data analysis, different crop growth schedules can be compared, and the unification control of growth degree of different crops can be carried out;
in the process of calculating the maturity index, different crops have different mining periods and ending periods, so the accumulated temperature is calculated according to different time sequence intervals, the maturity index calculation is further carried out by combining the accumulated temperature of the crops in the future based on the calculation result, data correspondence errors are not easy to occur, two accumulated temperature values of the same type are compared, and the corresponding accumulated temperature values are calculated according to different time sequence intervals, so that when the maturity of various crops is calculated, data confusion cannot occur, and the accuracy of the maturity prediction results of various crops is improved.
Drawings
FIG. 1 is a flow chart of a method for predicting tea leaf picking period based on accumulated temperature in the invention
FIG. 2 is a diagram illustrating a timing interval according to the present invention
FIG. 3 is a flow chart of a tea maturity index calculation scheme of the Leishan tea garden of the present invention
FIG. 4 is a schematic structural diagram of a accumulated temperature-based tea leaf picking period prediction system according to the present invention
FIG. 5 is a schematic structural diagram of an electronic device of a system for predicting tea leaf picking period based on accumulated temperature according to the present invention
Detailed Description
Example 1
As shown in fig. 1, a method for predicting a tea leaf picking period based on accumulated temperature comprises the following steps:
s11, acquiring the data of the tea leaves in the area to be detected in the previous picking period and integrating the data to obtain the data of the picking date;
s12, collecting day-to-day temperature equalization data of a corresponding period according to the picking day number data, and determining first effective accumulated temperature data according to the day-to-day temperature equalization data;
s13, dividing time sequence intervals according to the picking day number data and carrying out partition summarizing on the first effective accumulated temperature data according to the time sequence intervals to obtain interval accumulated temperature data;
s14, acquiring day average temperature data of a plurality of days in the future of the area to be detected, determining second effective accumulated temperature data according to the day average temperature data, calculating the tea leaf maturity based on the second effective accumulated temperature data and the interval accumulated temperature data, obtaining a maturity index, and analyzing the maturity index to obtain a prediction result of the tea leaf picking period.
One implementation mode of the scheme is as follows:
1. firstly, collecting phenological period data and extracting the date sequence of the mining period and the picking end period;
data collection is needed in the initial modeling stage, for tea trees, the dates of all key time nodes such as the exploitation period and the ending period of the past year need to be collected, all date data are collected, picking date data are obtained, and the picking date data are made into a table for later use.
| Time period | Spring mining | Spring over | Summer mining | End of summer | Autumn mining | End of autumn |
| Sequence of the day | a | b | c | d | e | f |
| Year, month and day |
2. Collecting day-by-day temperature data corresponding to the dates;
and (4) acquiring meteorological data according to the acquired phenological period information, namely acquiring day-to-day temperature-averaging data of corresponding years. The effective accumulated temperature is calculated according to the following formula:
W d ＝W r -10
in the formula, daily mean temperature W r When the temperature is higher than 10 ℃, the effective accumulated temperature exists, and calculation can be carried out; day average temperature W r When the temperature is less than 10 ℃, the effective accumulated temperature W is in the day d Is denoted by 0, where W d Effective accumulated temperature in the same day, W r The data are temperature data. And substituting the day-by-day temperature-equalizing data into the result calculated by the formula, and summarizing to obtain 'first effective accumulated temperature data'.
The accumulated temperature of different time sequence intervals is defined as follows:
as shown in fig. 2, there are:
The sequence a to b, i.e., the second time interval: accumulated temperature B
Sequences b to c, i.e., the third sequence interval: accumulated temperature C
The fourth time interval of the sequences c to d: accumulated temperature D
The sequence d to e is the fifth time sequence interval: accumulated temperature E
The sixth time sequence section with the sequence e-f: accumulated temperature F
And respectively calculating the numerical values of accumulated temperature A-accumulated temperature F according to the day-by-day temperature data, and calculating the maturity index on the basis of the numerical values and using the maturity index as a forecast of the tea leaf exploitation stage.
3. Then, definition and modeling of maturity index are carried out
The maturity index is first defined numerically: when the value is 0-1, the growth period is the period, and when the value reaches 1, the exploitation period is the period; when the value is 1-2, the picking period is set; when the value is more than 2, the fruit is over-ripe and should not be picked, namely the picking period is finished.
The calculation formula of the maturity index is a piecewise function and is calculated according to the percentage of reaching the standard of the accumulated temperature, and the expression is as follows:
wherein y is a maturity index, x is a current accumulated temperature (the current accumulated temperature is the sum of the historical accumulated temperature of the current year and the effective accumulated temperature of the current day), A to F are accumulated temperatures of different stages and are constants, and the accumulated temperatures are determined in the step 2.
4. Park accumulated temperature calculation and period prediction
The accumulated temperature for a single base (area to be measured) is calculated as follows:
(1) firstly, two parts of data are required to be acquired, wherein one part of the data is data accumulated in the current year and extracted from the nearest Internet of things equipment or meteorological stations of a base; the other part is prediction data of 14 days (which can be long or short) in the future, and can be obtained from channels such as a meteorology or a local meteorological bureau. The accumulated data is an accumulated value of the effective accumulated temperature of the current year, the predicted data is a daily average temperature forecast result of 14 days (which can be long or short) in the future, the daily average temperature data is substituted into a calculation formula of the effective accumulated temperature for calculation to obtain the effective accumulated temperature of the current day, and the effective accumulated temperatures of 14 days (which can be long or short) in the future are summarized to obtain 'second effective accumulated temperature data';
(2) after the data are obtained, the maturity index y1 of the day is calculated through a calculation formula of the maturity index, and the maturity index of each day within 14 days is calculated through future prediction data.
And finally, determining the date of tea leaf mining according to the calculated maturity index and a threshold interval defined by the maturity index.
Example 2
In order to better understand the scheme, the embodiment takes a leishan tea garden as an example for description, a main flow is shown in fig. 3, and specific technical contents are as follows:
the data are inquired, the picking start time of the spring tea in the three years of 2019-2021 is 3.12, and the end date is 4.23. Namely:
| beginning of spring tea picking | The picking of the spring tea is finished | |
| Date | 3.12 | 4.23 |
| Sequence of the day | 71 | 113 |
The daily average temperature data of three years in 2019, 2020 and 2021 are obtained through GSMaP meteorological satellite data, and the daily effective accumulated temperature is calculated by using a daily effective accumulated temperature calculation formula. And calculating to obtain the effective accumulated temperature sum of the sequences 1-71 and 72-113 in each year in three years.
| 2019 | 2020 | 2021 | Average of three years | |
| Sequence 1-71 | 12.993 | 62.306 | 28.033 | 34.444 |
| 72-113 in the sequence | 189.47 | 123.498 | 164.396 | 159.1213 |
Then, the maturity index can be calculated as:
y＝x/34.44 (x<34.44)
＝1+(x-34.44)/159.12 (34.44<x<193.56)
in the formula, x is the effective accumulated temperature accumulated from the beginning of the sequence 1.
Taking this kind of tea as an example, suppose that the current obtained effective accumulated temperature accumulated value of the current year through the internet of things device is 28.5, and the daily average temperature forecast result of 7 days in the future can be obtained, there are:
| days of the |
1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Average daily temperature | 12.35 | 11.16 | 10.40 | 9.18 | 11.19 | 12.78 | 10.58 |
| Effective accumulated temperature | 2.35 | 1.16 | 0.40 | 0.00 | 1.19 | 2.78 | 0.58 |
| Accumulated temperature accumulation | 30.85 | 32.01 | 32.41 | 32.41 | 33.60 | 36.38 | 36.96 |
| Degree of maturity | 0.90 | 0.93 | 0.94 | 0.94 | 0.98 | 1.01 | 1.02 |
The data in the table illustrate that: the daily average temperature is obtained through the Internet of things equipment; the effective accumulated temperature is obtained by calculating the average daily temperature in an effective accumulated temperature calculation formula; the accumulated temperature is obtained by adding the effective accumulated temperature of the day to the accumulated value of the effective accumulated temperature of the year, for example, when the number of days in the future is 1, the accumulated temperature =28.5+2.35=30.85, when the number of days in the future is 2, the accumulated temperature =30.85+1.16=32.01, and so on; the maturity is calculated by substituting the accumulated temperature into a maturity index calculation formula, because the accumulated value of the effective accumulated temperature obtained in the current year is 28.5, the corresponding period of the tea is between the sequence of 1 and 71, the maturity of the current day is calculated by applying the formula in two intervals of 34.444 and 159.1213 according to the accumulated temperature of seven days in the future, and the day when the maturity first reaches 1 is determined as the exploitation period according to a defined threshold interval.
From the maturity count calculations in the table, it can be seen that, in this case, the future day 6 will be the leaf date of the garden in leishan county.
Example 3
The present embodiment differs from the above embodiments in that: the method comprises the steps that picking periods of other crops (such as corn, soybean, wheat, rape and the like) are predicted, time sequence intervals of a picking start period and a picking time sequence period need to be determined again, accumulated temperature accumulated values corresponding to the time sequence intervals are calculated again on the basis of the new intervals, and then the maturity of the crops is calculated according to a new maturity index calculation formula, so that maturity index values are obtained, and the mining periods are determined;
the indexes in the prediction process need to be determined again for different types or varieties of crops, and modeling is carried out again; for the same tea variety or the same kind of crops, the original formula can be directly applied to calculate the exploitation date during transplanting.
Example 4
As shown in fig. 4, a tea leaf picking period prediction system based on accumulated temperature includes:
picking sequence determining module 10: the system is used for acquiring the data of the tea leaves in the area to be detected in the previous picking period and integrating the data to obtain the data of the number of picking days;
the effective accumulated temperature calculation module 20: the temperature control system is used for collecting day-to-day temperature data of a corresponding period according to the picking day sequence data and determining first effective accumulated temperature data according to the day-to-day temperature data;
the interval accumulated temperature calculating module 30: the system is used for dividing time sequence intervals according to the picking date data and collecting the first effective accumulated temperature data in a partitioning mode according to the time sequence intervals to obtain interval accumulated temperature data;
tea picking period prediction module 40: the system is used for obtaining day average temperature data of a plurality of days in the future of the area to be detected, determining second effective accumulated temperature data according to the day average temperature data, calculating the tea leaf maturity based on the second effective accumulated temperature data and the interval accumulated temperature data, obtaining the maturity index, and analyzing the maturity index to obtain the prediction result of the tea leaf picking period.
One embodiment of the above system is that, in the picking sequence determining module 10, the data of the previous-year picking period of the tea leaves in the area to be detected is obtained and integrated to obtain the data of the picking date, in the effective accumulated temperature calculating module 20, the day-by-day equalized temperature data of the corresponding period is collected according to the data of the picking date, the first effective accumulated temperature data is determined according to the day-by-day equalized temperature data, in the interval accumulated temperature calculating module 30, the time sequence interval is divided according to the data of the picking date, the first effective accumulated temperature data is collected in a partition mode according to the time sequence interval to obtain the interval accumulated temperature data, in the tea leaf picking period predicting module 40, the day equalized temperature data of a plurality of days in the future in the area to be detected is obtained, the second effective accumulated temperature data is determined according to the day equalized temperature data, the tea leaf maturity is calculated based on the second effective accumulated temperature data and the interval accumulated temperature data, and the maturity index is obtained and analyzed to obtain the prediction result of the tea leaf picking period.
Example 5
On the basis of the above embodiments, the present embodiment provides an electronic device, as shown in fig. 5.
Example 6
On the basis of the above embodiments, the present embodiment provides a storage medium.
The above description is only an embodiment of the present invention, but the technical features of the present invention are not limited thereto, and any changes or modifications within the technical field of the present invention by those skilled in the art are covered by the claims of the present invention.
Claims (8)
1. A method for predicting a tea leaf picking period based on accumulated temperature is characterized by comprising the following steps:
acquiring the data of the tea leaves in the area to be detected in the previous picking period and integrating the data to obtain the data of the picking date;
collecting day-by-day temperature-equalizing data of a corresponding period according to the picking day number data, and determining first effective accumulated temperature data according to the day-by-day temperature-equalizing data;
dividing a time sequence interval according to the picking date data and carrying out partition summarizing on the first effective accumulated temperature data according to the time sequence interval to obtain interval accumulated temperature data;
acquiring day average temperature data of a plurality of days in the future of the area to be detected, determining second effective accumulated temperature data according to the day average temperature data, calculating the tea leaf maturity based on the second effective accumulated temperature data and the interval accumulated temperature data, obtaining a maturity index, and analyzing the maturity index to obtain a prediction result of the tea leaf picking period.
2. The accumulated temperature-based tea leaf picking period prediction method according to claim 1, wherein determining the first effective accumulated temperature data from the day-to-day average temperature data comprises:
respectively substituting the day-by-day temperature data into the formula W d ＝W r In-10 ofCalculating to obtain first effective accumulated temperature data, and when W is r When less than 10, W d Is 0, wherein W d Effective accumulated temperature in the same day, W r The data are temperature data.
3. The accumulated temperature-based tea leaf picking period prediction method according to claim 1, wherein dividing time-series intervals according to picking day number data comprises:
the picking date sequence data comprises a spring mining date sequence a, a spring ending date sequence b, a summer mining date sequence c, a summer ending date sequence d, an autumn mining date sequence a and an autumn ending date sequence b, and the data are divided into 1-a as a first time sequence interval, a-b as a second time sequence interval, b-c as a third time sequence interval, c-d as a fourth time sequence interval, d-e as a fifth time sequence interval and e-f as a sixth time sequence interval.
4. The accumulated temperature-based tea leaf picking period prediction method according to claim 3, wherein the step of collecting the first effective accumulated temperature data in a partitioned manner according to the time sequence interval to obtain interval accumulated temperature data comprises the following steps:
and summarizing the first effective accumulated temperature data in a first time sequence interval, a second time sequence interval, a third time sequence interval, a fourth time sequence interval, a fifth time sequence interval and a sixth time sequence interval according to time sequences to sequentially obtain accumulated temperature A, accumulated temperature B, accumulated temperature C, accumulated temperature D, accumulated temperature E and accumulated temperature F, and summarizing the accumulated temperature A, accumulated temperature B, accumulated temperature C, accumulated temperature D, accumulated temperature E and accumulated temperature F to obtain interval accumulated temperature data.
5. The accumulated temperature-based tea leaf picking period prediction method according to claim 4, wherein the calculation of the tea leaf maturity is performed based on the second effective accumulated temperature data and the interval accumulated temperature data to obtain a maturity index, and the maturity index is analyzed to obtain a prediction result of the tea leaf picking period, and the method comprises the following steps:
substituting the second effective accumulated temperature data and the interval accumulated temperature data into the following formula for calculation to obtain a maturity index,
wherein y is a maturity index, x is a current accumulated temperature, namely the sum of the historical accumulated temperature of the current year and the effective accumulated temperature of the current day, and A-F are different interval accumulated temperature data;
presetting a growth period when the value of y is 0-1, a mining period when the value of y reaches 1,
when the value of y is 1-2, the picking period is defined,
when the y value is more than 2, the seeds are over-ripe and should not be picked, namely the picking period is finished;
and carrying out comparative analysis on the maturity indexes according to the threshold interval to obtain a prediction result of the tea leaf picking period.
6. A tea picking period prediction system based on accumulated temperature is characterized by comprising:
picking sequence determining module: the system is used for acquiring the data of the tea leaves in the area to be detected in the previous picking period and integrating the data to obtain the data of the number of picking days;
an effective accumulated temperature calculation module: the temperature control system is used for collecting day-to-day temperature data of a corresponding period according to the picking day sequence data and determining first effective accumulated temperature data according to the day-to-day temperature data;
the interval accumulated temperature calculation module: the system is used for dividing time sequence intervals according to the picking date data and collecting the first effective accumulated temperature data in a partitioning mode according to the time sequence intervals to obtain interval accumulated temperature data;
tea picking period prediction module: the system is used for obtaining day average temperature data of a plurality of days in the future of the area to be detected, determining second effective accumulated temperature data according to the day average temperature data, calculating the tea leaf maturity based on the second effective accumulated temperature data and the interval accumulated temperature data, obtaining the maturity index, and analyzing the maturity index to obtain the prediction result of the tea leaf picking period.
7. An accumulated temperature based tea plucking period prediction system, comprising a memory for storing one or more computer instructions and a processor, wherein the one or more computer instructions are executed by the processor to implement an accumulated temperature based tea plucking period prediction method according to any one of claims 1-5.
8. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer, implements a method for predicting a tea plucking period based on accumulated temperature according to any one of claims 1 to 5.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115860285A (en) * | 2023-03-01 | 2023-03-28 | 浙江领见数智科技有限公司 | Method and device for predicting optimal transplanting period of tobacco |
| CN117172385A (en) * | 2023-09-15 | 2023-12-05 | 数字广西集团有限公司 | Sugarcane high-sugar-content harvest period prediction method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115860285A (en) * | 2023-03-01 | 2023-03-28 | 浙江领见数智科技有限公司 | Method and device for predicting optimal transplanting period of tobacco |
| CN115860285B (en) * | 2023-03-01 | 2023-10-31 | 浙江领见数智科技有限公司 | Prediction method and device for optimal transplanting period of tobacco |
| CN117172385A (en) * | 2023-09-15 | 2023-12-05 | 数字广西集团有限公司 | Sugarcane high-sugar-content harvest period prediction method and system |
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