Agricultural time, agricultural work and agricultural technology fusion decision-making method based on crop growth period model

Abstract

The invention discloses an agricultural time, agricultural work and agricultural technology fusion decision-making method based on a crop growth period model, and relates to the technical field of agricultural information, and the method comprises the steps: collecting and sorting crop growth period data, meteorological data, soil data and agricultural work activity data of a certain region; constructing a crop growth period model based on the data, and verifying and optimizing the model; screening crop varieties based on the crop growth period model; on the basis of the screened crop varieties, combining a crop growth period model and local meteorological conditions, predicting the optimal sowing period and harvesting period, and optimizing field management. According to the agricultural time, agricultural work and agricultural technology fusion decision-making method provided by the invention, the crop growth period model is constructed by collecting and sorting the crop growth period data, the meteorological data, the soil data and the agricultural work activity data, various influence factors can be comprehensively considered, comprehensive simulation and prediction of the crop growth process are realized, and the decision-making efficiency is improved. The crops can grow in the optimal growth environment, and the yield and quality of the crops are improved.

Description

Technical Field

[0001] The present invention relates to the field of agricultural information technology, and in particular to a method for integrating farming time, farming activities and agronomy decision-making based on a crop growth period model. Background Art

[0002] In modern agricultural production, crop planting and management face many challenges, such as the uncertainty brought by climate change, differences in soil quality, the selection of crop varieties, and the requirements for refined field management. Traditional agricultural time and agronomic decisions often rely on farmers' experience and intuitive judgment. This method has great subjectivity and uncertainty and is difficult to adapt to the needs of modern agricultural production. With the rapid development of agricultural big data and artificial intelligence technology, new technical means have been provided for crop planting decision-making. By collecting and analyzing a large amount of crop growth period data, meteorological data, soil data and agricultural activity data, a crop growth period model can be constructed to predict crop growth and optimal management strategies. This data-based decision-making method can not only improve the accuracy and scientificity of decision-making, but also reduce production costs and improve agricultural production efficiency. Existing crop growth period models often have problems such as incomplete data collection, inaccurate model construction, and low prediction accuracy, making them difficult to be widely used in actual agricultural production. In this regard, we propose a decision-making method based on the crop growth period model that integrates farming time, farming activities and agronomy. Summary of the invention

[0003] In order to solve the above technical problems, a method for integrating farming time, farming activities and agronomy decision-making based on a crop growth period model is provided. This technical solution solves the above problems.

[0004] In order to achieve the above purpose, the technical solution adopted by the present invention is: A method for integrating farming time, farming activities and agronomy decision-making based on a crop growth period model, comprising: It is necessary to collect and organize crop growth period data, meteorological data, soil data and agricultural activity data in a certain area. When collecting crop growth period data, it is necessary to comprehensively cover the growth period information of different crop varieties in a certain area under various environmental conditions. Among them, the growth period information includes the time span of each stage such as sowing period, emergence period, tillering period, jointing period, booting period, heading period, flowering period, filling period and maturity period. These stages are crucial to understanding the growth and development process of crops. By recording the time of each stage in detail, we can better grasp the growth rhythm of crops.

[0005] The collection of meteorological data should include the temperature, precipitation, sunshine hours and wind speed of the region. The temperature directly affects the growth rate and development process of crops, and different crops have different temperature requirements at different growth stages. Precipitation is an indispensable factor for crop growth, and sufficient water supply is crucial for the normal growth and development of crops. The sunshine hours determine the length of time for crops to photosynthesize, which in turn affects the growth and yield of crops. Wind speed has a certain impact on the growth of crops. For example, strong winds can cause damage to crops such as lodging.

[0006] In determining soil data, professional tests are needed to determine indicators such as soil texture, fertility, pH value and water content. Soil texture affects soil aeration, water retention and fertility retention. Fertility is directly related to whether crops can obtain sufficient nutrient supply. pH value has an important impact on the growth suitability of different crops, and some crops have specific requirements for soil acidity and alkalinity. Water content reflects the moisture status of the soil, and appropriate soil moisture content is crucial for crop growth.

[0007] It is also necessary to record past agricultural operations, including the time and methods of sowing, fertilization, irrigation, and pest and disease control. The choice of sowing time directly affects the growth, development and yield of crops. Different crops have different suitable sowing times. The type, amount and time of fertilization play a key role in the growth and development of crops. Reasonable fertilization can improve the yield and quality of crops. The time and amount of irrigation should be reasonably adjusted according to the water requirements of crops and the moisture conditions of the soil. The time and methods of pest and disease control should be timely and effective to reduce the damage of pests and diseases to crops.

[0008] Based on the collected and sorted data, a crop growth period model was constructed, and the model was verified and optimized. First, the influencing factors and variables were determined. In terms of the growth period, the sowing period, emergence period, tillering period, jointing period, booting period, heading period, flowering period, filling period and maturity period were used as different growth period stage variables. These stage variables reflect the different stages of crop growth and development and are crucial for the construction of the model. In terms of meteorological factors, temperature, precipitation, sunshine hours and wind speed were used as meteorological variables. These meteorological variables have an important impact on the growth and development of crops. For example, temperature and precipitation directly affect the growth rate and development process of crops, sunshine hours determine the length of time for crops to photosynthesize, and wind speed causes damage to crops such as lodging. In terms of soil factors, texture, fertility, pH value and water content were used as soil variables. These soil variables reflect the physical and chemical properties of the soil and have an important impact on the growth and development of crops. In terms of agricultural activities, the time and methods of sowing, fertilization, irrigation, and pest and disease control were used as agricultural activity variables. These farming activity variables reflect the impact of human intervention on crop growth and development.

[0009] Construct a crop growth period model based on data. Mathematical modeling methods can be used to quantify and analyze various influencing factors and variables, and establish a model that can reflect the relationship between the crop growth period and various factors. In the process of model construction, it is necessary to fully consider the interaction and influence between various factors to improve the accuracy and reliability of the model.

[0010] The collected data is randomly divided into training set and test set, the training set data is used to train the model, and the test set data is used to evaluate the trained model. The accuracy and reliability of the model are improved by continuously adjusting the parameters and structure of the model.

[0011] This method of integrating farming time, farming activities and agronomy decision-making based on the crop growth period model can provide a scientific decision-making basis for agricultural production and improve the efficiency and quality of agricultural production by collecting and organizing various data, constructing and optimizing the crop growth period model.

[0012] Among them, the expression of the crop growth period model is: In the formula, represents the growth stage variable, Represent the regression coefficients of the variables temperature, precipitation, sunshine hours, wind speed, texture, fertility, pH value, water content, time and method of sowing, fertilization, irrigation, and pest control, respectively. They represent the variables temperature, precipitation, sunshine hours, wind speed, texture, fertility, pH value, water content, time and method of sowing, fertilization, irrigation, and pest control. represents the error term, and i represents the i-th variable.

[0013] Use the test set data to evaluate the trained model, including root mean square error, mean absolute error, and coefficient of determination; The root mean square error calculation formula is: In the formula, represents the root mean square error, n represents the number of samples, Indicates the actual value, represents the predicted value; The mean absolute error calculation formula is: In the formula, represents the mean absolute error; The determination coefficient calculation formula is: In the formula, represents the coefficient of determination, Indicates the average of the actual values.

[0014] Based on the crop growth period model, the screening of crop varieties specifically includes: Substitute the data of different crop varieties into the crop growth period model respectively. In this process, it is necessary to carefully observe the difference between the growth period stage predicted by the model and the actual growth period stage. This difference covers many aspects, including the length of the growth period of different crop varieties under the same meteorological, soil and farming conditions. The length of the growth period is crucial to agricultural production arrangements. A longer growth period requires more time and resource investment, while a shorter growth period is more suitable for rapid rotation or planting in a specific season. In addition, the yield differences of different crop varieties need to be considered. Yield is one of the important indicators to measure the quality of crop varieties. High-yield crop varieties can bring higher economic benefits to farmers. At the same time, quality is also a factor that cannot be ignored. Quality includes the taste, nutritional content, appearance and other aspects of the crop. High-quality crop varieties are more competitive in the market.

[0015] Analyze local weather data and consider soil factors. Through in-depth analysis of local weather data, understand local weather conditions such as temperature, precipitation, sunshine hours and wind speed. Different crop varieties have different adaptability to weather conditions. For example, some crop varieties are suitable for growing in warm and humid environments, while others are more adaptable to drought or cold climates. At the same time, soil factors also play a key role in the selection of crop varieties. Consider factors such as soil texture, fertility, pH value and water content to determine the crop varieties that are suitable for local soil conditions. For example, acidic soil is suitable for growing certain specific crop varieties, while alkaline soil is more suitable for other varieties.

[0016] According to the prediction results of the growth period model and the determination of crop varieties suitable for local climatic and soil conditions, a comprehensive evaluation of different crop varieties is conducted considering the yield, quality, and pest and disease resistance of the crops. Yield is one of the important aspects of the evaluation. Crop varieties with high yields can meet market demand and bring more economic benefits to farmers. Quality is related to the competitiveness of crops in the market and consumer satisfaction. Pest and disease resistance is also a key factor. Crop varieties with strong pest and disease resistance can reduce the use of pesticides and reduce production costs, while also being beneficial to environmental protection and the safety of agricultural products. In the comprehensive evaluation process, these factors need to be weighed and analyzed to determine the comprehensive advantages and disadvantages of each crop variety.

[0017] Select the best crop varieties based on the evaluation results. In the selection process, various factors should be considered, including the length of the growth period, yield, quality, resistance to pests and diseases, and local climate and soil conditions. Selecting the best crop varieties can not only improve the efficiency and quality of agricultural production, but also reduce production costs, increase farmers' income, and is conducive to the sustainable development of agriculture.

[0018] The expression of comprehensive evaluation is: In the formula, E represents the comprehensive evaluation value, They represent the matching degree of growth period, weight of yield potential, weight of quality grade and weight of pest and disease resistance respectively. Indicates the matching degree of reproductive period, Indicates the production potential, Indicates the quality level, Indicates the ability to resist diseases and pests.

[0019] Predicting the best planting and harvesting time specifically includes: Based on the acquired meteorological data, the local meteorological forecast data is substituted into the crop growth period model. In this process, the model will make full use of various information contained in the meteorological data, such as temperature, precipitation, sunshine hours and wind speed. These meteorological factors have a vital impact on the growth and development of crops. The model will predict the growth stage of crops at the corresponding time points based on the specific values ​​of different time points in the meteorological data. For example, the temperature will affect the growth rate of crops. Higher temperatures will accelerate the growth process of crops, while lower temperatures will lead to slow growth. The amount of precipitation determines whether crops can get enough water supply. Too much or too little precipitation will have an adverse effect on the growth of crops. The length of sunshine hours will affect the time for crops to photosynthesize, and then affect the growth and development of crops. The size of the wind speed will affect the morphology and growth environment of crops. Strong winds will cause crops to fall or be damaged.

[0020] By continuously adjusting the sowing time hypothesis, we can find the sowing time that makes the crop in the most suitable meteorological conditions during the critical growth period, which is the optimal sowing period. In this process, different sowing times need to be assumed and simulated. By substituting different sowing times into the crop growth period model, the time distribution of crops in various growth stages under different sowing times can be predicted. Then, combined with local meteorological forecast data, the meteorological conditions of crops in the critical growth period at different sowing times are analyzed. The critical growth period usually refers to the stage that is crucial to crop growth and yield formation, such as the emergence period, tillering period, heading period, etc. In these critical growth periods, suitable meteorological conditions can promote the growth and development of crops and improve the yield and quality of crops. Therefore, by continuously adjusting the sowing time hypothesis and finding the sowing time that makes the crops in the most suitable meteorological conditions during the critical growth period, the optimal sowing period can be determined. This process requires multiple attempts and adjustments to ensure that the optimal sowing period found can meet the growth needs of crops to the greatest extent.

[0021] According to the growth progress predicted by the crop growth period model and combined with meteorological conditions, the time point when the crop is in the mature stage and the meteorological conditions are suitable for harvesting is determined as the optimal harvest period. During the growth of crops, the growth period model will continuously predict the growth progress of crops based on meteorological data and other factors. When the crop is close to the mature stage, it is necessary to determine the optimal harvest period in combination with meteorological conditions. Suitable meteorological conditions are essential for the smooth progress of harvesting operations and the maintenance of crop quality. For example, if there is continuous rainy weather during the harvest period, the crops will become damp and moldy, thereby reducing the quality of the crops. Therefore, it is necessary to choose a time point with suitable meteorological conditions for harvesting based on meteorological forecast data. This time point should take into account both the maturity of the crop to ensure that the crop has reached the best state for harvesting, and the suitability of meteorological conditions to ensure the smooth progress of the harvest operation and the quality of the crop. Determining the optimal harvest period can maximize the yield and quality of crops, while also reducing losses and risks during the harvest process.

[0022] Optimizing field management includes: According to the prediction results of the crop growth period model, the start and end time of each growth stage of the crop can be determined more accurately. This process is crucial for the scientific management of agricultural production. Through the detailed division of the growth period, it can be divided into different stages such as sowing period, emergence period, tillering period, jointing period, booting period, heading period, flowering period, filling period and maturity period. Each stage has unique growth characteristics and requirements.

[0023] The sowing period is the starting point of crop growth, and choosing the right sowing time is crucial for the growth and development of crops. At this stage, factors such as soil temperature and humidity need to be considered to ensure that the seeds can germinate smoothly. The emergence period is the stage when the seedlings emerge from the soil after the seeds germinate. At this time, it is necessary to keep the soil moist to provide a good growth environment for the seedlings. The tillering period is the stage when the crop begins to grow multiple stems. This stage is of great significance for the yield formation of the crop. During the tillering period, sufficient nutrients and water need to be provided to promote the occurrence and growth of tillers.

[0024] The jointing stage is the stage when the crop stems elongate rapidly. At this time, it is necessary to control the application of nitrogen fertilizer to avoid causing the plants to grow too tall. The booting stage is the stage when the crop begins to form ears, which is crucial to the yield and quality of the crop. During the booting stage, sufficient nutrients and water need to be provided to ensure the normal development of the ear. The heading stage is the stage when the ear extends from the stem. At this time, it is necessary to pay attention to the prevention and control of diseases and pests to ensure the normal growth of the crop. The flowering stage is the stage when the crop is pollinated, which has an important impact on the fruit set rate of the crop. During the flowering stage, it is necessary to maintain suitable temperature and humidity to promote the smooth progress of pollination.

[0025] The grain filling stage is the stage when the crop grains are full. During this stage, sufficient nutrients and water need to be provided to ensure the fullness of the grains. The maturity stage is the stage when the crop grains are fully mature. At this time, they need to be harvested in time to avoid quality degradation due to over-maturity.

[0026] Formulating targeted management measures according to different stages is the key to improving agricultural production efficiency. During the sowing period, it is necessary to select appropriate seed varieties and sowing methods, and carry out reasonable soil treatment. During the emergence period, it is necessary to carry out thinning and seedling replacement in time to ensure the density and uniformity of the seedlings. During the tillering period, reasonable fertilization and irrigation are required to promote the occurrence and growth of tillers. During the jointing period, it is necessary to control the application of nitrogen fertilizers and increase the proportion of phosphorus and potassium fertilizers to promote the sturdiness and lodging resistance of the stems. During the booting period, sufficient nutrients and water need to be provided to ensure the normal development of the ear. During the heading period, pests and diseases need to be prevented and controlled in time to ensure the normal growth of crops. During the flowering period, it is necessary to pay attention to maintaining suitable temperature and humidity to promote the smooth pollination. During the filling period, reasonable fertilization and irrigation are required to ensure the fullness of the grains. During the maturity period, it is necessary to harvest in time to avoid quality degradation due to overmaturity.

[0027] Continuously monitor meteorological conditions, soil conditions and crop growth, and dynamically adjust field management measures according to actual conditions. Meteorological conditions have an important impact on the growth and development of crops, such as temperature, precipitation, and sunshine hours. Soil conditions are directly related to the nutrient supply and water retention capacity of crops. Crop growth reflects the effectiveness of field management measures and the growth status of crops. By continuously monitoring these factors, problems can be discovered in a timely manner and appropriate measures can be taken to make adjustments. For example, if the soil is found to be short of water, irrigation is required in a timely manner; if pests and diseases are found, prevention and control are required in a timely manner; if crops are found to be growing slowly, fertilization plans need to be adjusted, etc. Dynamic adjustment of field management measures can improve the adaptability and flexibility of agricultural production and ensure the healthy growth, high yield and high quality of crops.

[0028] Compared with the prior art, the present invention has the following beneficial effects: The agricultural time, agricultural activities and agronomy integration decision-making method proposed in the present invention constructs a crop growth period model by collecting and organizing crop growth period data, meteorological data, soil data and agricultural activity data. It can comprehensively consider various influencing factors and realize comprehensive simulation and prediction of the crop growth process. By comparing the growth period, yield and quality of different varieties under the same meteorological, soil and agricultural activity conditions, the crop varieties most suitable for local climate and soil conditions are selected, thereby improving the yield and quality of crops. By substituting local meteorological forecast data into the crop growth period model, the sowing time that makes the crops under the most suitable meteorological conditions at the critical growth stage is accurately found, thereby ensuring that the crops can grow healthily and achieve the best yield, ensuring that the crops grow in the best growth environment, and further improving the yield and quality of crops. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0030] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0031] Reference Figure 1 As shown in the figure, a method for integrating agricultural time, agricultural activities and agronomy decision-making based on crop growth period model includes: Collect and organize crop growth period data, meteorological data, soil data and agricultural activity data in a certain area; Construct crop growth period models based on data, and verify and optimize the models; Screen crop varieties based on crop growth period models; Based on the selected crop varieties, combined with the crop growth period model and local meteorological conditions, the optimal sowing and harvesting periods are predicted to optimize field management.

[0032] Preferably, collecting and organizing crop growth period data, meteorological data, soil data and agricultural activity data of a certain region specifically includes: Collect growth period information of different crop varieties in a certain area under different environmental conditions, including the time span of each stage of sowing period, seedling period, tillering period, jointing period, booting period, heading period, flowering period, grain filling period and maturity period; Collecting meteorological data of the area, the meteorological data including: temperature, precipitation, sunshine hours and wind speed; Testing to determine soil texture, fertility, pH, and moisture content; Record past agricultural operations, including the time and methods of sowing, fertilizing, irrigation, and pest and disease control.

[0033] Preferably, a crop growth period model is constructed based on data, and the method for verifying and optimizing the model is: Based on the collection and organization of crop growth period data, meteorological data, soil data and agricultural activity data in a certain area, the influencing factors and variables are determined: For the growth stage, the sowing period, emergence period, tillering period, jointing period, booting period, heading period, flowering period, grain filling period and maturity period were used as different growth stage variables; Meteorological factors, temperature, precipitation, sunshine hours and wind speed are used as meteorological variables; soil factors, texture, fertility, pH, and water content as soil variables; Farming activities, the time and methods of sowing, fertilization, irrigation, and pest and disease control were used as farming activity variables; Construct crop growth period models based on data; The collected data is randomly divided into training set and test set. The model is trained using the training set data, and the trained model is evaluated using the test set data.

[0034] Preferably, the expression of the crop growth period model is: In the formula, represents the growth stage variable, Represent the regression coefficients of the variables temperature, precipitation, sunshine hours, wind speed, texture, fertility, pH value, water content, time and method of sowing, fertilization, irrigation, and pest control, respectively. They represent the variables temperature, precipitation, sunshine hours, wind speed, texture, fertility, pH value, water content, time and method of sowing, fertilization, irrigation, and pest control. represents the error term, and i represents the i-th variable.

[0035] Preferably, the trained model is evaluated using the test set data, specifically including: root mean square error, mean absolute error and coefficient of determination; The root mean square error calculation formula is: In the formula, represents the root mean square error, n represents the number of samples, Indicates the actual value, represents the predicted value; The mean absolute error calculation formula is: In the formula, represents the mean absolute error; The determination coefficient calculation formula is: In the formula, represents the coefficient of determination, Indicates the average of the actual values.

[0036] Preferably, based on the crop growth period model, screening crop varieties specifically includes: Substitute the data of different crop varieties into the crop growth period model respectively, and observe the difference between the growth period stage predicted by the model and the actual growth period stage, which includes: the growth period length, yield and quality of different crop varieties under the same meteorological, soil and farming conditions; Analyze local meteorological data and consider soil factors to determine crop varieties suitable for local climate and soil conditions; Based on the prediction results of the growth period model and the determination of crop varieties suitable for local climate and soil conditions, a comprehensive assessment of different crop varieties is conducted taking into account crop yield, quality, and resistance to pests and diseases; Select the best crop variety based on the evaluation results.

[0037] Preferably, the expression of comprehensive evaluation is: In the formula, E represents the comprehensive evaluation value, They represent the matching degree of growth period, weight of yield potential, weight of quality grade and weight of pest and disease resistance respectively. Indicates the matching degree of reproductive period, Indicates the production potential, Indicates the quality level, Indicates the ability to resist diseases and pests.

[0038] Preferably, predicting the optimal sowing and harvesting time specifically includes: Based on the acquired meteorological data, the local meteorological forecast data is substituted into the crop growth period model, and the model predicts the growth period stage of crops at different time points according to the meteorological data; By continuously adjusting the sowing time hypothesis, we can find the sowing time that makes the crops in the most suitable meteorological conditions during the critical growth period, which is the optimal sowing time. Based on the growth progress predicted by the crop growth period model and combined with meteorological conditions, the optimal harvest period is determined when the crops are in the mature stage and the meteorological conditions are suitable for harvesting.

[0039] Preferably, optimizing field management specifically includes: According to the prediction results of the crop growth period model, the start and end time of each growth period of the crop is clarified, and the growth period is divided into different stages: sowing period, seedling period, tillering period, jointing period, booting period, heading period, flowering period, grain filling period and maturity period; Formulate targeted management measures according to different stages; Continuously monitor meteorological conditions, soil conditions and crop growth, and dynamically adjust field management measures based on actual conditions.

[0040] Preferably, the formulation of targeted management measures specifically includes: During the sowing period, calculate the sowing density to ensure that the seeds are evenly distributed and have good germination conditions. The sowing density calculation formula is: In the formula, the unit of seeding rate is kg / hectare. The unit is grams; During the seedling stage, the irrigation amount is determined based on the soil moisture content and crop water requirement. The irrigation amount calculation formula is: .

[0041] In the formula, the unit of irrigation volume is cubic meters per hectare; During the tillering stage, topdressing should be carried out according to soil fertility and crop growth; From the jointing stage to the booting stage, strengthen water management to ensure the water supply of crops in the key growth stage. At the same time, pay attention to the prevention and control of pests and diseases, and choose prevention and control measures according to the probability of occurrence and degree of damage of pests and diseases; From heading to flowering, ensure light and temperature to avoid the impact of extreme weather on crop pollination; During the grain filling period, foliar fertilization should be carried out according to the crop's nutrient requirements and soil fertility to continue to provide the right amount of water and nutrients to promote full grains; Maturity period: Harvest according to the predicted harvest period to avoid crop quality degradation or natural disasters caused by late harvest.

[0042] The use process of the present invention is: collecting and collating crop growth-related data; constructing a crop growth period model based on the data; screening crop varieties based on the crop growth period model; predicting the best sowing period and harvesting period, and optimizing field management.

[0043] To sum up, the advantages of the present invention are: by collecting and collating crop growth period, weather, soil and farming activity data to build a growth period model, comprehensively considering various factors to achieve comprehensive simulation and prediction of crop growth, comparing the growth period, yield and quality of different varieties under the same conditions, selecting the most appropriate variety to improve crop yield and quality, substituting local meteorological forecast data into the model, determining the best sowing time, ensuring that crops grow in the best growth environment, and further improving yield and quality.

[0044] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for integrating agricultural time, agricultural activities and agronomy decision-making based on a crop growth period model, characterized in that: include: Collect and organize crop growth period data, meteorological data, soil data and agricultural activity data in a certain area; Construct crop growth period models based on data, and verify and optimize the models; Screen crop varieties based on crop growth period models; Based on the selected crop varieties, combined with the crop growth period model and local meteorological conditions, the optimal sowing and harvesting periods are predicted to optimize field management.

2. The method for integrating agricultural time, agricultural activities and agronomy decision-making based on crop growth period model according to claim 1, characterized in that: Collect and organize crop growth period data, meteorological data, soil data and agricultural activity data in a certain area, including: Collect growth period information of different crop varieties in a certain area under different environmental conditions, including the time span of each stage of sowing period, seedling period, tillering period, jointing period, booting period, heading period, flowering period, grain filling period and maturity period; Collecting meteorological data of the area, the meteorological data including: temperature, precipitation, sunshine hours and wind speed; Testing to determine soil texture, fertility, pH, and moisture content; Record past agricultural operations, including the time and methods of sowing, fertilizing, irrigation, and pest and disease control.

3. The method for integrating agricultural time, agricultural activities and agronomy decision-making based on crop growth period model according to claim 1 is characterized in that: The crop growth period model is constructed based on the data, and the method for model verification and optimization is as follows: Based on the collection and organization of crop growth period data, meteorological data, soil data and agricultural activity data in a certain area, the influencing factors and variables are determined: For the growth stage, the sowing period, emergence period, tillering period, jointing period, booting period, heading period, flowering period, grain filling period and maturity period were used as different growth stage variables; Meteorological factors, temperature, precipitation, sunshine hours and wind speed are used as meteorological variables; soil factors, texture, fertility, pH, and water content as soil variables; Farming activities, the time and methods of sowing, fertilization, irrigation, and pest and disease control were used as farming activity variables; Construct crop growth period models based on data; The collected data is randomly divided into training set and test set. The model is trained using the training set data, and the trained model is evaluated using the test set data.

4. The method for integrating agricultural time, agricultural activities and agronomy decision-making based on crop growth period model according to claim 3 is characterized in that: in, The expression of crop growth period model is: In the formula, represents the growth stage variable, Represent the regression coefficients of the variables temperature, precipitation, sunshine hours, wind speed, texture, fertility, pH value, water content, time and method of sowing, fertilization, irrigation, and pest control, respectively. They represent the variables temperature, precipitation, sunshine hours, wind speed, texture, fertility, pH value, water content, time and method of sowing, fertilization, irrigation, and pest control. represents the error term, and i represents the i-th variable.

5. The method for integrating agricultural time, agricultural activities and agronomy decision-making based on crop growth period model according to claim 3 is characterized in that: Use the test set data to evaluate the trained model, including root mean square error, mean absolute error, and coefficient of determination; The root mean square error calculation formula is: In the formula, represents the root mean square error, n represents the number of samples, Indicates the actual value, represents the predicted value; The mean absolute error calculation formula is: In the formula, represents the mean absolute error; The determination coefficient calculation formula is: In the formula, represents the coefficient of determination, Indicates the average of the actual values.

6. The method for integrating agricultural time, agricultural activities and agronomy decision-making based on crop growth period model according to claim 1, characterized in that: Based on the crop growth period model, the screening of crop varieties specifically includes: Substitute the data of different crop varieties into the crop growth period model respectively, and observe the difference between the growth period stage predicted by the model and the actual growth period stage, which includes: the growth period length, yield and quality of different crop varieties under the same meteorological, soil and farming conditions; Analyze local meteorological data and consider soil factors to determine crop varieties suitable for local climate and soil conditions; Based on the prediction results of the growth period model and the determination of crop varieties suitable for local climate and soil conditions, a comprehensive assessment of different crop varieties is conducted taking into account crop yield, quality, and resistance to pests and diseases; Select the best crop variety based on the evaluation results.

7. The method for integrating agricultural time, agricultural activities and agronomy decision-making based on crop growth period model according to claim 6 is characterized in that: The expression of comprehensive evaluation is: In the formula, E represents the comprehensive evaluation value, They represent the matching degree of growth period, weight of yield potential, weight of quality grade and weight of pest and disease resistance respectively. Indicates the matching degree of reproductive period, Indicates the production potential, Indicates the quality level, Indicates the ability to resist diseases and pests.

8. The method for integrating agricultural time, agricultural activities and agronomy decision-making based on crop growth period model according to claim 1, characterized in that: Predicting the best planting and harvesting time includes: Based on the acquired meteorological data, the local meteorological forecast data is substituted into the crop growth period model, and the model predicts the growth period stage of crops at different time points according to the meteorological data; By continuously adjusting the sowing time hypothesis, we can find the sowing time that makes the crops in the most suitable meteorological conditions during the critical growth period, which is the optimal sowing time. Based on the growth progress predicted by the crop growth period model and combined with meteorological conditions, the optimal harvest period is determined when the crops are in the mature stage and the meteorological conditions are suitable for harvesting.

9. The method for integrating agricultural time, agricultural activities and agronomy decision-making based on crop growth period model according to claim 1, characterized in that: Optimizing field management includes: According to the prediction results of the crop growth period model, the start and end time of each growth period of the crop is clarified, and the growth period is divided into different stages: sowing period, seedling period, tillering period, jointing period, booting period, heading period, flowering period, grain filling period and maturity period; Formulate targeted management measures according to different stages; Continuously monitor meteorological conditions, soil conditions and crop growth, and dynamically adjust field management measures based on actual conditions.

10. The method for integrating agricultural time, agricultural activities and agronomy decision-making based on crop growth period model according to claim 9, characterized in that: The specific targeted management measures include: During the sowing period, calculate the sowing density to ensure that the seeds are evenly distributed and have good germination conditions. The sowing density calculation formula is: In the formula, the unit of seeding rate is kg / hectare. The unit is grams; During the seedling stage, the irrigation amount is determined based on the soil moisture content and crop water requirement. The irrigation amount calculation formula is: . In the formula, the unit of irrigation volume is cubic meters per hectare; During the tillering stage, topdressing should be carried out according to soil fertility and crop growth; From the jointing stage to the booting stage, strengthen water management to ensure the water supply of crops in the key growth stage. At the same time, pay attention to the prevention and control of pests and diseases, and choose prevention and control measures according to the probability of occurrence and degree of damage of pests and diseases; From heading to flowering, ensure light and temperature to avoid the impact of extreme weather on crop pollination; During the grain filling period, foliar fertilization should be carried out according to the crop's nutrient requirements and soil fertility to continue to provide the right amount of water and nutrients to promote full grains; Maturity period: Harvest according to the predicted harvest period to avoid crop quality degradation or natural disasters caused by late harvest.

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