CN116579872A - Accurate irrigation decision-making method based on crop growth model and weather forecast - Google Patents
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- 238000003973 irrigation Methods 0.000 title claims abstract description 52
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 47
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- 239000002689 soil Substances 0.000 claims description 12
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Abstract
The invention discloses a precise irrigation decision method based on a crop growth model and weather forecast, which comprises a crop water demand prediction model step based on the crop growth model and an irrigation decision model step based on the crop growth model, wherein the crop growth model is firstly corrected and verified based on related historical data of crop growth, then the water demand of each growth period of water-saving efficient crops is determined through the simulation of different irrigation strategies of different crop growth models, and then the real-time irrigation decision result of crops in a research area is determined according to the number of days and weather data after the crops are sowed and real-time weather forecast information, so that more scientific guidance irrigation can be provided for precise irrigation.
Description
Technical Field
The invention relates to an irrigation decision-making system, in particular to a precise irrigation decision-making method based on a crop growth model and weather forecast, and belongs to the technical field of agriculture and forestry irrigation.
Background
The precise agricultural technology is based on field cultivation, and according to the requirements of the crop growth process, the current situation of each growth and development state process and environmental elements of the crops are digitally, networked and intelligently monitored by modern monitoring means, and meanwhile, advanced technologies such as geographic information technology, computer monitoring and the like are utilized to realize macroscopic to microscopic monitoring prediction of the crops, soil moisture content, climate and the like, precise irrigation facilities are adopted to strictly, regularly and quantitatively and effectively fertilize and irrigate the crops in required time and places according to the monitoring results, so that the requirements of the crops in the growth process are ensured, and the agricultural irrigation facilities with high yield, high quality, high efficiency and water conservation are realized.
The crop growth model can dynamically simulate the growth and development process of crops and the relation between the crop growth model and climate factors, soil characteristics and management technologies, is a powerful tool for assisting in crop planting management, and is one of the most powerful tools for agricultural research with the rapid development of artificial intelligence and the continuous deep research on physiological mechanisms of crops, and the consideration of climate, soil, management measures and the like. However, the current crop growth models are still not very successful in practical application, mainly because many decision support systems contain limited agricultural technical knowledge, most systems still mainly emphasize scientific problems rather than practical application, resulting in insufficient knowledge density in the system, in addition, many crop growth models still do not allow flexible and instant decision input systems, decisions still need to be made after one simulation is finished or before the start, and model decision modes are difficult to match with actual production decision modes. The accuracy of the decision for accurate irrigation is therefore not ideal.
Disclosure of Invention
Aiming at the problems, the invention provides the accurate irrigation decision method based on the crop growth model and the weather forecast, which can determine the real-time accurate irrigation decision result of crops through the crop water demand and the rainfall of the weather forecast and can provide data support for accurate agricultural technology.
In order to achieve the purpose, the accurate irrigation decision method based on the crop growth model and the weather forecast specifically comprises the following steps:
step1, constructing a crop water demand prediction model based on a crop growth model,
step1-1, acquiring crop growth related historical data of a research area, wherein the crop growth related historical data comprises observation data such as crop field management data, historical meteorological data, soil data, crop growth, yield and the like;
step1-2, inputting partial data of the related data and the total data of the historical meteorological data into a crop growth model to obtain the simulated yield of the crop, calculating the relative root mean square error and the consistency coefficient of the difference between the simulated yield and the actual yield of the crop, correcting the parameters of the crop growth model by using a parameter calibration tool of the crop growth model, and training the model to obtain a calibrated crop growth model;
step1-3, inputting the residual data of the related data of the crop growth and the total data of the historical meteorological data into the calibrated crop growth model for verification to obtain a final crop growth model of the research area;
step1-4, simulating the final crop growth model of the research area for a plurality of times by using different irrigation strategies, and taking water conservation and high efficiency as targets to obtain the water demand of each growth stage of the crops, thereby obtaining a crop water demand prediction model based on the crop growth model;
step2, constructing an irrigation decision model based on a crop growth model,
step2-1, acquiring real-time weather forecast information of a research area and a crop growth period time number (namely the number of days from the sowing of crops to the maturity of seeds);
step2-2, determining a real-time irrigation decision result of crops in a research area according to a crop water demand prediction model based on a crop growth model, a crop growth period time number and real-time weather forecast information, wherein the real-time irrigation decision result specifically comprises the following steps:
determining whether rainfall occurs within 2 days in the future according to weather forecast information, and determining the rainfall if the rainfall occurs;
judging whether the crops need to be irrigated in a supplementing mode or not according to the initial water content before sowing of the crops, the current growth period number of the crops and a crop water demand prediction model based on a crop growth model through a water balance equation, if the rainfall in the future 2 days is medium rain or more, the real-time irrigation decision result is not needed to be irrigated, and if the rainfall in the future 2 days is no rain or little rain, the water balance equation is utilized to calculate the supplementing irrigation quantity as the real-time irrigation decision result.
Further, in Step1-2, the partial data of the total data of the crop growth related data and the historical meteorological data is 1/3 of the total data; in Step1-3, the remaining data of the total data of the crop growth related data and the historical meteorological data is the remaining 2/3 of the total data.
Further, in Step2-2, the water balance equation is:
I i ＝W i -W 0 +ETc i -P i +G i +R i
wherein: w (W) 0 、W i The water content of the soil at the initial period and the water content in the soil planned wetting layer at any time i are respectively measured in mm; i i The water filling amount in the time period is mm; p (P) i The effective rainfall in the period is mm; ETc i The water consumption of crops in a period of time is mm; g i Supplementing groundwater by the amount of mm; r is R i Is the ground runoff, mm.
Compared with the prior art, the accurate irrigation decision method based on the crop growth model and the weather forecast firstly corrects and verifies the crop growth model based on the historical data related to the crop growth, the trained crop growth model can be used for determining the optimal time and proper irrigation quantity for irrigation in agricultural production management, the water demand of the water-saving efficient crops in each growth period is determined by simulating different crop growth models through different irrigation strategies, then the real-time accurate irrigation decision result of crops in a research area is determined according to the daily number and weather data after the crops are sowed and real-time weather forecast information, and the rainfall information of the weather forecast is combined, so that more scientific guidance irrigation is realized, the optimal growth environment is provided for the crop growth, and data support can be provided for accurate agricultural technology so as to achieve the agricultural production target of high yield.
Drawings
Fig. 1 is a flow chart of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, the precise irrigation decision method based on the crop growth model and the weather forecast specifically comprises the following steps:
step1, constructing a crop water demand prediction model based on a crop growth model
Step1-1, acquiring crop growth related historical data of a research area, wherein the crop growth related historical data comprises observation data such as crop field management data, historical meteorological data, soil data, crop growth, yield and the like;
step1-2, inputting crop growth related data and historical meteorological data (1/3 of total data) into a crop growth model to obtain simulated yield of crops, calculating relative root mean square error and consistency coefficient of difference between the simulated yield and actual yield of the crops, correcting parameters of the crop growth model by using a parameter calibration tool of the crop growth model, and training the model to obtain a calibrated crop growth model;
step1-3, inputting crop growth related data and historical meteorological data (2/3 data of total data) into the calibrated crop growth model for verification, and obtaining a final crop growth model of a research area;
step1-4, simulating the crop growth model of the final research area for multiple times by different irrigation strategies, and obtaining the water demand of each growth stage of the crop by taking water conservation and high efficiency as targets, thereby obtaining a crop water demand prediction model based on the crop growth model.
Step2, constructing an irrigation decision model based on a crop growth model
Step2-1, acquiring real-time weather forecast information of a research area and a crop growth period time number (namely the number of days from the sowing of crops to the maturity of seeds);
step2-2, determining a real-time irrigation decision result of crops in a research area according to a crop water demand prediction model based on a crop growth model, a crop growth period time number and real-time weather forecast information, wherein the real-time irrigation decision result specifically comprises the following steps:
determining whether rainfall occurs within 2 days in the future according to weather forecast information, and determining the rainfall if the rainfall occurs;
judging whether the crops need to be irrigated in a supplementing mode or not according to the initial water content before sowing of the crops, the current growth period number of the crops and a crop water demand prediction model based on a crop growth model through a water balance equation, if the rainfall in the future 2 days is medium rain or more, the real-time irrigation decision result is not needed to be irrigated, and if the rainfall in the future 2 days is no rain or little rain, the water balance equation is utilized to calculate the supplementing irrigation quantity as the real-time irrigation decision result.
The water balance equation is:
I i ＝W i -W 0 +ETc i -P i +G i +R i
wherein: w (W) 0 、W i The water content of the soil at the initial period and the water content in the soil planned wetting layer at any time i are respectively measured in mm; i i The water filling amount in the time period is mm; p (P) i The effective rainfall in the period is mm; ETc i The water consumption of crops in a period of time is mm; g i Supplementing groundwater by the amount of mm; r is R i Is the ground runoff, mm.
The precise irrigation decision method based on the crop growth model and the weather forecast can determine the real-time irrigation decision result of crops in a research area according to the number of days and weather data after the crops are sowed and the real-time weather forecast information, so that more scientific guidance irrigation can be provided for precise irrigation.
Claims (3)
1. A precise irrigation decision-making method based on a crop growth model and weather forecast specifically comprises the following steps:
step1, constructing a crop water demand prediction model based on a crop growth model,
step1-1, acquiring crop growth related historical data of a research area, wherein the crop growth related historical data comprises observation data such as crop field management data, historical meteorological data, soil data, crop growth, yield and the like;
step1-2, inputting partial data of the related data and the total data of the historical meteorological data into a crop growth model to obtain the simulated yield of the crop, calculating the relative root mean square error and the consistency coefficient of the difference between the simulated yield and the actual yield of the crop, correcting the parameters of the crop growth model by using a parameter calibration tool of the crop growth model, and training the model to obtain a calibrated crop growth model;
step1-3, inputting the residual data of the related data of the crop growth and the total data of the historical meteorological data into the calibrated crop growth model for verification to obtain a final crop growth model of the research area;
step1-4, simulating the final crop growth model of the research area for a plurality of times by using different irrigation strategies, and taking water conservation and high efficiency as targets to obtain the water demand of each growth stage of the crops, thereby obtaining a crop water demand prediction model based on the crop growth model;
step2, constructing an irrigation decision model based on a crop growth model,
step2-1, acquiring real-time weather forecast information of a research area and a crop growth period time number (namely the number of days from the sowing of crops to the maturity of seeds);
step2-2, determining a real-time irrigation decision result of crops in a research area according to a crop water demand prediction model based on a crop growth model, a crop growth period time number and real-time weather forecast information, wherein the real-time irrigation decision result specifically comprises the following steps:
determining whether rainfall occurs within 2 days in the future according to weather forecast information, and determining the rainfall if the rainfall occurs;
judging whether the crops need to be irrigated in a supplementing mode or not according to the initial water content before sowing of the crops, the current growth period number of the crops and a crop water demand prediction model based on a crop growth model through a water balance equation, if the rainfall in the future 2 days is medium rain or more, the real-time irrigation decision result is not needed to be irrigated, and if the rainfall in the future 2 days is no rain or little rain, the water balance equation is utilized to calculate the supplementing irrigation quantity as the real-time irrigation decision result.
2. The precise irrigation decision method based on a crop growth model and weather forecast of claim 1, wherein in Step1-2, the partial data of the total data of the crop growth-related data and the historical meteorological data is 1/3 of the total data; in Step1-3, the remaining data of the total data of the crop growth related data and the historical meteorological data is the remaining 2/3 of the total data.
3. The precise irrigation decision method based on a crop growth model and weather forecast of claim 1, wherein in Step2-2, the water balance equation is:
I i ＝W i -W 0 +ETc i -P i +G i +R i
wherein: w (W) 0 、W i The water content of the soil at the initial period and the water content in the soil planned wetting layer at any time i are respectively measured in mm; i i The water filling amount in the time period is mm; p (P) i The effective rainfall in the period is mm; ETc i The water consumption of crops in a period of time is mm; g i Supplementing groundwater by the amount of mm; r is R i Is the ground runoff, mm.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117322214A (en) * | 2023-11-30 | 2024-01-02 | 余姚市农业技术推广服务总站 | Crop fertilizer accurate application method and system based on neural network |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117322214A (en) * | 2023-11-30 | 2024-01-02 | 余姚市农业技术推广服务总站 | Crop fertilizer accurate application method and system based on neural network |
| CN117322214B (en) * | 2023-11-30 | 2024-02-09 | 余姚市农业技术推广服务总站 | Crop fertilizer accurate application method and system based on neural network |
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