Utilizing Crop Models for Irrigation Decision-making
This article introduces a series of articles on using crop models for irrigation decision-making. They mainly explore the development process of crop models, how to establish a regional crop growth model from point to surface, the method of using machine learning algorithms to construct crop models, and how to predict crop water demand through crop models to make optimal irrigation and fertilization decisions. These articles have important guidance and reference significance for readers to establish crop twin digital models and apply them to agricultural production.
These articles first introduce several models that have emerged in the development of crop models, as well as their basic principles and application scenarios, and how they accurately predict crop growth, yield, water use, and other aspects. Next, the principles and methods of using crop models for irrigation decision-making were introduced. By analyzing factors such as the water absorption capacity, soil moisture status, and meteorological factors of existing crops, irrigation water consumption decisions were made. By predicting future weather data, the water demand of crops in the coming period was given, and a reasonable irrigation plan was formulated.
The article also mentioned the use of modern scientific and technological means, such as unmanned aerial vehicles, remote sensing and other technologies, to collect land, weather and other data, modify model parameters, and carry out data validation and precision improvement of crop models. Finally, the application advantages of crop models were summarized, including increasing crop yield, reducing water resource waste, and achieving smart agriculture.
These articles provide useful ideas and methods for the digitization of agricultural production, and provide strong support for achieving smart agriculture.
1. History of crop growth models
The development of crop growth models can be traced back to the 1960s. The initial model mainly considered the effects of growth factors such as light, temperature, and water on crop growth, and gradually developed crop growth models based on growth physics (mechanisms) models (such as the WheatSim model). At that time, the models were often too complex and had a large demand for data, making it difficult to apply them to actual production.
With the development of computer technology, data-driven statistical models (such as the CLASSIC model) have become popular. This model fits a set of parameters through sample data, and then performs prediction and simulation. It is relatively simple and easy to operate, but usually requires a large amount of sample data, and its accuracy is affected by the quality of the sample.
In recent years, the application of machine learning technology has made crop growth models more accurate and intelligent.
2. Establishment of crop growth models for regions
In order to better simulate the growth process of crops on the ground, crop models usually need to have an evolution process from point models to surface models. The point model takes meteorological and soil data from a single location as input, output, and crop growth status at that location. The surface model takes regional meteorological and soil data as inputs, outputs, and the average growth status of crops within the region. Introduce several methods for secondary development using GIS.
3. Machine learning method for constructing digital twin models of crop growth
Machine learning methods can be used to construct digital twin models of crop growth. Among them, the digital twin model involves digitizing all the parameters and variables collected during actual crop planting, and then simulating the behavior of this digitized system with a computer to obtain a virtual system that is consistent with the behavior of the actual system. Then, by comparing the actual physical planting system with the virtual system, the model is validated and updated.
4. Utilizing crop models for irrigation and fertilization decisions
The only way to understand the details of crop growth is to establish a twin mathematical model of crop growth. Crop models refer to the transformation of various physiological and ecological characteristics of crops during their growth process into digital models, which are then used to simulate the growth and development process of crops, thereby providing a basis for irrigation decision-making.
Based on the mathematical model of crop growth, predict the growth of crops, and then complete irrigation and fertilization decisions and design future irrigation and fertilization plans based on the water and fertilizer demand model. Finally, optimize irrigation and fertilization based on irrigation and fertilization efficiency, with the goal of improving the utilization efficiency of water and nutrients in farmland, reducing the waste of water resources and water-soluble fertilizers, and increasing the yield and income of farmland.
5. Examples of irrigation and fertilization decision-making
Taking the planting of spring corn in Inner Mongolia as an example, it is assumed that there is a digital twin model of spring corn crops, and parameters have been calibrated based on local planting habits and varieties. Using crop digital models to simulate the growth process, identify the critical water and fertilizer requirements for corn growth, and meet the water and nutrient needs of crops during these critical periods.
6. Revealing the Application of Crop Digital Models in Intelligent Irrigation
Comprehensively introduced the application situation in intelligent irrigation, from basic theory to practical application knowledge. How to transform the model from a universal model to a specific crop specific model, and how to adapt the model to local varieties and planting habits through parameter adjustment and calibration. How to complete a comprehensive theoretical, practical, and systematic introduction to the entire decision-making process of using crop models for irrigation.
For more information, please refer to the article summary in Chinese
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