A Django-Based Modeling Platform for Predicting Soil Moisture in Agricultural Fields

To solve the problems of strong professionalism and cumbersome operation required for crop soil moisture prediction, a soil moisture prediction platform has been developed for real-time irrigation decision-making based on the Django framework. This platform supports users in quickly selecting prediction models, setting model hyperparameters, and training models based on historical data to achieve the real-time predictions of soil moisture. Users can simply run the model according to the prompts, greatly reducing the threshold for non-professionals to use the prediction model and improving the convenience and scalability of practical applications. In addition, to improve the accuracy and stability of the model, the platform has integrated automatic parameter tuning methods including grid search and random search to further optimize model performance. The experimental results show that the platform’s models can continuously and accurately predict soil moisture during crop reproductive stages, achieving an R2 of 0.95 compared to observed values. This study not only effectively avoids the problem of duplicate development of soil moisture models and significantly improves the efficiency of model development and application, but also provides important support for the practical application and promotion of smart irrigation decision-making systems.