Optimizing Soil Moisture Forecasting with Deep Learning Approaches

Abstract

Humidity alone is an important factor in agricultural production. This influences plant growth, water use, and overall productivity. Accurate moisture forecasts alone are critical to optimizing an irrigation program. conserve water resources and improve agricultural sustainability Traditional moisture prediction models often face the problem of non-linearity and complexity of environmental factors. meteorology and related factors This research present a new deep learning architecture for slowness prediction that combines convolutional neural networks (CNN) and long-term and short-term memory networks (LSTM) to exploit spatial dependencies. Both spatial and temporal information the proposed model combines a variety of data sets. Including past soil moisture measurements. Meteorological information soil properties and culture-specific information. CNNs are used to extract spatial features from multidimensional input data such as satellite images and climate variables, while LSTMs are used to detect temporal dependencies in sequence data. This coupled approach allows the model to efficiently predict soil moisture at multiple spatial and temporal scales. To evaluate the effectiveness of the proposed model. Performance is compared with traditional forecasting methods such as linear regression and decision tree-based models. and other deep learning techniques including single layer models, LSTM and CNN, which are controlled by metrics such as mean square error (MSE), root mean square error (RMSE), error, etc. The R square value of this research emphasizes the potential of deep learning techniques to improve the accuracy of soil moisture forecasts. This provides great benefits for irrigation and water management. Resource Optimization The proposed model provides more accurate and reliable predictions. It can help increase crop yields, reduce water use and promote sustainable agricultural practices. Future research will investigate integration of additional data sources and further refinement of the model to improve accuracy and scalability.