Soilcast: a Multitask Encoder-Decoder AI Model for Precision Agriculture

This paper introduces Soilcast, an advanced multitask encoder-decoder predictive model designed to accurately forecast soil moisture in agricultural fields. By leveraging data from multiple sources and locations, Soilcast enhances resilience against overfitting, a common issue with traditional Long Short-Term Memory (LSTM) models. Tested on over 1,000 agricultural fields within the region of XXXX, in YYYY (masked as requested), Soilcast demonstrated superior performance compared to pure LSTM models, reducing mean squared error and mean absolute error by 10% and 15%, respectively, on average across datasets. The model's flexible architecture allows for both generalization across diverse datasets and specialization for specific fields, ensuring accurate daily soil moisture predictions, which are crucial for effectively optimizing irrigation. Additionally, Soilcast achieved a classification accuracy exceeding 92% in predicting soil moisture stress, outperforming singletask models in both robustness and generalization. These results position Soilcast as a valuable tool for improving water efficiency in response to climate challenges, fostering sustainable precision agriculture practices.