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Leveraging Time-Series Foundation Models in Smart Agriculture for Soil Moisture Forecasting
Specifically, this work presents a novel application of $\texttt{TimeGPT}$, a state-of-the-art (SOTA) time-series foundation model, to predict soil water potential ($\psi_\mathrm{soil}$), a key indicator of field water status that is typically used for irrigation advice.
End-To-End Self-tuning Self-supervised Time Series Anomaly Detection
A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types of time series anomalies (spikes, discontinuities, trend shifts, etc.)
Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors
IoT data is a central element in the successful digital transformation of agriculture.
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