EigenCL is a contrastive learning framework that stages crop stress by embedding Sentinel‑2 NDRE trajectories, producing four physiologically coherent clusters—Healthy, Mild, Moderate, and Severe—without retraining across different states. Trained on 10,000 maize NDRE patches from Iowa in 2020 and validated on Nebraska data in 2023, EigenCL outperformed baselines such as K‑Means, SimCLR, and ProtoCLR, achieving high silhouette, DBI, and CHI scores. The resulting clusters align with maize growth stages, correlate strongly with soil moisture and yield anomalies, and provide interpretable outputs like heatmaps and scouting priorities for decision‑support systems.
arXiv:2606. 25174v1 Announce Type: new Abstract: Field-scale retrieval of surface soil moisture (SM), leaf area index (LAI), and plant height (PH) is essential for precision agriculture, yet it remains an ill-posed inverse problem.
arXiv:2606. 05731v1 Announce Type: new Abstract: In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops.
arXiv:2509.18123v2 Announce Type: replace Abstract: Accurate interpretation of soil moisture patterns is critical for irrigation scheduling and crop management, yet existing approaches for soil moist...
arXiv:2606. 27277v1 Announce Type: new Abstract: Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions.
arXiv:2608.30392v1 Announce Type: new Abstract: Global food security and sustainable climate action increasingly rely on robust, scalable agricultural monitoring. Earth observation foundation models...
arXiv:2608. 15282v1 Announce Type: new Abstract: Earth observation foundation models (EOFMs) are emerging as reusable representation frameworks for data-driven retrieval, prediction and process modelling within ecohydrology, which integrate EO, meteorological forcing and process models to characterise coupled water, energy and carbon dynamics in vegetation and soil across scales.
arXiv:2608. 07801v1 Announce Type: cross Abstract: Precision nitrogen (N) management (PNM) for cotton requires in-season monitoring of crop growth parameters and N status indicators to decide fertilizer timing, placement, and application rates for optimal canopy development and yield.
arXiv:2607. 06585v1 Announce Type: cross Abstract: Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually, resulting in economic damages exceeding USD 220 billion.
arXiv:2607. 17507v1 Announce Type: new Abstract: Regional climate prediction presents unique challenges for time series foundation models, which typically process temporal patterns through single-pass inference.
arXiv:2509. 06625v3 Announce Type: replace-cross Abstract: Plants in their natural habitats endure an array of interacting stresses, both biotic and abiotic, that rarely occur in isolation.
The paper introduces RNN Maxent, a new extension of the Maxent species distribution modelling framework that replaces its fixed linear feature dictionary with a neural network—specifically a Gated Recurrent Unit (GRU)—trained end‑to‑end via backpropagation. This approach retains Maxent’s presence‑only statistical foundations, background normalisation, and probability calibration while learning nonlinearity directly from data. Applied to Desert Locust distribution modelling using 50‑day environmental time‑series, RNN Maxent outperforms standard Maxent, achieving higher ROC AUC (0.862 vs. 0.792) and F1 scores (0.671 vs. 0.590).
arXiv:2602. 17683v3 Announce Type: replace Abstract: Short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support in precision agriculture.