arXiv AI

SPADE: A Large Language Model Framework for Soil Moisture Pattern Recognition and Anomaly Detection in Precision Agriculture

arXiv Machine Learning

Jun 18

A Survey on Data-Driven Models for Soil Moisture Regression and Classification

arXiv:2606. 18316v1 Announce Type: new Abstract: Soil Moisture (SM) modelling constitutes a complex spatiotemporal learning problem characterised by nonlinear environmental interactions, heterogeneous data sources, and limited ground observations.

By Ilektra Tsimpidi, George Georgoulas, Vidya Sumathy, George Nikolakopoulos

arXiv Machine Learning

Aug 19

Evaluating and improving crop-yield forecasting methods during extreme drought

The study evaluates crop‑yield forecasting methods for the 2012 Midwestern US drought, comparing non‑deep learning machine learning models with a deep learning model (VITA) using 16 meteorological predictors. It highlights challenges such as distributional dissimilarity between training and test data, spatial and temporal sparsity, and demonstrates that sample weighting and feature selection improve non‑deep learning models but not VITA. The work contrasts deep versus non‑deep learning approaches and shows how modifications can mitigate issues arising from extreme drought conditions.

By Shrey Gupta, Yi Ming, George Mohler

efficiency

arXiv AI

Aug 12

Unsupervised Detection of Groundwater Storage Anomalies in Ghana Using GRACE Satellite Data

arXiv:2608. 10233v1 Announce Type: cross Abstract: Groundwater variability in Ghana remains poorly characterized due to limited long-term in-situ observations.

By George Yamoah Afrifa, Theophilus Ansah-Narh, Marcellin Atemkeng

arXiv Machine Learning

Jul 15

Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection

arXiv:2607. 12454v1 Announce Type: new Abstract: Multivariate Time Series Anomaly Detection (MTSAD) is essential for reliability and safety in domains such as industrial process monitoring and financial risk management, yet conventional approaches rely on application-specific models that are costly to train and hard to scale.

By Martin Uray, Saverio Messineo, Roland Kwitt, Stefan Huber

rag benchmarks safety

arXiv Machine Learning

Jun 5

Intercomparison of Machine Learning Algorithms for Remote Sensing-based In-season Crop Mapping

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.

By August Posch, Jitendra Kumar, Forrest M. Hoffman, Auroop R. Ganguly

arXiv Machine Learning

Jun 10

AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning

arXiv:2602. 13807v2 Announce Type: replace Abstract: Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings.

By Xiaoyu Tao, Yuchong Wu, Mingyue Cheng, Ze Guo, Tian Gao

agents safety

arXiv AI

Jun 4

Physics-Informed Machine Learning for Short-Term Flood Prediction

arXiv:2606. 04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

safety

arXiv Machine Learning

Jun 26

Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates

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.

By Irene Iele, Giulia Romoli, Daniele Molino, Elena Mulero Ayll\'on, Filippo Ruffini, Paolo Soda, Matteo Tortora

multimodal benchmarks

arXiv Machine Learning

Jun 17

Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection

arXiv:2604. 17616v3 Announce Type: replace Abstract: Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems.

By Shashank Mishra, Karan Patil, Cedric Schockaert, Didier Stricker, Jason Rambach

rag benchmarks

arXiv Machine Learning

Sep 1

Foundation Models Meet Agriculture: Challenges Beyond Pretraining

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...

By Vishal Nedungadi, Xingguo Xiong, Marc Ru{\ss}wurm, Ioannis N. Athanasiadis

benchmarks

arXiv AI

Jun 9

Crop Recommendation and Agricultural Query Answering System Using Spatio-Temporal Graph Neural Networks and Hybrid Retrieval Augmentation

arXiv:2606. 09160v1 Announce Type: cross Abstract: This paper presents a unified system designed to support precision agriculture by integrating advanced weather prediction, crop recommendation, and a question-answering tool for farmers.

By Prajwal Thapa, Yagya Raj Pandeya

llms rag

arXiv Computer Vision

Aug 27

Embedding NDRE Trajectories into Contrastive Learning for Label-Free, Physiology-Aware Crop-Stress Staging and DSS Outputs

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.

By Shafqaat Ahmad

rag benchmarks

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