arXiv:2508. 03898v2 Announce Type: replace-cross Abstract: Accurate prediction of grape phenology is essential for timely vineyard management decisions, such as scheduling irrigation and fertilization, to maximize crop yield and quality.
The paper introduces a new framework for predicting grapevine cold hardiness that learns transferable latent representations capturing region-specific variation. By inferring embeddings from cultivar descriptions, growing region text, and limited historical data, the method supports zero‑shot and few‑shot transfer to unseen regions. Experiments across six North American regions show that this approach outperforms existing models, delivering more accurate predictions and better performance in data‑scarce areas.
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.
arXiv:2511. 09789v2 Announce Type: replace Abstract: Recent advances in deep forecasting models have achieved remarkable performance, yet most approaches still struggle to provide both accurate predictions and interpretable insights into temporal dynamics.
Accurate yield forecasting at the individual-plant level is critical for precision agriculture and supply-chain planning, yet public datasets capturing both visual growth dynamics and per-plant measurement labels are scarce. In this paper, we introduce a novel, annotated image time-series dataset of 691 sweet pepper plants monitored over two growing seasons, comprising 4837 images with per-plant fruit counts categorized by maturity.
The paper proposes an event-based approach to predict transitions into vineyard disease‑risk periods within a 3–7 day window, rather than daily disease status. It defines events only after a minimum disease‑free gap to reduce label fragmentation and uses multi‑year agro‑meteorological data to capture humidity, rainfall, temperature, and seasonal patterns. Experiments with XGBoost, LSTM, and TCN models show that this formulation improves short‑horizon warning, highlighting trade‑offs among recall, lead time, and false alerts.
The paper proposes a method for predicting future snow water equivalent (SWE) across the Western United States by first removing spatial correlations using a Gaussian Process-based linear transformation, then training a long short-term memory (LSTM) neural network on the decorrelated data. This separation of spatial and temporal components improves predictive accuracy compared to baseline models. Additionally, the authors incorporate conformal prediction to provide distribution‑free uncertainty estimates for SWE forecasts.
arXiv:2608.29008v1 Announce Type: new Abstract: Accurate forecasting of grape berry temperature (Tb) is essential for enabling timely heat stress management in vineyards. In this study, a feed-forwar...
arXiv:2609.13477v1 Announce Type: new Abstract: Predictive Process Monitoring (PPM) forecasts how ongoing organizational processes unfold, enabling information systems to move beyond execution suppor...
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.
arXiv:2607. 10208v1 Announce Type: cross Abstract: Accurate meteorological forecasting is essential for agricultural planning, irrigation management, and environmental decision support.
The paper introduces m-WCN, an end‑to‑end deep learning framework that neuralizes multi‑wavelet decomposition to jointly extract temporal patterns and frequency components from time series. Two task‑specific architectures built on m‑WCN—TFBC for classification and FTB for forecasting—are shown to outperform baseline models on 64 UCR datasets and seven forecasting benchmarks, achieving average improvements of nearly 20% in both tasks. The approach leverages trainable convolutional operators and orthogonality constraints to produce interpretable multi‑resolution representations.