arXiv AI
Sep 1

Cross-Regional Grapevine Cold Hardiness Prediction via Learned Multimodal Latent Representations

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.

By William Solow, Paola Pesantez-Cabrera, Markus Keller, Lav Khot, Sandhya Saisubramanian, Alan Fern
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
Hugging Face Trending Papers
Jul 22

Forecasting the Number of Harvest-ready Fruits of Sweet Peppers Using Multimodal Time-Series Data

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.

arXiv Machine Learning
Sep 23

Event-Based Early Warning of Vineyard Disease Risk from Environmental Time Series

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.

By Ivica Dimitrovski, Ivan Kitanovski, Danco Davcev, Slobodan Kalajdziski, Kosta Mitreski