arXiv:2504. 18451v2 Announce Type: replace Abstract: Rapid global population growth underscores the need for digitally enabled agricultural systems that support sustainable food production and data-driven resource management for farmers and stakeholders.
arXiv:2506. 03168v2 Announce Type: replace-cross Abstract: Amid the challenges posed by global population growth and climate change, traditional agricultural Internet of Things (IoT) systems is currently undergoing a significant digital transformation to facilitate efficient big data processing.
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: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:2608. 14698v1 Announce Type: cross Abstract: Hyperlocal meteorological sensing is essential for accurate solar photovoltaic forecasting, yet professional-grade meteorological stations require investments easily exceeding 1000~USD per node, making distributed deployments economically inaccessible.
arXiv:2608. 17971v1 Announce Type: new Abstract: The impact of climate variability on food production has led to the creation of various forecasting models that uses machine learning (ML), numerical weather predictors (NWP) or a hybrid of ML-NWP models to identify structural and physical relationships between meteorological drivers and crop growth, in order to predict crop yield.
arXiv:2608. 02088v1 Announce Type: new Abstract: Reliable photovoltaic (PV) forecasts are needed for low-carbon energy systems, but newly deployed sites often have short, imperfect records.
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:2606. 13959v1 Announce Type: new Abstract: Sierra Leone's agriculture operates with almost no data-driven decision support, and no published machine learning study has examined the country's crop yields.
arXiv:2608. 06001v1 Announce Type: new Abstract: Commercial grazing systems yield irregular livestock observations, which challenge cattle growth forecasting.
arXiv:2607. 09751v1 Announce Type: new Abstract: Machine Learning (ML) algorithms have been widely used to estimate agricultural variables across diverse contexts.
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.
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.