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. The adoption of Internet of Things (IoT) technologies, capable of capturing real-time environmental (e.g., temperature, humidity) and operational (e.g., irrigation) parameters, is a crucial step toward enabling advanced applications such as AI-based yield forecasting. However, the effectiveness of such models is often constrained by limited dat
The paper leverages recent advancements in machine learning and the increasing availability of IoT agricultural data to address real-world challenges in food production, aligning with urgent global sustainability goals.
Precise yield forecasting directly impacts food security, resource allocation, and agricultural profitability, making this development crucial for optimizing global food systems.
The improved accuracy in yield forecasting shifts agricultural management from reactive to predictive, enabling more efficient resource use and better risk mitigation.
- · Agricultural technology companies
- · Farmers adopting IoT and AI
- · Food producers and distributors
- · Data analytics platforms
- · Traditional farming methods
- · Regions lacking digital infrastructure
Increased efficiency and sustainability in strawberry farming through data-driven decisions.
Expansion of AI integration across diverse agricultural sectors, leading to more resilient food supply chains.
Global food price stabilization and reduced waste through optimized production and distribution networks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG