arXiv:2605.31276v1 Announce Type: new Abstract: Accurately modeling crop response to Nitrogen (N) fertilization is a fundamental challenge in precision agriculture, as it impacts both economic returns and environmental sustainability. Existing approaches either rely on predefined parametric forms or opaque machine learning models, limiting their ability to interpret or discover site-specific functional relationships from data. In this work, we propose a neuro symbolic regression (SR) approach to learn parametric N-response curves without assuming a predefined functional form. Our approach inte
Advances in neuro-symbolic regression are enabling more interpretable and adaptable AI models, specifically in domains like precision agriculture where nuanced functional relationships are critical.
This development allows for data-driven, site-specific optimization of agricultural inputs, directly impacting economic efficiency and environmental sustainability for a critical sector.
Farmers and agricultural scientists can now use AI to discover optimal nitrogen fertilization strategies without relying on pre-defined models, leading to more precise and less wasteful practices.
- · Precision agriculture technology providers
- · Farmers
- · Agricultural AI researchers
- · Environmental sustainability initiatives
- · Traditional fertilizer companies (if not adapting)
- · Less efficient agricultural practices
Reduced nitrogen fertilizer runoff and enhanced crop yields due to optimized application.
Increased adoption of AI and data-driven methods across other agricultural inputs and resource management.
Potential for new business models centered on real-time, adaptive farm management services, significantly altering the food production supply chain.
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Read at arXiv cs.LG