Artificial Intelligence-based irrigation and nitrogen optimization in Cotton (Precision Agriculture Research, 2025).

In 2026, the application of Artificial Intelligence (AI) in cotton production has evolved into a highly integrated “closed-loop” system. Unlike the separate irrigation and fertilization schedules of the past, 2026 research highlights Coupled Optimization, where AI manages water and nitrogen (N) as a single, interdependent unit to maximize Nitrogen Use Efficiency (NUE) and fiber quality.

🛰️ 1. The Multi-Sensor Data Fusion Layer

Precision cotton management in 2026 relies on Hyperspectral and Thermal data fusion.

• The AI “X-Ray”: By combining UAV-based thermal imagery (which detects water stress via canopy temperature) with hyperspectral data (which detects Nitrogen status via the Red-Edge position), AI models can now differentiate between a “thirsty” plant and a “hungry” one.
• Feature Selection: Leading research (e.g., Frontiers in Plant Science, 2025) has identified five critical spectral features—NDVI, NDRE, GRVI, and specific Mean Red/Blue bands—that allow Random Forest algorithms to estimate cotton plant nitrogen concentration with 97%–98% accuracy.

🤖 2. Advanced AI Modeling Techniques

Two specific AI architectures have become the “standard” in 2026 for managing cotton’s complex growth stages:

A. Nested Dual-Agent Reinforcement Learning (NDRL)

Published in late 2025, the NDRL framework uses two “agents” to manage the field:

• The Parent Agent: Identifies “Macro” actions (e.g., determining the total amount of N and water needed for the next two weeks based on yield projections).
• The Child Agent: Refines those actions daily based on real-time Water Stress Factors (WSF) and Nitrogen Stress Factors (NSF).
• Impact: This approach has demonstrated a 5.6% increase in water productivity and a 6.3% increase in nitrogen partial factor productivity.