Ai-driven crop management optimization for carbon sequestration and yield

Overview

Soil carbon sequestration in croplands offers significant potential for mitigating climate change, but developing optimal management practices that simultaneously maximize stored carbon and crop yield remains a major challenge. This research applies deep reinforcement learning (RL), a modern artificial intelligence technique for sequential decision-making, to agricultural management. By leveraging deep neural networks as powerful approximators, the approach aims to achieve superior performance compared to human experts in crafting management policies that balance carbon sequestration with crop productivity, even under uncertain weather conditions.

The solution addresses a critical need in sustainable agriculture by providing data-driven management recommendations for fertilization, irrigation, and tillage. This enables farmers and agricultural stakeholders to make informed decisions that contribute to climate goals while maintaining or improving yields.

Technical specifications

Core approach:

• Deep reinforcement learning applied to sequential agricultural decision-making
• Crop and soil simulator built on APSIM (Agricultural Production Systems sIMulator), one of the most widely used agricultural modeling tools
• Simulator trained and evaluated with real-world soil and weather data from Illinois, US

RL problem formulation:

• Management practices (actions): fertilization, irrigation, and tillage decisions
• States: soil conditions, weather conditions, and crop conditions
• Reward functions: designed to evaluate management decisions based on both carbon sequestration and crop yield outcomes

Algorithms used:

• State-of-the-art deep RL algorithms, including deep Q-learning
• Deep neural networks serving as function approximators for policy learning
• Policy comparison against human expert benchmarks

Technology readiness level

The project is in active development with support from C3.ai for AI-driven soil carbon sequestration research. An APSIM-based crop and soil simulator is currently being built in collaboration with a crop science research group. The next phase involves formulating the RL problem, training management policies with deep RL algorithms, and validating performance against human expert management practices. The technology is at an early-to-mid research stage, with simulator development and initial RL training underway.