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Artificial Intelligence based on behavioral recognition and optimization for low carbon fertilization in agriculture
Yan Hao1, Yanmei Yuan2,3, Hui Liu4
1Tempering Business School, Taishan College of Science and Technology, Tai'an, 271000, China.
Scientific Reports
|June 15, 2026
Summary
This study introduces an AI-driven framework for recognizing fertilization behaviors and optimizing low-carbon decisions using agricultural time-series data. The approach enhances precision management by reducing carbon emissions and improving fertilizer efficiency.
Area of Science:
- Agricultural Science
- Environmental Science
- Data Science
Background:
- Conventional fertilization monitoring lacks temporal continuity, hindering real-time, low-carbon precision agriculture.
- Existing methods cannot support real-time behavioral identification or carbon emission-constrained decision-making.
Purpose of the Study:
- To develop a data-driven approach for fertilization behavior recognition and low-carbon decision optimization.
- To enable dynamic nutrient trend capture for low-carbon precision management.
Main Methods:
- Utilized multi-source agricultural time-series data (soil, meteorological, crop growth) with an edge-cloud architecture.
- Employed a bidirectional long short-term memory (LSTM) network with attention for fertilization event recognition.
- Formulated a mixed integer linear programming (MILP) model for optimizing fertilization plans to minimize carbon emissions.
Main Results:
- Achieved an 8.5% prediction error on day 30 for the behavior recognition model.
- Reduced carbon emission intensity to 0.365 kgCO₂-eq/kg fertilizer.
- Demonstrated the feasibility of AI-driven decision framework for behavior recognition and carbon emission control.
Conclusions:
- The proposed AI-driven framework effectively recognizes fertilization behaviors and optimizes low-carbon decisions.
- This approach supports dynamic nutrient management, crucial for sustainable and precise agriculture.
- The system offers a viable solution for reducing environmental impact in fertilizer application.
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