<p>This study addresses the limitation of conventional fertilization monitoring methods that fail to capture dynamic nutrient trends for low-carbon precision management by proposing a data-driven approach for fertilization behavior recognition and low-carbon decision optimization based on multi-source agricultural time-series data. Traditional approaches lack temporal continuity and are unable to support real-time behavioral identification or carbon emission-constrained decision-making. The long short-term memory (LSTM) model is selected for its ability to process long-sequence heterogeneous sensor data and accurately recognize sparse fertilization events under environmental noise, while MILP is used to formulate a globally optimal fertilization plan that minimizes carbon emissions subject to crop nitrogen demand and environmental safety constraints. By deploying soil, meteorological, and crop growth sensors to establish an edge-cloud collaborative architecture, this method enables real-time collection and feature extraction of multi-source heterogeneous farmland data. A behavior recognition model combining a bidirectional LSTM network with an attention mechanism is developed to accurately annotate fertilization events and their temporal and spatial parameters. Carbon equivalent is calculated based on nitrogen dynamic balance and lifecycle carbon emission factors. Using carbon emission minimization as the objective function and crop nitrogen requirement and environmental safety as constraints, a mixed integer linear programming (MILP) model is constructed to generate a low-cost, high-yield fertilization plan. Results show that the system’s prediction error is 8.5% on day 30, and carbon emission intensity is reduced to 0.365 kgCO₂-eq/kg fertilizer, supporting the feasibility of this AI-driven decision framework in terms of behavior recognition accuracy and carbon emission control effectiveness under the tested plot conditions.</p>