Artificial Intelligence based on behavioral recognition and optimization for low carbon fertilization in agriculture
農業における低炭素施肥のための行動認識と最適化に基づく人工知能 (AI 翻訳)
Yan Hao, Yanmei Yuan, Hui Liu
🤖 gxceed AI 要約
日本語
本研究は、従来の施肥モニタリング手法の時系列的な栄養動態把握の限界を克服するため、マルチソース時系列農業データに基づく施肥行動認識と低炭素意思決定最適化のデータ駆動型手法を提案する。LSTMモデルと注意機構を用いた行動認識、MILPによる炭素排出最小化の施肥計画を組み合わせ、エッジクラウド連携アーキテクチャで実現。30日目の予測誤差8.5%、炭素排出原単位0.365kgCO2-eq/kg肥料を達成。
English
This study proposes a data-driven approach for fertilization behavior recognition and low-carbon decision optimization using multi-source agricultural time-series data. It combines LSTM with attention mechanism for behavior recognition and MILP for carbon-minimizing fertilization planning, implemented via edge-cloud architecture. Achieves 8.5% prediction error at day 30 and carbon intensity of 0.365 kgCO2-eq/kg fertilizer.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では農業の脱炭素化が急務であり、スマート農業技術への期待が高い。本研究はAIを活用した精密施肥により炭素排出削減と生産性向上を両立する手法を示しており、日本の農業DX政策（みどりの食料システム戦略等）に資する。
In the global GX context
Globally, AI-driven precision agriculture is a key pathway for agricultural decarbonization. This paper demonstrates an integrated framework combining behavior recognition and optimization, relevant to global efforts in climate-smart agriculture and carbon footprint reduction.
👥 読者別の含意
🔬研究者:AIと農業の融合研究に携わる研究者は、時系列データからの行動認識と最適化の統合手法を参考にできる。
🏢実務担当者:農業法人や農業ITベンダーは、本手法を基にした低炭素施肥システムの実装可能性を検討できる。
🏛政策担当者:農業分野のカーボンニュートラル政策担当者は、AI技術を活用した排出削減策の一例として参照できる。
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1038/s41598-026-57895-3first seen 2026-06-17 05:03:34 · last seen 2026-06-17 07:11:23
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