A low-cost AI-based sensing approach to quantify ammonia volatilization as a driver of indirect greenhouse gas emissions
低コストAIベースセンシングによるアンモニア揮発の定量化：間接温室効果ガス排出の要因として (AI 翻訳)
Ünal Kızıl, Cafer Türkmen, Yakup Çıkılı, Sait Can Yücebaş, Ali Sümer
🤖 gxceed AI 要約
日本語
本研究は、低コストで携帯可能なAI強化電子鼻システムを開発し、施肥農地からのアンモニア(NH₃)揮発を定量化する。NH₃は非温室効果ガスだが、その揮発は強力な温室効果ガスである亜酸化窒素(N₂O)の間接排出に寄与する。機械学習アルゴリズム（特に勾配ブースティング）を用いて高精度（R²=0.84）でNH₃損失を予測し、デジタル窒素管理戦略を支援する。これにより、肥料由来の窒素損失を削減し、農業環境負荷を低減する精密農業ソリューションに貢献する。
English
This paper presents a low-cost, AI-enhanced electronic nose system for quantifying ammonia (NH₃) volatilization from fertilized soils, which contributes to indirect nitrous oxide (N₂O) emissions. Using machine learning, Gradient Boosting achieved R²=0.84 for NH₃ prediction. The system enables real-time monitoring to support digital nitrogen management and reduce agricultural GHG emissions.
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
日本の農業分野では、化学肥料の使用によるN₂O排出が問題となっている。本手法は低コストでリアルタイムモニタリングを可能にし、SSBJや農業分野の脱炭素化に貢献する可能性がある。また、日本のスマート農業技術としても期待される。
In the global GX context
Globally, agriculture contributes ~10% of GHG emissions, with N₂O from nitrogen fertilizers being a key component. This AI-based sensing approach offers a scalable solution for monitoring NH₃ losses, aligning with IPCC guidelines and precision agriculture goals. It supports climate-smart agriculture and could be integrated into carbon accounting frameworks.
👥 読者別の含意
🔬研究者:The paper demonstrates a novel application of machine learning to NH₃ monitoring, achieving high predictive accuracy with low-cost sensors, relevant for GHG accounting and precision agriculture research.
🏢実務担当者:Farmers and agtech companies can use this e-nose system for real-time nitrogen management to reduce fertilizer costs and indirect N₂O emissions.
🏛政策担当者:This technology supports national greenhouse gas inventories by providing low-cost monitoring of indirect N₂O emissions from agriculture, aiding policy on fertilizer use.
📄 Abstract（原文）
This study presents the development of a low-cost, portable, and AI-enhanced electronic nose (e-nose) system for quantifying ammonia (NH₃) volatilization from fertilized agricultural soils, with a specific emphasis on its implications for indirect greenhouse gas concentrations. Although NH₃ is not a greenhouse gas itself, its volatilization contributes significantly to indirect nitrous oxide (N₂O) emissions, one of the most potent GHGs regulated under IPCC guidelines. The proposed system integrates a MICS-6814 metal oxide sensor, ESP32 microcontroller, cloud-based data transfer, and machine learning algorithms to provide real-time monitoring and predictive analysis of NH₃ losses. Time-series sensor data were normalized, converted into area-under-the-curve (AUC) metrics, and modeled using eight machine learning algorithms. After preprocessing and hyperparameter tuning, Gradient Boosting achieved the highest performance (R² = 0.84; MAE=0.86). Laboratory evaluations demonstrated strong correlations between AUC values and NH₃-N measurements obtained through classical boric acid trapping, validating the system’s accuracy. The findings confirm that rapid detection of NH₃ volatilization can support digital nitrogen management strategies, reduce fertilizer-derived nitrogen losses, and ultimately help mitigate indirect N₂O emissions by minimizing surplus reactive nitrogen in agricultural fields. By enabling real-time emission monitoring through a low-cost digital platform, this research contributes to emerging precision agriculture solutions aimed at reducing the environmental footprint of nitrogen fertilization.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.4081/jae.2026.2097first seen 2026-06-18 06:40:49
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