AI-Driven Regenerative Agriculture for Climate Resilience: A Review
気候回復力のためのAI駆動型リジェネラティブ農業：レビュー (AI 翻訳)
Ummesara ., Chaitanya Kumar Sahu, R Ranjith, Santhosh BL, Naitik Nayan, Arunima Goswami, Dhanashila Subhash Sutar, S. B. Shinde
🤖 gxceed AI 要約
日本語
本レビューは、AI技術（機械学習、リモートセンシング、IoTなど）とリジェネラティブ農業の統合が、土壌健全性の回復、炭素隔離、気候回復力の向上にどのように貢献するかを考察している。AIによる動的モニタリングと精密管理により、持続可能な食料生産システムの実現が期待される。課題と研究の方向性も提示している。
English
This review integrates trends in AI-enabled regenerative agriculture (RA) to promote climate resilience. AI tools (ML, remote sensing, IoT) enable dynamic monitoring of soil/crop health, carbon accounting, and adaptive farm management, accelerating adoption of RA practices. The paper assesses challenges and suggests research directions for enhancing productivity, sustainability, and ecosystem restoration.
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
日本の農業は高齢化や後継者不足に直面しており、AIを活用したリジェネラティブ農業は労働力不足の解消と環境負荷低減の両立に寄与する可能性がある。また、日本政府が推進する「みどりの食料システム戦略」やカーボンニュートラル目標とも親和性が高く、SSBJのスコープ3排出量算定における農業由来の排出削減にもつながる。
In the global GX context
Globally, this review aligns with the push for climate-resilient food systems under the UNFCCC and ISSB disclosure standards for agriculture-related emissions. AI-RA integration offers scalable solutions for carbon sequestration and ecosystem services, relevant to transition finance and climate risk management. The paper provides a useful framework for policymakers and investors evaluating nature-based solutions.
👥 読者別の含意
🔬研究者:This review synthesizes current AI-RA literature, highlighting research gaps for scholars working at the intersection of AI, soil science, and climate modeling.
🏢実務担当者:Corporate sustainability teams in agri-food chains can leverage AI-RA approaches for Scope 3 carbon accounting and regenerative supply chain initiatives.
🏛政策担当者:Policymakers should note that AI-driven regenerative agriculture supports Nationally Determined Contributions (NDCs) and can be integrated into agricultural subsidies and carbon credit schemes.
📄 Abstract（原文）
Agriculture is being impacted by climate change with a multitude of challenges to address, such as increasing temperatures, unpredictable precipitation, soil erosion, biodiversity decline, and more frequent extreme weather events. Regenerative agriculture has been recognized as a more sustainable agricultural practice with regard to restoring soil health, increasing biodiversity, enhancing ecosystem services, and sequestering carbon. Meanwhile, the development of technologies such as Artificial Intelligence (AI), machine learning, deep learning, remote sensing, Internet of Things (IoT), and big data analytics are revolutionizing the management of agricultural resources and decisions. The convergence of AI and regenerative agriculture is a viable potential solution for climate resilient food production systems. AI-enabled interactive tools also allow dynamic monitoring of soil and crop health, simulation of climate risks, nutrient and water management precision, carbon accounting, and farm management adaptation. This review integrates emerging trends in AI-enabled RA, considers its potential to promote climate resilience, assesses challenges and barriers, and suggests possible research directions. The review demonstrates that AI could greatly enhance the speed at which the regenerative practices are adopted and how effective they are at improving productivity, sustainability and ecosystem restoration.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.33545/2618060x.2026.v9.i7sa.5900first seen 2026-07-27 04:46:46
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。