農業・持続可能性・気候緩和の推進に向けた人工知能の現行応用
Current applications of artificial intelligence for advancing agriculture, sustainability and climate mitigation (原題)
Mostafa Farajpour
🤖 gxceed AI 要約
日本語
本レビューは、精密灌漑・土壌管理・収量予測・病害虫監視・自律ロボティクス・LLM意思決定支援など農業AIの応用を横断的に整理する。予測精度だけでなく汎化性・説明可能性・経済性・相互運用性が実装の鍵だと指摘。Vision Transformerや基盤モデル、デジタルツイン等の新技術と、データ所有権・公平性・小規模農家のアクセスを含む責任あるAIガバナンスの必要性を論じる。
English
This review synthesizes AI applications across agriculture—precision irrigation, soil and nutrient management, yield prediction, pest monitoring, robotics, and LLM-based decision support. It argues adoption hinges not only on accuracy but on generalizability, explainability, economic feasibility, and interoperability. Emerging tools (Vision Transformers, foundation models, digital twins, federated learning) and responsible-AI governance—data ownership, fairness, smallholder access—are discussed as priorities.
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
農業分野のAI活用は、日本のGX政策における食料安全保障・スマート農業・気候適応の文脈と接続しうるが、SSBJや有報開示、Scope3算定への直接的示唆は乏しい。日本の読者には、AI導入時のガバナンスや小規模経営体への公平性議論が参考になる。
In the global GX context
While not a disclosure paper, it speaks to the climate-adaptation and sustainable-land-use dimension of global GX frameworks (e.g., TNFD, CSRD's environmental objectives) by mapping where AI can support measurable agricultural sustainability outcomes. Its governance discussion—data ownership, fairness, equitable access—is relevant to emerging expectations for responsible AI in sustainability reporting.
👥 読者別の含意
🔬研究者:農業AIの実装ギャップ（汎化性・説明可能性・データ断片化）と新技術動向を整理する枠組みとして有用。
🏢実務担当者:農業・食品企業のサステナビリティ担当が、AI導入時のガバナンスやデータ管理の論点を把握するのに役立つ。
🏛政策担当者:スマート農業政策やAIガバナンス設計において、小規模農家のアクセス確保と公平性の論点を検討する材料になる。
📄 Abstract（原文）
Artificial intelligence (AI) is reshaping agriculture by enabling data-driven decision-making across crop production systems and supporting the transition toward more sustainable, resilient, and climate-smart farming. This review critically synthesizes advances in AI applications spanning precision irrigation, nutrient and soil management, yield prediction, pest and disease monitoring, computer vision, autonomous agricultural robotics, and large language model (LLM)-based decision support systems. Beyond summarizing technological developments, the review evaluates their practical deployment, emphasizing that successful agricultural AI depends not only on predictive accuracy but also on model generalizability, explainability, economic feasibility, interoperability, and integration with existing agronomic knowledge. Emerging technologies including Vision Transformers, foundation models, Digital Twins, multimodal AI, Retrieval-Augmented Generation, and federated and continual learning—are discussed as important directions for overcoming limitations related to data scarcity, environmental variability, and scalable field implementation. The review highlights that responsible AI adoption requires transparent governance frameworks addressing data ownership, privacy, algorithmic fairness, cybersecurity, and equitable access, particularly for smallholder farming systems. Although numerous AI models report outstanding performance, widespread agricultural adoption remains constrained by cross-region validation, fragmented datasets, infrastructure requirements, and insufficient real-world deployment. Future progress will depend on multidisciplinary collaboration integrating advances in artificial intelligence, sensing technologies, robotics, crop science, and agricultural engineering to develop robust, trustworthy, and farmer-centered decision-support systems. By critically evaluating technological innovations and implementation challenges, this review provides a framework for understanding the current state of AI in agriculture while identifying research priorities that will shape the next generation of sustainable and climate-resilient agricultural systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s44163-026-02540-1first seen 2026-11-06 04:37:11
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