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市場調査レポート
商品コード
1670187
自律型作物管理市場の市場規模、シェア、成長分析、コンポーネント別、作物タイプ別、技術タイプ別、展開モデル別、用途別、エンドユーザー別、地域別 - 産業予測、2025-2032年Autonomous Crop Management Market Size, Share, and Growth Analysis, By Component (Software, Services), By Crop Type (Row Crops, Field Crops), By Technology Type, By Deployment Model, By Application, By End-User, By Region - Industry Forecast 2025-2032 |
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自律型作物管理市場の市場規模、シェア、成長分析、コンポーネント別、作物タイプ別、技術タイプ別、展開モデル別、用途別、エンドユーザー別、地域別 - 産業予測、2025-2032年 |
出版日: 2025年02月28日
発行: SkyQuest
ページ情報: 英文 199 Pages
納期: 3~5営業日
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自律型作物管理の世界市場規模は2023年に24億米ドルと評価され、2024年の26億9,000万米ドルから2032年には67億1,000万米ドルに成長し、予測期間(2025-2032年)のCAGRは12.1%で成長する見通しです。
自律型作物管理市場は、AI、機械学習、IoT技術の統合によって精密農業の需要が高まり、大きな成長を遂げています。自律型機械、ドローン、スマートセンサーを含むこれらのイノベーションは、資源効率を最適化しながら作物の収量を向上させる。リアルタイムのデータ収集により、農家は効率的な慣行を採用できるようになり、労働力への依存が減り、持続可能な農業が促進されます。自律走行型トラクターやロボット収穫機などの主要な進歩は、正確な作業を保証し、サブスクリプションベースの「サービスとしての農業(FaaS:Farming as a Service)」モデルは、最先端の自動化への手頃なアクセスを提供します。John DeereやGuardian Agricultureのような企業は、完全自律型機器や環境に優しい航空穀物管理ソリューションで農業に革命を起こしています。投資と提携が盛んになるにつれ、この分野は急速な革新を遂げる態勢が整い、大規模農家と零細農家の両方が持続可能な生産性向上を達成できるよう支援しています。
Global Autonomous Crop Management Market size was valued at USD 2.4 billion in 2023 and is poised to grow from USD 2.69 billion in 2024 to USD 6.71 billion by 2032, growing at a CAGR of 12.1% during the forecast period (2025-2032).
The autonomous crop management market is experiencing significant growth due to the rising demand for precision agriculture, driven by the integration of AI, machine learning, and IoT technologies. These innovations, including autonomous machinery, drones, and smart sensors, enhance crop yields while optimizing resource efficiency. Real-time data collection enables farmers to adopt efficient practices, reducing labor dependency and promoting sustainable agriculture. Key advancements like autonomous tractors and robotic harvesters ensure precise operations, while subscription-based farming-as-a-service models provide affordable access to cutting-edge automation. Companies like John Deere and Guardian Agriculture are revolutionizing agriculture with fully autonomous equipment and eco-friendly aerial crop management solutions. As investments and partnerships flourish, the sector is poised for rapid innovation, supporting both large-scale and smallholder farmers in achieving sustainable productivity enhancements.
Top-down and bottom-up approaches were used to estimate and validate the size of the Global Autonomous Crop Management market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.
Global Autonomous Crop Management Market Segments Analysis
Global Autonomous Crop Management Market is segmented by Component, Crop Type, Technology Type, Deployment Model, Application, End-User and region. Based on Component, the market is segmented into Software and Services. Based on Crop Type, the market is segmented into Row Crops, Field Crops, Horticultural Crops and Others. Based on Technology Type, the market is segmented into Precision Agriculture, Satellite Imagery, Soil and Plant Monitoring and Artificial Intelligence. Based on Deployment Model, the market is segmented into On-Premise and Cloud-Based. Based on Application, the market is segmented into Yield Optimization, Water Management, Pest and Disease Management and Labor Automation. Based on End-User, the market is segmented into Farmers, Agribusinesses and Research Institutions. Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America and Middle East & Africa.
Driver of the Global Autonomous Crop Management Market
The global market for autonomous crop management is significantly propelled by the growing need for precision agriculture. The integration of AI-powered drones, self-driving tractors, and IoT-enabled monitoring systems enhances operational efficiency and reduces costs, leading to more efficient resource utilization. In light of increasing worries about food security and the sustainability of farming practices, there has been a swift adoption of autonomous technologies among farmers, enabling them to achieve greater productivity levels. As these innovative solutions continue to evolve, they play a crucial role in addressing the challenges faced in modern agriculture and meet the rising global food demands.
Restraints in the Global Autonomous Crop Management Market
The Global Autonomous Crop Management market faces significant challenges due to the limited technical expertise among farmers and agribusinesses, which obstructs the large-scale adoption of these innovative solutions. Implementing AI-driven tools, IoT sensors, and robotics demands a level of sophistication and knowledge that is often lacking in rural farming communities. This gap in technical skills is exacerbated by the absence of well-structured training programs and digital literacy initiatives, resulting in slow integration of autonomous crop management technologies. Consequently, fostering adequate education and resources is essential for enhancing the capabilities of farmers and accelerating market growth.
Market Trends of the Global Autonomous Crop Management Market
The global autonomous crop management market is experiencing substantial growth, propelled by the expansion of AI and machine learning technologies in precision farming. These advancements enable farmers to achieve real-time monitoring and predictive analytics, which significantly improve decision-making processes for irrigation, pest control, and yield optimization. As a result, the agriculture sector is witnessing a shift towards more efficient, cost-effective, and sustainable practices that enhance productivity on a large scale. Ongoing investments in research and development further drive innovation, making AI-driven solutions increasingly accessible to farmers worldwide, thereby solidifying the trend towards smarter farming methodologies and sustainable agricultural practices.