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市場調査レポート
商品コード
1722322
デジタル農業の市場規模、シェア、成長分析、提供別、技術別、作業別、タイプ別、地域別 - 産業予測 2025~2032年Digital Agriculture Market Size, Share, and Growth Analysis, By Offering (Advisory Services, Precision Agriculture & Farm Management), By Technology (Peripheral Technology, Core Technology), By Operation, By Type, By Region - Industry Forecast 2025-2032 |
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デジタル農業の市場規模、シェア、成長分析、提供別、技術別、作業別、タイプ別、地域別 - 産業予測 2025~2032年 |
出版日: 2025年05月03日
発行: SkyQuest
ページ情報: 英文 192 Pages
納期: 3~5営業日
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デジタル農業の世界市場規模は、2023年に223億米ドルと評価され、2024年の246億6,000万米ドルから2032年には552億2,000万米ドルに成長し、予測期間(2025-2032年)のCAGRは10.6%で成長する見通しです。
デジタル農業分野では、精密農業の需要が大幅に急増しています。精密農業は、投入コストを削減しながら作物の収量を向上させるように設計された最先端の農業手法です。この革新的な手法では、センサーやドローンを使って気温、土壌水分、養分レベルに関する重要なデータを収集し、農家がより効果的に畑を管理し、肥料や農薬の散布を最適化できるようにします。モノのインターネット(IoT)技術と精密農業ソフトウェアの統合により、農家は作物の健康状態や進捗状況をリアルタイムで監視できるようになり、より良い意思決定と農業慣行の改善につながります。こうしたデジタルソリューションへの依存の高まりは農業を変革し、最終的には生産性を向上させ、よりデータ主導の状況に適応しながら、世界中の農家の生活を改善しています。
Global Digital Agriculture Market size was valued at USD 22.3 billion in 2023 and is poised to grow from USD 24.66 billion in 2024 to USD 55.22 billion by 2032, growing at a CAGR of 10.6% during the forecast period (2025-2032).
The digital agriculture sector is experiencing a significant surge in demand for precision farming, a state-of-the-art agricultural approach designed to enhance crop yields while reducing input costs. This innovative method employs sensors and drones to collect vital data on temperature, soil moisture, and nutrient levels, enabling farmers to more effectively manage fields and optimize the application of fertilizers and pesticides. With the integration of Internet of Things (IoT) technology and precision farming software, farmers can now monitor crop health and progress in real-time, leading to better decision-making and improved agricultural practices. This growing reliance on digital solutions is transforming farming, ultimately enhancing productivity and improving the livelihoods of farmers globally as they adapt to a more data-driven landscape.
Top-down and bottom-up approaches were used to estimate and validate the size of the Global Digital Agriculture 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 Digital Agriculture Market Segments Analysis
Global Digital Agriculture Market is segmented by Offering, Technology, Operation, Type and region. Based on Offering, the market is segmented into Advisory Services, Precision Agriculture & Farm Management, Quality Management & Traceability, Digital Procurement, Agri Ecommerce and Financial Services. Based on Technology, the market is segmented into Peripheral Technology and Core Technology. Based on Operation, the market is segmented into Farming & Feeding, Monitoring & Scouting and Marketing & Demand Generation. Based on Type, the market is segmented into Hardware, Software and Services. Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America and Middle East & Africa.
Driver of the Global Digital Agriculture Market
The Global Digital Agriculture market is experiencing significant growth due to the adoption of Artificial Intelligence (AI) and the Internet of Things (IoT) in farming practices. These technologies are revolutionizing agricultural operations by providing farmers with advanced analytics that facilitate data-driven decision-making, ultimately enhancing crop yields. Additionally, AI helps in early detection of plant diseases and weather pattern predictions, improving overall efficiency. Meanwhile, IoT-enabled smart sensors monitor critical factors such as soil moisture and nutrient levels in real-time, enabling precision irrigation and fertilization. In countries like the U.S. and Canada, agritech startups are innovating with autonomous tractors, AI-powered drones for crop monitoring, and robotic harvesters, significantly reducing reliance on labor.
Restraints in the Global Digital Agriculture Market
Although the Global Digital Agriculture market offers numerous advantages, the high costs associated with adopting digital farming technologies pose a significant challenge, particularly for small and medium-sized farms in developing regions. The implementation of AI-driven farming equipment, precision irrigation systems, and cloud-based farm management tools necessitates considerable initial financial investment, which can be prohibitive for many farmers. While large agribusinesses in regions like North America and Europe can easily afford autonomous tractors, robotic harvesters, and AI-enhanced greenhouses, farmers in Southeast Asia, Africa, and Latin America often encounter financial obstacles. Moreover, limited access to affordable credit and inadequate government support further hampers their ability to embrace these advanced agricultural technologies.
Market Trends of the Global Digital Agriculture Market
The global digital agriculture market is witnessing a transformative trend driven by the rapid adoption of AI technologies, which significantly enhance farming efficiency, productivity, and sustainability. Innovations in precision farming leverage AI-powered computer vision and machine learning to facilitate real-time monitoring of crops, leading to proactive disease management and optimized irrigation strategies through tools like drones, satellite imagery, and IoT sensors. Predictive analytics further empower farmers by analyzing extensive datasets, including historical weather and soil conditions, to accurately forecast yields and optimize planting schedules. The rise of AI-driven automation, including autonomous machinery and robotic harvesters, enhances operational efficiency while minimizing labor costs. Additionally, AI-powered chatbots and virtual assistants provide farmers with immediate access to crucial insights, revolutionizing decision-making and support in agricultural practices.