農業分析市場レポート：2031年までの動向、予測、競合分析

世界の農業分析市場の将来は、農家、農学者、農業関連ビジネス、農業研究者、政府機関の各市場における機会で有望視されています。世界の農業分析市場は、2025年から2031年までのCAGRが12.7%で、2031年までに推定37億米ドルに達すると予想されます。この市場の主な促進要因は、最適な資源利用のニーズの高まり、改善された農業技術を展開するための政府イニシアチブの高まり、IoTデバイスの採用の増加です。

• Lucintelの予測では、農業タイプ別では、畜産が予測期間中に最も高い成長を遂げる見込みです。
• 最終用途別では、農学者が最も高い成長を遂げると予想されます。
• 地域別では、北米が農業生産高の拡大とデジタル農業技術への投資拡大により、予測期間中も最大地域であり続けると思われます。

農業分析市場の戦略的成長機会

農業分析市場は、さまざまな用途でデータ駆動型技術の採用が増加していることが大きな成長の原動力となっています。世界の食糧需要と持続可能性の目標を満たすために農業慣行が進化するにつれて、いくつかの戦略的成長機会が出現します。これらの機会は農業分析のさまざまなアプリケーションにまたがり、革新と拡大の可能性を提供します。主要なアプリケーションに焦点を当てることで、企業や利害関係者はこれらの機会を活用して、農業セクターの生産性、効率性、持続可能性を高めることができます。

• 精密農業：精密農業は、GPS技術、センサー、データ分析の進歩によって、農業分析市場における主要な成長機会となっています。このアプリケーションは、水、肥料、農薬などの作物投入を特定の圃場条件に合わせて正確に管理することを可能にします。資源の利用を最適化し無駄を最小限に抑えることで、精密農業は作物の収量を高め、環境への影響を軽減します。持続可能な農法と資源管理の効率化に対する需要の高まりが、精密農業ソリューションの拡大を後押ししています。
• 作物管理と収量予測：作物管理と収量予測のための高度な分析は、主要な成長分野です。衛星画像、気象予測、過去の作物実績からのデータを活用することで、これらのツールは作物の収量と健康状態の正確な予測を提供します。これにより農家は、作付け、収穫、資源配分に関して、情報に基づいた意思決定を行うことができます。作物のパフォーマンスを予測する能力は、農業計画を最適化し、収量の変動に伴うリスクを軽減するのに役立ち、農場全体の生産性と収益性をサポートします。
• 土壌の健康モニタリング：土壌の健全性モニタリングは、農業分析の重要なアプリケーションとして人気を集めています。養分レベル、水分含有量、pHなどの土壌特性を分析するテクノロジーは、土壌管理のための貴重な洞察を提供します。このアプリケーションは、農家が最適な土壌条件を維持し、作物の生育を改善し、土壌の肥沃度を長期的に維持するのに役立ちます。土壌の健康が農業の持続可能性に与える影響に対する認識が高まるにつれて、土壌モニタリング・ソリューションへの投資が増加し、この分野に大きな成長機会がもたらされます。
• 家畜モニタリング：家畜モニタリングは、家畜の健康と生産性の向上に焦点を当てた農業分析の新たなアプリケーションです。ウェアラブルセンサー、GPS追跡、データ分析を使用するソリューションは、動物の行動、健康状態、生産性に関するリアルタイムの洞察を提供します。このアプリケーションにより、農家は問題を早期に発見し、給餌方法を最適化し、牛群管理全体を強化することができます。動物福祉と効率的な家畜管理が重視されるようになり、高度な家畜モニタリング技術の需要が高まっています。
• サプライチェーンの最適化：サプライチェーンの最適化は、農業分析における戦略的成長機会です。農場から食卓までの農産物の動きを追跡・分析するデータ分析プラットフォームは、業務の合理化、無駄の削減、トレーサビリティの向上に役立ちます。サプライチェーン管理を強化することで、これらのソリューションはより良い意思決定をサポートし、より効率的で持続可能な農業サプライチェーンに貢献します。

農業分析の戦略的成長機会は、精密性、効率性、持続可能性を高めることで、農業セクターの将来を形作る。精密農業、作物管理、土壌健康モニタリング、家畜モニタリング、サプライチェーン最適化のアプリケーションは、イノベーションと成長を促進します。これらの機会が発展し続けるにつれて、農業慣行に大きな影響を与え、生産性の向上と農業へのより持続可能なアプローチに貢献することが期待されます。

農業分析市場の促進要因・課題

農業分析市場は、その市場開拓を形作る様々な促進要因・課題の影響を受けています。技術的進歩、経済的要因、規制への配慮は、市場の成長を促進し、障害に対処する上で重要な役割を果たします。利害関係者が市場を効果的にナビゲートし、リスクを軽減しながら成長の機会を活用するためには、これらの促進要因・課題を理解することが不可欠です。

農業分析市場を牽引する要因は以下の通り：

• 技術の進歩：センサー、データ分析、AIの急速な技術進歩が農業分析の成長を促進します。土壌や作物モニタリング用の高度なセンサー、予測分析用の機械学習アルゴリズム、空撮用のドローンなどの技術革新は、データ収集と分析を強化します。これらの技術は精密農業を可能にし、資源管理を改善し、作物収量を最適化します。テクノロジーが進化し続けることで、新たな機能と効率性が提供され、農業分析市場を前進させる。
• 食糧安全保障に対する需要の増加：世界人口の増加と食糧需要の増加は、農業分析市場の重要な促進要因です。こうした需要に対応するため、農業の生産性と効率を高める必要があります。農業分析は、作物の収量を最適化し、資源を効果的に管理し、食糧安全保障を確保するのに役立つツールと洞察を提供します。より多くの食糧を生産しなければならないという圧力が強まる中、持続可能な農業を実現するにはデータ主導型ソリューションの採用が不可欠となっています。
• 持続可能性の重視：持続可能性は農業分析の主要な促進要因であり、環境への影響を低減し、環境に優しい農法を推進することが重視されています。精密農業、土壌の健全性モニタリング、資源の最適化をサポートするアナリティクスソリューションは、廃棄物を最小限に抑え、資源を節約することで、持続可能性の目標に合致します。環境問題に対する意識の高まりと、持続可能な慣行を採用するよう求める規制当局の圧力が、より環境に優しい農業部門に貢献するアナリティクス・ソリューションへの需要を後押ししています。
• 政府の支援と投資：農業技術とデータ分析に対する政府の取り組みと投資は、市場の成長を促進します。多くの政府は、農業における先端技術の採用を支援するための政策を実施し、資金を提供しています。こうした投資は、農業分析ソリューションの開発と展開を加速させ、農家にとってより利用しやすいものとし、市場全体の成長を促進します。
• IoTとビッグデータの採用拡大：モノのインターネット（IoT）デバイスとビッグデータ分析の採用は、農業におけるイノベーションを促進します。IoTセンサーは農作業の様々な側面に関するリアルタイムデータを提供し、ビッグデータ分析プラットフォームは意思決定のための洞察を提供します。これらの技術を統合することで、農家はより効率的に作業を監視・管理できるようになり、効率性と生産性の向上につながります。IoTとビッグデータの普及は、農業分析の成長に大きく貢献しています。

農業分析市場の課題は以下の通り：

• 高い導入コスト：農業分析市場における主な課題の1つは、先進技術の導入コストが高いことです。センサー、データ分析プラットフォーム、その他の技術に必要な初期投資は、中小規模の農場にとっては法外な金額になる可能性があります。高い導入コストは、農業分析・ソリューションの採用を制限し、一部の利害関係者にとって参入障壁となり、市場全体の成長に影響を与える可能性があります。
• データプライバシーとセキュリティの懸念：データのプライバシーとセキュリティへの懸念は、農業分析市場における重要な課題です。農業分析には機密データの収集と分析が含まれるため、データ侵害や悪用のリスクがあります。強固なデータ保護対策を確保し、プライバシーの懸念に対処することは、信頼を維持し、アナリティクスソリューションの採用を促進する上で極めて重要です。厳格なセキュリティプロトコルの必要性は、市場にとって継続的な課題となっています。
• 開発途上地域におけるテクノロジーへのアクセス制限：新興国市場では高度な技術やインフラへのアクセスが限られていることが、農業分析市場の課題となっています。技術インフラが不十分な地域では、データ主導型ソリューションの導入が遅れる可能性があります。技術ギャップを埋め、手頃な価格で利用しやすいソリューションを提供することは、農業分析の普及を拡大し、そのメリットを世界規模で確実に実現するために不可欠です。

農業分析市場は、技術の進歩、食糧安全保障の需要、持続可能性の目標、政府の支援、IoTとビッグデータの導入などの影響を大きく受けています。しかし、導入コストの高さ、データプライバシーへの懸念、新興国地域での技術アクセスの制限といった課題も市場に影響を与えています。農業分析が継続的な成長と進化を遂げ、この分野が生産性の向上と持続可能性という目標を達成できるようにするためには、こうした促進要因と課題に対処することが極めて重要です。

目次

第1章 エグゼクティブサマリー

第2章 世界の農業分析市場：市場力学

• イントロダクション、背景、分類
• サプライチェーン
• 業界の促進要因と課題

第3章 2019年から2031年までの市場動向と予測分析

• マクロ経済動向（2019-2024年）と予測（2025-2031年）
• 世界の農業分析市場の動向（2019～2024年）と予測（2025～2031年）
• 世界の農業分析市場：農業タイプ別
  • 精密農業
  • 畜産
  • 養殖業
  • 垂直農法
  • その他
• 世界の農業分析市場：農場規模別
  • 大規模農場
  • 中小規模農場
• 世界の農業分析市場：技術別
  • リモートセンシングと衛星画像
  • 地理情報システム
  • ロボット工学と自動化
  • ビッグデータとクラウドコンピューティング
  • 視覚化とレポート
  • ブロックチェーン技術
  • その他
• 世界の農業分析市場：最終用途別
  • 農家
  • 農学者
  • 農業関連産業
  • 農業研究者
  • 政府機関
  • その他

第4章 2019年から2031年までの地域別市場動向と予測分析

• 地域別：世界の農業分析市場
• 北米の農業分析市場
• 欧州の農業分析市場
• アジア太平洋の農業分析市場
• その他地域の農業分析市場

第5章 競合分析

• 製品ポートフォリオ分析
• 業務統合
• ポーターのファイブフォース分析

第6章 成長機会と戦略分析

• 成長機会分析
  • 世界の農業分析市場の成長機会：農業タイプ別
  • 世界の農業分析市場の成長機会：農場規模別
  • 世界の農業分析市場の成長機会：技術別
  • 世界の農業分析市場の成長機会：最終用途別
  • 世界の農業分析市場の成長機会：地域別
• 世界の農業分析市場の新たな動向
• 戦略分析
  • 新製品開発
  • 世界の農業分析市場の能力拡大
  • 世界の農業分析市場における合併、買収、合弁事業
  • 認証とライセンシング

第7章 主要企業の企業プロファイル

• Deere & Company
• IBM
• Bayer
• SAP
• Trimble
• Accenture
• ABACO
• DeLaval
• Oracle
• DTN

The future of the global agriculture analytic market looks promising with opportunities in the farmer, agronomist, agribusiness, agricultural researcher, and government agency markets. The global agriculture analytic market is expected to reach an estimated $3.7 billion by 2031 with a CAGR of 12.7% from 2025 to 2031. The major drivers for this market are the growing need for optimal resource utilization, rising government initiatives for deploying improved agricultural techniques, and the rise in the adoption of IoT devices.

• Lucintel forecasts that, Within the agriculture type category, livestock farming is expected to witness the highest growth over the forecast period.
• Within the end use category, agronomist is expected to witness the highest growth.
• In terms of regions, North America will remain the largest region over the forecast period due to expanding the region's agricultural output and investing more in digital agriculture techniques.

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Emerging Trends in the Agriculture Analytic Market

The agriculture analytics market is witnessing several emerging trends that are reshaping how data is used in farming. These trends reflect advancements in technology and changing priorities within the agricultural sector, from enhancing precision to promoting sustainability. As technology continues to evolve, these trends drive significant transformations in how agricultural data is collected, analyzed, and applied, leading to more efficient and sustainable farming practices.

• AI and Machine Learning Integration: Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into agriculture analytics, offering advanced predictive capabilities and automated decision-making. AI algorithms analyze vast amounts of data from various sources, such as satellite imagery and sensors, to provide actionable insights. This trend helps farmers optimize crop management, predict yields, and detect issues like pest infestations early. The result is more precise and data-driven farming practices that significantly enhance productivity and reduce waste.
• Precision Agriculture Technologies: Precision agriculture technologies are transforming farming by enabling more accurate and efficient use of resources. Technologies such as GPS-guided tractors, variable rate application systems, and remote sensing tools allow farmers to apply inputs like water, fertilizers, and pesticides precisely where needed. This trend reduces the overuse of resources, minimizes environmental impact, and increases crop yields. By leveraging detailed data and advanced analytics, precision agriculture supports sustainable and efficient farming practices.
• IoT and Smart Farming Solutions: The Internet of Things (IoT) plays a crucial role in agriculture analytics by connecting various sensors and devices to collect real-time data from the field. Smart farming solutions utilize this data to monitor soil conditions, weather patterns, and crop health. IoT-enabled devices provide continuous feedback and automation, helping farmers make informed decisions and respond quickly to changing conditions. This trend enhances operational efficiency and supports precision agriculture practices.
• Blockchain for Traceability and Transparency: Blockchain technology is gaining traction in agriculture analytics for its ability to enhance traceability and transparency in the supply chain. By providing a secure and immutable record of transactions, blockchain helps track the origin and journey of agricultural products from farm to table. This trend improves food safety, reduces fraud, and builds consumer trust. It also supports better management of resources and adherence to quality standards.
• Sustainable and Regenerative Farming Analytics: There is a growing emphasis on sustainable and regenerative farming practices supported by advanced analytics. Tools that analyze soil health, biodiversity, and environmental impact are becoming essential for promoting sustainable agriculture. Analytics help farmers implement practices that improve soil fertility, reduce carbon footprints, and enhance ecosystem health. This trend aligns with global efforts to address climate change and promote long-term agricultural sustainability.

These emerging trends significantly reshape the agriculture analytics market by driving innovation and enhancing farming practices. AI, precision agriculture, IoT, blockchain, and sustainability-focused analytics transform how data is used to improve productivity, efficiency, and sustainability in agriculture. As these trends continue to evolve, they promise to address critical challenges and unlock new opportunities for the agricultural sector.

Recent Developments in the Agriculture Analytic Market

The agriculture analytics market reflects the sector's ongoing transformation driven by technological innovation and a focus on optimizing agricultural practices. These advancements make data-driven approaches more accessible and effective, addressing key challenges such as resource management, productivity, and sustainability. By integrating advanced technologies, the agriculture analytics market evolves rapidly, offering new solutions and insights to enhance farming practices across the globe.

• Advanced Sensor Technologies: Recent advancements in sensor technologies have revolutionized data collection in agriculture. Modern sensors, including those for soil moisture, nutrient levels, and weather conditions, provide real-time data that helps farmers make informed decisions. These sensors are increasingly integrated into precision farming systems, allowing for better management of resources and improved crop health monitoring. The enhanced accuracy and timeliness of data support more effective and targeted agricultural practices.
• Big Data and Analytics Platforms: The rise of big data and advanced analytics platforms transforms agriculture by providing deeper insights into farming operations. These platforms analyze large volumes of data from various sources, such as satellites and IoT devices, to offer comprehensive insights into crop performance, soil conditions, and market trends. This development enables farmers to optimize their operations, forecast yields, and make data-driven decisions that enhance productivity and efficiency.
• Autonomous Farming Equipment: Autonomous farming equipment, including drones and self-driving tractors, is becoming increasingly prevalent. These technologies automate tasks such as planting, harvesting, and monitoring crops, reducing the need for manual labor and increasing operational efficiency. Autonomous equipment leverages data from sensors and AI to perform tasks with high precision, leading to more consistent and reliable farming practices.
• Cloud-Based Data Management: Cloud-based data management solutions facilitate the storage, processing, and analysis of agricultural data. These platforms offer scalable and accessible solutions for managing vast amounts of data collected from various sources. Cloud-based systems enable farmers to access real-time insights and collaborate with other stakeholders more effectively. This development supports more efficient data management and enhances decision-making capabilities.
• Integration of Blockchain for Supply Chain Transparency: Blockchain technology is integrated into agriculture analytics to improve supply chain transparency and traceability. By providing a secure and immutable record of transactions, blockchain enhances the ability to track the origin and journey of agricultural products. This development promotes food safety, reduces fraud, and ensures compliance with quality standards, benefiting both producers and consumers.

Recent developments in agriculture analytics significantly impact the sector by introducing advanced technologies and data-driven solutions. The adoption of sensor technologies, big data platforms, autonomous equipment, cloud-based systems, and blockchain enhances productivity, efficiency, and transparency in agriculture. These developments reshape farming practices and address key challenges, paving the way for a more innovative and sustainable agricultural future.

Strategic Growth Opportunities for Agriculture Analytic Market

The agriculture analytics market witnesses significant growth driven by the increasing adoption of data-driven technologies across various applications. As agricultural practices evolve to meet global food demands and sustainability goals, several strategic growth opportunities emerge. These opportunities span different applications within agriculture analytics, offering the potential for innovation and expansion. By focusing on key applications, businesses, and stakeholders can leverage these opportunities to enhance productivity, efficiency, and sustainability in the agricultural sector.

• Precision Agriculture: Precision agriculture is a major growth opportunity within the agriculture analytics market, driven by advancements in GPS technology, sensors, and data analytics. This application allows for the precise management of crop inputs such as water, fertilizers, and pesticides, tailored to specific field conditions. By optimizing resource use and minimizing waste, precision agriculture enhances crop yields and reduces environmental impact. The growing demand for sustainable farming practices and increased efficiency in resource management fuels the expansion of precision agriculture solutions.
• Crop Management and Yield Prediction: Advanced analytics for crop management and yield prediction is a key growth area. By utilizing data from satellite imagery, weather forecasts, and historical crop performance, these tools provide accurate predictions of crop yields and health. This enables farmers to make informed decisions regarding planting, harvesting, and resource allocation. The ability to anticipate crop performance helps optimize agricultural planning and mitigate risks associated with yield variability, supporting overall farm productivity and profitability.
• Soil Health Monitoring: Soil health monitoring is gaining traction as a crucial application of agriculture analytics. Technologies that analyze soil properties, such as nutrient levels, moisture content, and pH, provide valuable insights for soil management. This application helps farmers maintain optimal soil conditions, improve crop growth, and sustain long-term soil fertility. As awareness of soil health's impact on agricultural sustainability grows, investment in soil monitoring solutions increases, presenting significant growth opportunities in this segment.
• Livestock Monitoring: Livestock monitoring is an emerging application of agriculture analytics that focuses on improving animal health and productivity. Solutions that use wearable sensors, GPS tracking, and data analytics provide real-time insights into animal behavior, health status, and productivity. This application enables farmers to detect issues early, optimize feeding practices, and enhance overall herd management. The growing emphasis on animal welfare and efficient livestock management drives the demand for advanced livestock monitoring technologies.
• Supply Chain Optimization: Supply chain optimization is a strategic growth opportunity within agriculture analytics, driven by the need for greater transparency and efficiency in food distribution. Data analytics platforms that track and analyze the movement of agricultural products from farm to table help streamline operations, reduce waste, and improve traceability. By enhancing supply chain management, these solutions support better decision-making and contribute to a more efficient and sustainable agricultural supply chain.

The strategic growth opportunities in agriculture analytics shape the future of the agricultural sector by enhancing precision, efficiency, and sustainability. Applications in precision agriculture, crop management, soil health monitoring, livestock monitoring, and supply chain optimization drive innovation and growth. As these opportunities continue to develop, they are expected to significantly impact agricultural practices, contributing to increased productivity and a more sustainable approach to farming.

Agriculture Analytic Market Driver and Challenges

The agriculture analytics market is influenced by various drivers and challenges that shape its development. Technological advancements, economic factors, and regulatory considerations play crucial roles in driving market growth and addressing obstacles. Understanding these drivers and challenges is essential for stakeholders to navigate the market effectively and leverage opportunities for growth while mitigating risks.

The factors responsible for driving the agriculture analytics market include:

• Technological Advancements: Rapid technological advancements in sensors, data analytics, and AI drive growth in agriculture analytics. Innovations such as advanced sensors for soil and crop monitoring, machine learning algorithms for predictive analytics, and drones for aerial imaging enhance data collection and analysis. These technologies enable precision farming, improve resource management, and optimize crop yields. As technology continues to evolve, it provides new capabilities and efficiencies that propel the agriculture analytics market forward.
• Increasing Demand for Food Security: The growing global population and increasing demand for food are significant drivers of the agriculture analytics market. To meet these demands, there is a need for enhanced agricultural productivity and efficiency. Agriculture analytics provides tools and insights that help optimize crop yields, manage resources effectively, and ensure food security. As the pressure to produce more food intensifies, the adoption of data-driven solutions becomes essential to achieving sustainable agricultural practices.
• Focus on Sustainability: Sustainability is a major driver of agriculture analytics, as there is a strong emphasis on reducing environmental impact and promoting eco-friendly farming practices. Analytics solutions that support precision agriculture, soil health monitoring, and resource optimization align with sustainability goals by minimizing waste and conserving resources. The increasing awareness of environmental issues and regulatory pressure to adopt sustainable practices drive the demand for analytics solutions that contribute to a greener agricultural sector.
• Government Support and Investment: Government initiatives and investments in agricultural technology and data analytics foster market growth. Many governments implement policies and provide funding to support the adoption of advanced technologies in agriculture. These investments help accelerate the development and deployment of agriculture analytics solutions, making them more accessible to farmers and enhancing the overall growth of the market.
• Growing Adoption of IoT and Big Data: The adoption of Internet of Things (IoT) devices and big data analytics drives innovation in agriculture. IoT sensors provide real-time data on various aspects of farming operations, while big data analytics platforms offer insights for decision-making. The integration of these technologies enables farmers to monitor and manage their operations more effectively, leading to increased efficiency and productivity. The widespread adoption of IoT and big data contributes significantly to the growth of agriculture analytics.

Challenges in the agriculture analytics market are:

• High Implementation Costs: One of the key challenges in the agriculture analytics market is the high cost of implementing advanced technologies. The initial investment required for sensors, data analytics platforms, and other technologies can be prohibitive for small and medium-sized farms. High implementation costs may limit the adoption of agriculture analytics solutions and create barriers to entry for some stakeholders, affecting overall market growth.
• Data Privacy and Security Concerns: Data privacy and security concerns are significant challenges in the agriculture analytics market. As agriculture analytics involves the collection and analysis of sensitive data, there is a risk of data breaches and misuse. Ensuring robust data protection measures and addressing privacy concerns are crucial for maintaining trust and encouraging the adoption of analytics solutions. The need for stringent security protocols presents an ongoing challenge for the market.
• Limited Access to Technology in Developing Regions: Limited access to advanced technology and infrastructure in developing regions poses a challenge for the agriculture analytics market. In regions with inadequate technological infrastructure, the adoption of data-driven solutions may be slow. Bridging the technology gap and providing affordable and accessible solutions are essential to expanding the reach of agriculture analytics and ensuring its benefits are realized globally.

The agriculture analytics market is significantly influenced by technological advancements, food security demands, sustainability goals, government support, and the adoption of IoT and big data. However, challenges such as high implementation costs, data privacy concerns, and limited technology access in developing regions also impact the market. Addressing these drivers and challenges is crucial for the continued growth and evolution of agriculture analytics, ensuring that the sector can meet its goals of increased productivity and sustainability.

List of Agriculture Analytic Companies

Companies in the market compete on the basis of product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. through these strategies agriculture analytic companies cater increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the agriculture analytic companies profiled in this report include-

• Deere & Company
• IBM
• Bayer
• SAP
• Trimble
• Accenture
• ABACO
• DeLaval
• Oracle
• DTN

Agriculture Analytic by Segment

The study includes a forecast for the global agriculture analytic by agriculture type, farm size, technology, end use, and region.

Agriculture Analytic Market by Agriculture Type [Analysis by Value from 2019 to 2031]:

• Precision Farming
• Livestock Farming
• Aquaculture Farming
• Vertical Farming
• Others

Agriculture Analytic Market by Farm Size [Analysis by Value from 2019 to 2031]:

• Large Farms
• Small & Medium-Sized Farms

Agriculture Analytic Market by Technology [Analysis by Value from 2019 to 2031]:

• Remote Sensing & Satellite Imagery
• Geographic Information System
• Robotics & Automation
• Big Data & Cloud Computing
• Visualization & Reporting
• Blockchain Technology
• Others

Agriculture Analytic Market by End Use [Analysis by Value from 2019 to 2031]:

• Farmers
• Agronomists
• Agribusinesses
• Agricultural Researchers
• Government Agencies
• Others

Agriculture Analytic Market by Region [Analysis by Value from 2019 to 2031]:

• North America
• Europe
• Asia Pacific
• The Rest of the World

Country Wise Outlook for the Agriculture Analytic Market

The agriculture analytics market is rapidly evolving, driven by technological advancements and a growing need for data-driven decision-making in farming. As global food demands rise and sustainability becomes paramount, countries are investing heavily in agricultural technology to optimize crop yields, improve resource management, and enhance overall productivity. Recent developments in the agriculture analytics market reflect this shift, with notable progress across the United States, China, Germany, India, and Japan. These advancements are shaping how data is used to address agricultural challenges and opportunities, transforming traditional farming practices into more efficient, technology-driven processes.

• United States: In the U.S., agriculture analytics is increasingly centered on precision farming and AI-driven insights. Major developments include the integration of satellite imagery and machine learning to optimize crop management. Companies like Trimble and John Deere lead innovations in precision agriculture, offering advanced tools for real-time data collection and analysis. These technologies help farmers increase yield, reduce waste, and manage resources more efficiently. The U.S. also sees significant investment in developing autonomous farming equipment, which aims to further enhance productivity and operational efficiency.
• China: China focuses on leveraging big data and IoT (Internet of Things) to revolutionize its agricultural sector. The government and private enterprises invest in smart farming solutions that include drone technology for crop monitoring and automated systems for precision irrigation. Companies such as DJI and Alibaba are at the forefront, using data analytics to improve crop forecasting and resource management. This push aligns with China's broader strategy to ensure food security and increase agricultural efficiency amid a growing population.
• Germany: Germany's advancements in agriculture analytics are driven by a strong emphasis on sustainability and precision farming. Innovations include the development of advanced sensor technologies and data analytics platforms that monitor soil health and optimize nutrient use. Companies like BASF and Bayer lead these initiatives, focusing on integrating digital solutions into traditional farming practices. Germany's approach highlights the importance of environmentally friendly farming techniques and the efficient use of resources to support sustainable agricultural practices.
• India: In India, agriculture analytics is rapidly evolving with a focus on improving smallholder farmer productivity. Developments include mobile applications and AI-powered platforms that provide real-time weather forecasts, pest detection, and crop recommendations. Startups such as AgroStar and CropIn are making significant strides in offering affordable, accessible analytics tools tailored to the needs of Indian farmers. These innovations aim to address challenges such as low productivity and resource constraints, helping farmers make informed decisions and improve their yields.
• Japan: Japan embraces advanced robotics and data analytics to address labor shortages and enhance agricultural efficiency. Developments include the use of automated systems for planting, harvesting, and monitoring crops. Companies like Kubota and Yamaha develop robotics solutions that integrate with data analytics to optimize farming operations. Additionally, Japan's focus on precision agriculture and smart farming technologies reflects its commitment to maximizing productivity while addressing challenges such as an aging farming population and limited arable land.

Features of the Global Agriculture Analytic Market

Market Size Estimates: Agriculture analytic market size estimation in terms of value ($B).

Trend and Forecast Analysis: Market trends (2019 to 2024) and forecast (2025 to 2031) by various segments and regions.

Segmentation Analysis: Agriculture analytic market size by agriculture type, farm size, technology, end use, and region in terms of value ($B).

Regional Analysis: Agriculture analytic market breakdown by North America, Europe, Asia Pacific, and Rest of the World.

Growth Opportunities: Analysis of growth opportunities in different agriculture type, farm size, technology, end use, and regions for the agriculture analytic market.

Strategic Analysis: This includes M&A, new product development, and competitive landscape of the agriculture analytic market.

Analysis of competitive intensity of the industry based on Porter's Five Forces model.

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This report answers following 11 key questions:

• Q.1. What are some of the most promising, high-growth opportunities for the agriculture analytic market by agriculture type (precision farming, livestock farming, aquaculture farming, vertical farming, and others), farm size (large farms and small & medium-sized farms), technology (remote sensing & satellite imagery, geographic information system, robotics & automation, big data & cloud computing, visualization & reporting, blockchain technology, and others), end use (farmers, agronomists, agribusinesses, agricultural researchers, government agencies, and others), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
• Q.2. Which segments will grow at a faster pace and why?
• Q.3. Which region will grow at a faster pace and why?
• Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
• Q.5. What are the business risks and competitive threats in this market?
• Q.6. What are the emerging trends in this market and the reasons behind them?
• Q.7. What are some of the changing demands of customers in the market?
• Q.8. What are the new developments in the market? Which companies are leading these developments?
• Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
• Q.10. What are some of the competing products in this market and how big of a threat do they pose for loss of market share by material or product substitution?
• Q.11. What M&A activity has occurred in the last 5 years and what has its impact been on the industry?

Table of Contents

1. Executive Summary

2. Global Agriculture Analytic Market : Market Dynamics

• 2.1: Introduction, Background, and Classifications
• 2.2: Supply Chain
• 2.3: Industry Drivers and Challenges

3. Market Trends and Forecast Analysis from 2019 to 2031

• 3.1. Macroeconomic Trends (2019-2024) and Forecast (2025-2031)
• 3.2. Global Agriculture Analytic Market Trends (2019-2024) and Forecast (2025-2031)
• 3.3: Global Agriculture Analytic Market by Agriculture Type
  • 3.3.1: Precision Farming
  • 3.3.2: Livestock Farming
  • 3.3.3: Aquaculture Farming
  • 3.3.4: Vertical Farming
  • 3.3.5: Others
• 3.4: Global Agriculture Analytic Market by Farm Size
  • 3.4.1: Large Farms
  • 3.4.2: Small & Medium-Sized Farms
• 3.5: Global Agriculture Analytic Market by Technology
  • 3.5.1: Remote Sensing & Satellite Imagery
  • 3.5.2: Geographic Information System
  • 3.5.3: Robotics & Automation
  • 3.5.4: Big Data & Cloud Computing
  • 3.5.5: Visualization & Reporting
  • 3.5.6: Blockchain Technology
  • 3.5.7: Others
• 3.6: Global Agriculture Analytic Market by End Use
  • 3.6.1: Farmers
  • 3.6.2: Agronomists
  • 3.6.3: Agribusinesses
  • 3.6.4: Agricultural Researchers
  • 3.6.5: Government Agencies
  • 3.6.6: Others

4. Market Trends and Forecast Analysis by Region from 2019 to 2031

• 4.1: Global Agriculture Analytic Market by Region
• 4.2: North American Agriculture Analytic Market
  • 4.2.1: North American Agriculture Analytic Market by Agriculture Type: Precision Farming, Livestock Farming, Aquaculture Farming, Vertical Farming, and Others
  • 4.2.2: North American Agriculture Analytic Market by End Use: Farmers, Agronomists, Agribusinesses, Agricultural Researchers, Government Agencies, and Others
• 4.3: European Agriculture Analytic Market
  • 4.3.1: European Agriculture Analytic Market by Agriculture Type: Precision Farming, Livestock Farming, Aquaculture Farming, Vertical Farming, and Others
  • 4.3.2: European Agriculture Analytic Market by End Use: Farmers, Agronomists, Agribusinesses, Agricultural Researchers, Government Agencies, and Others
• 4.4: APAC Agriculture Analytic Market
  • 4.4.1: APAC Agriculture Analytic Market by Agriculture Type: Precision Farming, Livestock Farming, Aquaculture Farming, Vertical Farming, and Others
  • 4.4.2: APAC Agriculture Analytic Market by End Use: Farmers, Agronomists, Agribusinesses, Agricultural Researchers, Government Agencies, and Others
• 4.5: ROW Agriculture Analytic Market
  • 4.5.1: ROW Agriculture Analytic Market by Agriculture Type: Precision Farming, Livestock Farming, Aquaculture Farming, Vertical Farming, and Others
  • 4.5.2: ROW Agriculture Analytic Market by End Use: Farmers, Agronomists, Agribusinesses, Agricultural Researchers, Government Agencies, and Others

5. Competitor Analysis

• 5.1: Product Portfolio Analysis
• 5.2: Operational Integration
• 5.3: Porter's Five Forces Analysis

6. Growth Opportunities and Strategic Analysis

• 6.1: Growth Opportunity Analysis
  • 6.1.1: Growth Opportunities for the Global Agriculture Analytic Market by Agriculture Type
  • 6.1.2: Growth Opportunities for the Global Agriculture Analytic Market by Farm Size
  • 6.1.3: Growth Opportunities for the Global Agriculture Analytic Market by Technology
  • 6.1.4: Growth Opportunities for the Global Agriculture Analytic Market by End Use
  • 6.1.5: Growth Opportunities for the Global Agriculture Analytic Market by Region
• 6.2: Emerging Trends in the Global Agriculture Analytic Market
• 6.3: Strategic Analysis
  • 6.3.1: New Product Development
  • 6.3.2: Capacity Expansion of the Global Agriculture Analytic Market
  • 6.3.3: Mergers, Acquisitions, and Joint Ventures in the Global Agriculture Analytic Market
  • 6.3.4: Certification and Licensing

7. Company Profiles of Leading Players

• 7.1: Deere & Company
• 7.2: IBM
• 7.3: Bayer
• 7.4: SAP
• 7.5: Trimble
• 7.6: Accenture
• 7.7: ABACO
• 7.8: DeLaval
• 7.9: Oracle
• 7.10: DTN