農業向けAI市場レポート：提供別、技術別、アプリケーション別、地域別、2025-2033年

農業向けAI市場の世界市場規模は2024年に22億米ドルに達しました。今後、IMARC Groupは、市場は2033年までに130億米ドルに達し、2025年から2033年の間に19.48%の成長率（CAGR）を示すと予測しています。同市場は、作物収量の向上、資源利用の最適化、環境への影響の緩和を目的とした精密農業技術へのニーズの高まり、データ主導型農業の実践を目的としたIoTデバイスやセンサーの採用拡大、膨大な農業データセットを分析するための高度なAIアルゴリズムや機械学習モデルへの需要に牽引され、着実な成長を遂げています。

農業向けAI市場分析：

市場の成長と規模：農作物の収穫量を向上させる精密農業のニーズの高まりにより、市場は安定した成長を遂げています。市場規模は今後数年以内に数十億米ドルに達すると予測されます。

技術の進歩：機械学習やコンピュータービジョンなどのAI技術は急速に進歩しています。また、データ収集のためのIoT、ドローン、リモートセンシングの統合も普及しています。

産業への応用：農業向けAIは、作物モニタリング、土壌分析、予測アナリティクス、自律型機械など、さまざまな分野で応用されています。また、家畜のモニタリング、病気の検出、スマート農業などにも利用されています。

地理的動向：アジア太平洋地域は、大規模な農業セクターと技術の進歩により、農業向けAI市場の有力なプレーヤーとなっています。北米も、特に精密農業技術の採用において大きな貢献をしています。

競合情勢：同市場は、農業向けAIソリューションを提供する既存企業と新興企業が混在しているのが特徴です。主要企業は競争力を維持するために研究開発に投資しています。

課題と機会：課題には、AI導入の初期コストの高さや、農家に対する教育と訓練の必要性などがあります。また、世界の食糧安全保障問題への対応や、AIを活用した持続可能な農業手法の開発には機会があります。

将来の展望：農業におけるAIの将来は有望であり、継続的な革新と導入が期待されます。AI技術は、環境への影響を最小限に抑えつつ、増加する世界人口に食糧を供給するという課題に取り組む上で極めて重要な役割を果たすと思われます。

農業向けAI市場動向：

効率性と生産性の向上

農業市場におけるAIの採用を促進する主な要因の1つは、AIがもたらす効率性と生産性の大幅な向上です。伝統的な農法では、推測や手作業が多く、時間がかかるうえにミスが発生しやすいです。機械学習アルゴリズムやデータ分析などのAI技術により、農家は植え付け、水やり、収穫について、より多くの情報に基づいた決定を下すことができます。例えば、AIを搭載したシステムは、土壌の状態、天候パターン、作物の健康状態を分析し、灌漑スケジュールを最適化し、水の無駄を減らすことができます。このような精密農業のアプローチは、作物収量の増加と資源管理の改善につながり、長期的には農業をより持続可能で収益性の高いものにします。

労働力不足とコスト削減

農業における労働力不足は世界の問題であり、多くの地域で農業活動のための安定した熟練労働力の確保が困難な状況に直面しています。AIと自動化は、手作業への依存を減らすことで、この課題に対する解決策を提供します。例えば、ロボット工学は、草取り、収穫、果実の摘み取りなど、労働集約的で人手不足になりがちな作業を行うことができます。さらに、AIシステムは広大な農地を監視・管理することができるため、現場での大規模な労働力の必要性を減らすことができます。この自動化は労働力不足に対処するだけでなく、コスト削減にも貢献します。定型的な作業を自動化することで、農家は運営コストを削減し、農場経営のより戦略的な側面にリソースを集中させることができます。

技術の進歩

AIと機械学習技術の急速な進歩は、農業向けAI市場の主要な促進要因です。これらの技術は大きく進化し、複雑な農業課題に対する洗練されたソリューションを提供しています。機械学習アルゴリズムは、天候パターン、作物の健康状態、土壌の状態など、さまざまなソースからの膨大なデータを処理し、農家に正確でリアルタイムの洞察を提供することができます。自律走行トラクターやドローンなどの農業機械にAIを統合することで、圃場作業が強化され、人件費の削減と効率化が実現します。このようなAI技術の絶え間ない進化により、AI技術はますます利用しやすくなり、農業への応用が可能になるため、市場の成長に拍車をかけています。

持続可能な農業へのニーズ

持続可能性は現代農業における大きな関心事であり、AIは持続可能な実践を促進する上で重要な役割を果たしています。AI主導のツールは、資源の利用を最適化することで、農業が環境に与える影響を監視・削減するのに役立ちます。例えば、AIシステムは、特定の作物エリアに必要な水や農薬の量に関する正確なデータを提供し、浪費や環境への害を最小限に抑えることができます。さらにAIは、長期的な持続可能性に不可欠な輪作や土壌の健全性管理を支援することができます。二酸化炭素排出量を削減し、環境に優しい慣行を促進することで、AIは単なる農業ツールではなく、気候変動と闘う手段でもあります。

政府の取り組み

世界各国の政府は、農業に革命をもたらすAIの可能性を認識しており、イニシアティブや資金提供を通じて実質的な支援を行っています。こうした取り組みには、AI農業技術への補助金、研究開発への助成金、農家への教育プログラムなどが含まれることが多いです。このような政府の支援は、市場の成長を刺激するだけでなく、農業におけるAIの革新と採用を促進します。財政的・政策的支援を提供することで、政府は農業向けAI市場を推進する上で重要な役割を果たし、農家や農業界全体にとってより身近で有益なものとします。

目次

第1章 序文

第2章 調査範囲と調査手法

• 調査の目的
• ステークホルダー
• データソース
  • 一次情報
  • 二次情報
• 市場推定
  • ボトムアップアプローチ
  • トップダウンアプローチ
• 調査手法

第3章 エグゼクティブサマリー

第4章 イントロダクション

• 概要
• 主要業界動向

第5章 世界の農業向けAI市場

• 市場概要
• 市場実績
• COVID-19の影響
• 市場予測

第6章 市場内訳：提供別

• ソフトウェア
  • 市場動向
  • 市場予測
• ハードウェア
  • 市場動向
  • 市場予測
• AIaaS
  • 市場動向
  • 市場予測
• サービス
  • 市場動向
  • 市場予測

第7章 市場内訳：技術別

• 機械学習
  • 市場動向
  • 市場予測
• コンピュータービジョン
  • 市場動向
  • 市場予測
• 予測アナリティクス
  • 市場動向
  • 市場予測

第8章 市場内訳：アプリケーション別

• 精密農業
  • 市場動向
  • 市場予測
• 農業ロボット
  • 市場動向
  • 市場予測
• 家畜モニタリング
  • 市場動向
  • 市場予測
• ドローンアナリティクス
  • 市場動向
  • 市場予測
• 労務管理
  • 市場動向
  • 市場予測
• その他
  • 市場動向
  • 市場予測

第9章 市場内訳：地域別

• 北米
  • 米国
  • カナダ
• アジア太平洋地域
  • 中国
  • 日本
  • インド
  • 韓国
  • オーストラリア
  • インドネシア
  • その他
• 欧州
  • ドイツ
  • フランス
  • 英国
  • イタリア
  • スペイン
  • ロシア
  • その他
• ラテンアメリカ
  • ブラジル
  • メキシコ
  • その他
• 中東・アフリカ

第10章 SWOT分析

• 概要
• 強み
• 弱み
• 機会
• 脅威

第11章 バリューチェーン分析

第12章 ポーターのファイブフォース分析

• 概要
• 買い手の交渉力
• 供給企業の交渉力
• 競合の程度
• 新規参入業者の脅威
• 代替品の脅威

第13章 価格分析

第14章 競合情勢

• 市場構造
• 主要企業
• 主要企業のプロファイル
  • A.A.A Taranis Visual Ltd.
  • Ag Leader Technology
  • AgEagle Aerial Systems Inc.
  • Bayer AG
  • Deere & Company
  • Gamaya
  • Granular Inc.（DuPont de Nemours, Inc.）
  • International Business Machines Corporation
  • Raven Industries Inc.
  • Trimble Inc.
  • Oracle Corporation
  • Wipro Limited

List of Figures

• Figure 1: Global: AI in Agriculture Market: Major Drivers and Challenges
• Figure 2: Global: AI in Agriculture Market: Sales Value (in Billion USD), 2019-2024
• Figure 3: Global: AI in Agriculture Market Forecast: Sales Value (in Billion USD), 2025-2033
• Figure 4: Global: AI in Agriculture Market: Breakup by Offering (in %), 2024
• Figure 5: Global: AI in Agriculture Market: Breakup by Technology (in %), 2024
• Figure 6: Global: AI in Agriculture Market: Breakup by Application (in %), 2024
• Figure 7: Global: AI in Agriculture Market: Breakup by Region (in %), 2024
• Figure 8: Global: AI in Agriculture (Software) Market: Sales Value (in Million USD), 2019 & 2024
• Figure 9: Global: AI in Agriculture (Software) Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 10: Global: AI in Agriculture (Hardware) Market: Sales Value (in Million USD), 2019 & 2024
• Figure 11: Global: AI in Agriculture (Hardware) Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 12: Global: AI in Agriculture (AI-as-a-Service) Market: Sales Value (in Million USD), 2019 & 2024
• Figure 13: Global: AI in Agriculture (AI-as-a-Service) Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 14: Global: AI in Agriculture (Services) Market: Sales Value (in Million USD), 2019 & 2024
• Figure 15: Global: AI in Agriculture (Services) Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 16: Global: AI in Agriculture (Machine Learning) Market: Sales Value (in Million USD), 2019 & 2024
• Figure 17: Global: AI in Agriculture (Machine Learning) Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 18: Global: AI in Agriculture (Computer Vision) Market: Sales Value (in Million USD), 2019 & 2024
• Figure 19: Global: AI in Agriculture (Computer Vision) Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 20: Global: AI in Agriculture (Predictive Analytics) Market: Sales Value (in Million USD), 2019 & 2024
• Figure 21: Global: AI in Agriculture (Predictive Analytics) Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 22: Global: AI in Agriculture (Precision Farming) Market: Sales Value (in Million USD), 2019 & 2024
• Figure 23: Global: AI in Agriculture (Precision Farming) Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 24: Global: AI in Agriculture (Agriculture Robots) Market: Sales Value (in Million USD), 2019 & 2024
• Figure 25: Global: AI in Agriculture (Agriculture Robots) Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 26: Global: AI in Agriculture (Livestock Monitoring) Market: Sales Value (in Million USD), 2019 & 2024
• Figure 27: Global: AI in Agriculture (Livestock Monitoring) Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 28: Global: AI in Agriculture (Drone Analytics) Market: Sales Value (in Million USD), 2019 & 2024
• Figure 29: Global: AI in Agriculture (Drone Analytics) Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 30: Global: AI in Agriculture (Labor Management) Market: Sales Value (in Million USD), 2019 & 2024
• Figure 31: Global: AI in Agriculture (Labor Management) Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 32: Global: AI in Agriculture (Other Applications) Market: Sales Value (in Million USD), 2019 & 2024
• Figure 33: Global: AI in Agriculture (Other Applications) Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 34: North America: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 35: North America: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 36: United States: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 37: United States: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 38: Canada: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 39: Canada: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 40: Asia-Pacific: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 41: Asia-Pacific: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 42: China: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 43: China: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 44: Japan: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 45: Japan: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 46: India: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 47: India: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 48: South Korea: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 49: South Korea: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 50: Australia: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 51: Australia: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 52: Indonesia: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 53: Indonesia: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 54: Others: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 55: Others: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 56: Europe: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 57: Europe: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 58: Germany: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 59: Germany: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 60: France: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 61: France: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 62: United Kingdom: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 63: United Kingdom: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 64: Italy: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 65: Italy: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 66: Spain: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 67: Spain: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 68: Russia: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 69: Russia: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 70: Others: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 71: Others: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 72: Latin America: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 73: Latin America: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 74: Brazil: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 75: Brazil: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 76: Mexico: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 77: Mexico: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 78: Others: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 79: Others: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 80: Middle East and Africa: AI in Agriculture Market: Sales Value (in Million USD), 2019 & 2024
• Figure 81: Middle East and Africa: AI in Agriculture Market: Breakup by Country (in %), 2024
• Figure 82: Middle East and Africa: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2025-2033
• Figure 83: Global: AI in Agriculture Industry: SWOT Analysis
• Figure 84: Global: AI in Agriculture Industry: Value Chain Analysis
• Figure 85: Global: AI in Agriculture Industry: Porter's Five Forces Analysis

List of Tables

• Table 1: Global: AI in Agriculture Market: Key Industry Highlights, 2024 & 2033
• Table 2: Global: AI in Agriculture Market Forecast: Breakup by Offering (in Million USD), 2025-2033
• Table 3: Global: AI in Agriculture Market Forecast: Breakup by Technology (in Million USD), 2025-2033
• Table 4: Global: AI in Agriculture Market Forecast: Breakup by Application (in Million USD), 2025-2033
• Table 5: Global: AI in Agriculture Market Forecast: Breakup by Region (in Million USD), 2025-2033
• Table 6: Global: AI in Agriculture Market: Competitive Structure
• Table 7: Global: AI in Agriculture Market: Key Players

The global AI in agriculture market size reached USD 2.2 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 13.0 Billion by 2033, exhibiting a growth rate (CAGR) of 19.48% during 2025-2033. The market is experiencing steady growth driven by the increasing need for precision agriculture techniques to enhance crop yield, optimize resource utilization, and mitigate environmental impact, the growing adoption of IoT devices and sensors for data-driven farming practices, and the demand for advanced AI algorithms and machine learning models to analyze vast agricultural datasets.

AI in Agriculture Market Analysis:

Market Growth and Size: The market is witnessing stable growth, driven by the increasing need for precision agriculture to enhance crop yield. The market size is projected to reach billions of dollars within the next few years.

Technological Advancements: AI technologies like machine learning and computer vision are advancing rapidly. Besides, integration of IoT, drones, and remote sensing for data collection is also gaining traction.

Industry Applications: AI in Agriculture is applied across various segments, including crop monitoring, soil analysis, predictive analytics, and autonomous machinery. It is also used for livestock monitoring, disease detection, and smart farming practices.

Geographical Trends: Asia Pacific is a prominent player in the AI in Agriculture market, driven by its large agricultural sector and technological advancements. North America is also a significant contributor, particularly in the adoption of precision agriculture technologies.

Competitive Landscape: The market is characterized by a mix of established players and startups offering AI solutions for agriculture. Leading companies are investing in research and development to stay competitive.

Challenges and Opportunities: Challenges include the high initial cost of AI implementation and the need for education and training for farmers. Besides, opportunities lie in addressing global food security issues and developing sustainable agriculture practices through AI.

Future Outlook: The future of AI in Agriculture looks promising, with continued innovation and adoption expected. AI technologies will play a pivotal role in addressing the challenges of feeding a growing global population while minimizing environmental impact.

AI in Agriculture Market Trends:

Increased efficiency and productivity

One of the primary factors driving the adoption of AI in the agriculture market is the significant increase in efficiency and productivity it offers. Traditional farming methods often involve a lot of guesswork and manual labor, which can be both time-consuming and prone to errors. AI technologies, such as machine learning algorithms and data analytics, enable farmers to make more informed decisions about planting, watering, and harvesting. For example, AI-powered systems can analyze soil conditions, weather patterns, and crop health, optimizing irrigation schedules and reducing water waste. This precision farming approach leads to higher crop yields and better resource management, making agriculture more sustainable and profitable in the long term.

Labor shortage and cost reduction

Labor shortages in agriculture are a global issue, with many regions facing difficulties in securing a stable and skilled workforce for farming activities. AI and automation offer a solution to this challenge by reducing the reliance on manual labor. Robotics, for instance, can perform tasks like weeding, harvesting, and even fruit picking, which are labor-intensive and often hard to staff. Additionally, AI systems can monitor and manage large areas of farmland, reducing the need for a large on-site workforce. This automation not only addresses the labor shortage but also contributes to cost reduction. By automating routine tasks, farmers can lower their operational costs and focus their resources on more strategic aspects of farm management.

Technological advancements

The rapid advancements in AI and machine learning technologies are key drivers in the AI in Agriculture market. These technologies have evolved significantly, offering sophisticated solutions for complex agricultural challenges. Machine learning algorithms can process vast amounts of data from various sources, including weather patterns, crop health, and soil conditions, to provide precise and real-time insights for farmers. The integration of AI in agricultural machinery, like autonomous tractors and drones, enhances field operations, reducing labor costs and increasing efficiency. This continuous evolution of AI technologies makes them increasingly accessible and applicable in agriculture, thus fueling the market growth.

Need for sustainable agriculture practices

Sustainability is a major concern in modern agriculture, and AI plays a crucial role in promoting sustainable practices. AI-driven tools help in monitoring and reducing the environmental impact of farming by optimizing the use of resources. For instance, AI systems can provide precise data on the amount of water or pesticides needed for specific crop areas, minimizing wastage and environmental harm. Additionally, AI can assist in crop rotation and soil health management, which are essential for long-term sustainability. By reducing the carbon footprint and promoting eco-friendly practices, AI is not just an agricultural tool but also a means to combat climate change.

Government initiatives

Governments worldwide are recognizing the potential of AI in revolutionizing agriculture and are thus providing substantial support through initiatives and funding. These initiatives often include subsidies for AI agricultural technology, research and development grants, and educational programs for farmers. This government backing not only stimulates market growth but also encourages innovation and adoption of AI in agriculture. By providing financial and policy support, governments play a significant role in driving the AI in Agriculture market, making it more accessible and beneficial for farmers and the agricultural industry as a whole.

AI in Agriculture Industry Segmentation:

Breakup by Offering:

Software

Hardware

AI-as-a-Service

Services

Software accounts for the majority of the market share

The software segment holds the largest share in the AI in Agriculture market, driven by the increasing adoption of AI-powered farm management software, data analytics platforms, and AI-driven decision support systems by farmers and agricultural enterprises. These software solutions offer features such as crop monitoring, yield prediction, pest detection, and irrigation management, enabling precise and data-driven farming practices. With the integration of AI algorithms, farmers can make informed decisions, optimize resource utilization, and enhance crop yields while minimizing environmental impact. This segment is expected to witness sustained growth as technology providers continue to develop advanced software solutions tailored to the evolving needs of the agriculture sector.

The hardware segment encompasses AI-related physical devices and components used in agricultural applications, including sensors, drones, autonomous machinery, and AI-specific hardware accelerators. These technologies are essential for data collection, monitoring, and autonomous operations on the farm. The adoption of precision agriculture techniques has driven the demand for hardware solutions, allowing farmers to gather real-time data and automate various farming tasks. As technological advancements continue to enhance hardware capabilities, the hardware segment is poised for significant growth in the AI in agriculture market.

AI-as-a-Service is a segment that offers AI capabilities through cloud-based platforms and services to agricultural stakeholders. It enables farmers and organizations to access AI tools, machine learning models, and data analytics on a subscription or pay-per-use basis. This approach lowers the entry barriers for adopting AI in agriculture, particularly for smaller farms and businesses with limited IT infrastructure. AI-as-a-Service provides scalability and flexibility, allowing users to harness the power of AI without significant upfront investments in software or hardware. This segment is witnessing increased adoption due to its convenience and cost-effective nature.

The services segment includes consulting, implementation, training, and support services provided by AI solution providers and agricultural technology companies. Services play a crucial role in assisting farmers and organizations in the successful adoption and utilization of AI technologies in agriculture. Consulting services help assess specific needs, while implementation and training services ensure a smooth transition to AI-driven practices. Ongoing support services are essential for troubleshooting and optimizing AI systems. As the AI in Agriculture market matures, the demand for specialized services is expected to grow, providing added value to customers seeking to maximize the benefits of AI technologies in farming operations.

Breakup by Technology:

Machine Learning

Computer Vision

Predictive Analytics

Machine learning holds the largest share in the industry

Machine learning stands out as the largest segment. Machine learning algorithms are widely used for data analysis, pattern recognition, and decision-making in agriculture. They enable farmers to harness the power of data from various sources, including sensors, satellites, and drones, to make informed decisions about planting, irrigation, pest control, and crop health. Machine learning models help optimize resource allocation, predict crop yields, and even identify early signs of diseases or nutrient deficiencies in crops. The scalability and versatility of machine learning make it a fundamental technology driving precision agriculture and sustainable farming practices.

Computer vision is another crucial segment within the market. It involves the use of AI algorithms to interpret visual data from cameras and sensors. In agriculture, computer vision is employed for tasks like crop monitoring, weed detection, and fruit quality assessment. By analyzing images and videos, computer vision systems can identify anomalies in crops, enabling timely interventions. It also plays a significant role in autonomous farming by guiding robotic equipment and ensuring accurate plant and soil analysis. As technology continues to advance, computer vision applications in agriculture are becoming more sophisticated and accurate.

Predictive analytics is a vital component of AI in agriculture market. It involves the use of historical and real-time data to forecast future trends and outcomes in agriculture. Predictive models assist farmers in making proactive decisions related to weather, pest control, and crop management. By analyzing data on weather patterns, soil conditions, and past crop performance, predictive analytics can help optimize planting schedules and predict potential crop diseases or yield fluctuations. This segment contributes to risk mitigation and efficient resource allocation in agriculture, making it an essential tool for farmers seeking to maximize their yields and profitability.

Breakup by Application:

Precision Farming

Agriculture Robots

Livestock Monitoring

Drone Analytics

Labor Management

Others

Precision farming represents the leading market segment

Precision farming, also known as precision agriculture, dominates the market segment with its focus on optimizing crop management practices. This segment involves the use of AI-driven technologies for precise monitoring of soil conditions, weather, and crop health. It enables farmers to make data-driven decisions about irrigation, fertilization, and pest control, ultimately leading to increased crop yield and resource efficiency. Precision farming also contributes to sustainability by reducing resource wastage and environmental impact, making it the largest and most influential segment in the AI in Agriculture market.

Agriculture robots play a vital role in automating various farming tasks, from planting and harvesting to weeding and monitoring. These robots leverage AI and machine learning to enhance efficiency and reduce labor requirements. They are particularly valuable in addressing labor shortages and increasing productivity in the agriculture sector. Agriculture robots continue to evolve, offering new opportunities for precision and automation in farming practices.

The livestock monitoring segment focuses on using AI to enhance the management and well-being of farm animals. It involves the use of sensors, wearables, and data analytics to monitor the health, behavior, and productivity of livestock. This segment ensures that animals receive proper care, leading to improved livestock yield, disease prevention, and overall animal welfare. It is an essential component of modern livestock farming practices.

Drone analytics in agriculture involves the use of unmanned aerial vehicles (UAVs) equipped with sensors and cameras to collect data on crop health, soil conditions, and pest infestations. AI-powered analytics then process this data to provide valuable insights to farmers. This segment offers a bird's-eye view of farms, enabling quick and efficient decision-making. It is particularly beneficial for large-scale farms and those with challenging terrain.

The labor management segment focuses on optimizing workforce management on farms using AI-driven solutions. It helps farmers allocate labor resources efficiently, plan tasks, and improve overall labor productivity. By automating routine administrative tasks and providing insights into labor utilization, this segment ensures that farm labor is utilized effectively and that the workforce is better managed, ultimately contributing to cost savings and improved farm operations.

Breakup by Region:

North America

United States

Canada

Asia-Pacific

China

Japan

India

South Korea

Australia

Indonesia

Others

Europe

Germany

France

United Kingdom

Italy

Spain

Russia

Others

Latin America

Brazil

Mexico

Others

Middle East and Africa

North America leads the market, accounting for the largest AI in agriculture market share

The market research report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, North America accounted for the largest market share.

North America dominates the market, driven by advanced technological infrastructure, a strong focus on precision farming, and extensive investments in AI adoption. The region benefits from the presence of leading AI solution providers, and the agriculture sector's willingness to embrace innovative technologies like AI for crop monitoring, soil analysis, and autonomous machinery. Favorable government policies and incentives also contribute to the market's growth in North America.

In Europe, the market is experiencing steady growth, with countries like Germany, France, and the Netherlands leading in adoption. The region places emphasis on sustainable farming practices and environmental conservation, making AI a valuable tool for optimizing resource utilization and minimizing ecological impact. European farmers are increasingly using AI for crop management, livestock monitoring, and predictive analytics to enhance overall agricultural productivity.

The Asia Pacific region is witnessing remarkable growth in the AI in agriculture market, driven by its vast agricultural sector, population growth, and increasing demand for food security. Countries like China and India are adopting AI-driven technologies for precision agriculture, crop monitoring, and smart farming practices. The region's agricultural innovation, combined with government support, positions Asia Pacific as a significant player in the global AI in Agriculture market.

Latin America is emerging as a promising market for AI in agriculture, with countries like Brazil and Argentina leading the adoption curve. The region's rich agricultural resources and growing awareness of the benefits of AI are driving its expansion. Latin American farmers are utilizing AI for crop optimization, disease detection, and yield prediction, aiming to increase productivity and ensure food sustainability.

The Middle East and Africa region are gradually embracing AI in agriculture, with a focus on addressing water scarcity and extreme climatic conditions. AI solutions for precision irrigation and crop management are gaining traction in arid regions. Additionally, governments and international organizations are investing in AI-powered projects to improve agricultural practices and ensure food security in this region.

Leading Key Players in the AI in Agriculture Industry:

Key players in the market are actively engaged in several strategic initiatives to maintain their competitive edge. These initiatives include significant investments in research and development to enhance AI algorithms and machine learning models tailored for agriculture. Companies are also focusing on partnerships and collaborations with agricultural technology firms, research institutions, and government agencies to expand their market reach and access to valuable data sources. Moreover, they are launching new AI-powered solutions and platforms that offer farmers advanced tools for crop monitoring, predictive analytics, and autonomous machinery. Additionally, key players are providing training and support services to educate farmers on the effective use of AI technologies in agriculture, fostering broader adoption.

The market research report has provided a comprehensive analysis of the competitive landscape. Detailed profiles of all major companies have also been provided. Some of the key players in the market include

A.A.A Taranis Visual Ltd.

Ag Leader Technology

AgEagle Aerial Systems Inc.

Bayer AG

Deere & Company

Gamaya

Granular Inc. (DuPont de Nemours, Inc.)

International Business Machines Corporation

Raven Industries Inc.

Trimble Inc.

Oracle Corporation

Wipro Limited

Key Questions Answered in This Report

• 1. What was the size of the global AI in agriculture market in 2024?
• 2. What is the expected growth rate of the global AI in agriculture market during 2025-2033?
• 3. What are the key factors driving the global AI in agriculture market?
• 4. What has been the impact of COVID-19 on the global AI in agriculture market?
• 5. What is the breakup of the global AI in agriculture market based on the offering?
• 6. What is the breakup of the global AI in agriculture market based on the technology?
• 7. What is the breakup of the global AI in agriculture market based on the application?
• 8. What are the key regions in the global AI in agriculture market?
• 9. Who are the key players/companies in the global AI in agriculture market?

Table of Contents

1 Preface

2 Scope and Methodology

• 2.1 Objectives of the Study
• 2.2 Stakeholders
• 2.3 Data Sources
  • 2.3.1 Primary Sources
  • 2.3.2 Secondary Sources
• 2.4 Market Estimation
  • 2.4.1 Bottom-Up Approach
  • 2.4.2 Top-Down Approach
• 2.5 Forecasting Methodology

3 Executive Summary

4 Introduction

• 4.1 Overview
• 4.2 Key Industry Trends

5 Global AI in Agriculture Market

• 5.1 Market Overview
• 5.2 Market Performance
• 5.3 Impact of COVID-19
• 5.4 Market Forecast

6 Market Breakup by Offering

• 6.1 Software
  • 6.1.1 Market Trends
  • 6.1.2 Market Forecast
• 6.2 Hardware
  • 6.2.1 Market Trends
  • 6.2.2 Market Forecast
• 6.3 AI-as-a-Service
  • 6.3.1 Market Trends
  • 6.3.2 Market Forecast
• 6.4 Services
  • 6.4.1 Market Trends
  • 6.4.2 Market Forecast

7 Market Breakup by Technology

• 7.1 Machine Learning
  • 7.1.1 Market Trends
  • 7.1.2 Market Forecast
• 7.2 Computer Vision
  • 7.2.1 Market Trends
  • 7.2.2 Market Forecast
• 7.3 Predictive Analytics
  • 7.3.1 Market Trends
  • 7.3.2 Market Forecast

8 Market Breakup by Application

• 8.1 Precision Farming
  • 8.1.1 Market Trends
  • 8.1.2 Market Forecast
• 8.2 Agriculture Robots
  • 8.2.1 Market Trends
  • 8.2.2 Market Forecast
• 8.3 Livestock Monitoring
  • 8.3.1 Market Trends
  • 8.3.2 Market Forecast
• 8.4 Drone Analytics
  • 8.4.1 Market Trends
  • 8.4.2 Market Forecast
• 8.5 Labor Management
  • 8.5.1 Market Trends
  • 8.5.2 Market Forecast
• 8.6 Others
  • 8.6.1 Market Trends
  • 8.6.2 Market Forecast

9 Market Breakup by Region

• 9.1 North America
  • 9.1.1 United States
    • 9.1.1.1 Market Trends
    • 9.1.1.2 Market Forecast
  • 9.1.2 Canada
    • 9.1.2.1 Market Trends
    • 9.1.2.2 Market Forecast
• 9.2 Asia-Pacific
  • 9.2.1 China
    • 9.2.1.1 Market Trends
    • 9.2.1.2 Market Forecast
  • 9.2.2 Japan
    • 9.2.2.1 Market Trends
    • 9.2.2.2 Market Forecast
  • 9.2.3 India
    • 9.2.3.1 Market Trends
    • 9.2.3.2 Market Forecast
  • 9.2.4 South Korea
    • 9.2.4.1 Market Trends
    • 9.2.4.2 Market Forecast
  • 9.2.5 Australia
    • 9.2.5.1 Market Trends
    • 9.2.5.2 Market Forecast
  • 9.2.6 Indonesia
    • 9.2.6.1 Market Trends
    • 9.2.6.2 Market Forecast
  • 9.2.7 Others
    • 9.2.7.1 Market Trends
    • 9.2.7.2 Market Forecast
• 9.3 Europe
  • 9.3.1 Germany
    • 9.3.1.1 Market Trends
    • 9.3.1.2 Market Forecast
  • 9.3.2 France
    • 9.3.2.1 Market Trends
    • 9.3.2.2 Market Forecast
  • 9.3.3 United Kingdom
    • 9.3.3.1 Market Trends
    • 9.3.3.2 Market Forecast
  • 9.3.4 Italy
    • 9.3.4.1 Market Trends
    • 9.3.4.2 Market Forecast
  • 9.3.5 Spain
    • 9.3.5.1 Market Trends
    • 9.3.5.2 Market Forecast
  • 9.3.6 Russia
    • 9.3.6.1 Market Trends
    • 9.3.6.2 Market Forecast
  • 9.3.7 Others
    • 9.3.7.1 Market Trends
    • 9.3.7.2 Market Forecast
• 9.4 Latin America
  • 9.4.1 Brazil
    • 9.4.1.1 Market Trends
    • 9.4.1.2 Market Forecast
  • 9.4.2 Mexico
    • 9.4.2.1 Market Trends
    • 9.4.2.2 Market Forecast
  • 9.4.3 Others
    • 9.4.3.1 Market Trends
    • 9.4.3.2 Market Forecast
• 9.5 Middle East and Africa
  • 9.5.1 Market Trends
  • 9.5.2 Market Breakup by Country
  • 9.5.3 Market Forecast

10 SWOT Analysis

• 10.1 Overview
• 10.2 Strengths
• 10.3 Weaknesses
• 10.4 Opportunities
• 10.5 Threats

11 Value Chain Analysis

12 Porters Five Forces Analysis

• 12.1 Overview
• 12.2 Bargaining Power of Buyers
• 12.3 Bargaining Power of Suppliers
• 12.4 Degree of Competition
• 12.5 Threat of New Entrants
• 12.6 Threat of Substitutes

13 Price Analysis

14 Competitive Landscape

• 14.1 Market Structure
• 14.2 Key Players
• 14.3 Profiles of Key Players
  • 14.3.1 A.A.A Taranis Visual Ltd.
    • 14.3.1.1 Company Overview
    • 14.3.1.2 Product Portfolio
  • 14.3.2 Ag Leader Technology
    • 14.3.2.1 Company Overview
    • 14.3.2.2 Product Portfolio
  • 14.3.3 AgEagle Aerial Systems Inc.
    • 14.3.3.1 Company Overview
    • 14.3.3.2 Product Portfolio
    • 14.3.3.3 Financials
  • 14.3.4 Bayer AG
    • 14.3.4.1 Company Overview
    • 14.3.4.2 Product Portfolio
    • 14.3.4.3 Financials
    • 14.3.4.4 SWOT Analysis
  • 14.3.5 Deere & Company
    • 14.3.5.1 Company Overview
    • 14.3.5.2 Product Portfolio
    • 14.3.5.3 Financials
    • 14.3.5.4 SWOT Analysis
  • 14.3.6 Gamaya
    • 14.3.6.1 Company Overview
    • 14.3.6.2 Product Portfolio
  • 14.3.7 Granular Inc. (DuPont de Nemours, Inc.)
    • 14.3.7.1 Company Overview
    • 14.3.7.2 Product Portfolio
  • 14.3.8 International Business Machines Corporation
    • 14.3.8.1 Company Overview
    • 14.3.8.2 Product Portfolio
    • 14.3.8.3 Financials
    • 14.3.8.4 SWOT Analysis
  • 14.3.9 Raven Industries Inc.
    • 14.3.9.1 Company Overview
    • 14.3.9.2 Product Portfolio
  • 14.3.10 Trimble Inc.
    • 14.3.10.1 Company Overview
    • 14.3.10.2 Product Portfolio
    • 14.3.10.3 Financials
    • 14.3.10.4 SWOT Analysis
  • 14.3.11 Oracle Corporation
    • 14.3.11.1 Company Overview
    • 14.3.11.2 Product Portfolio
    • 14.3.11.3 Financials
    • 14.3.11.4 SWOT Analysis
  • 14.3.12 Wipro Limited
    • 14.3.12.1 Company Overview
    • 14.3.12.2 Product Portfolio
    • 14.3.12.3 Financials

Kindly, note that this only represents a partial list of companies, and the complete list has been provided in the report