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人工智能精密农业

市场调查报告书

商品编码

1451727

2024-2032 年按产品、技术、应用和地区分類的农业市场人工智慧报告

AI in Agriculture Market Report by Offering, Technology, Application, and Region 2024-2032

出版日期: 2024年03月02日 | 出版商:

IMARC | 英文 139 Pages | 商品交期: 2-3个工作天内

价格

简介目录图表

2023年全球农业人工智慧市场规模达18亿美元。展望未来， IMARC Group预计到2032年市场规模将达到101亿美元，2024-2032年复合年增长率（CAGR）为20.51%。。由于对精准农业技术的需求不断增长，以提高作物产量、优化资源利用和减轻环境影响，越来越多地采用物联网设备和感测器进行数据驱动的农业实践，以及对先进人工智慧的需求，推动市场稳定成长用于分析大量农业数据集的演算法和机器学习模型。

人工智慧在农业市场分析：

市场成长与规模：在精准农业提高作物产量的需求不断增长的推动下，市场正在稳定成长。预计未来几年市场规模将达数十亿美元。

技术进步：机器学习和电脑视觉等人工智慧技术正在迅速发展。此外，物联网、无人机和遥感资料收集的整合也越来越受到关注。

产业应用：农业中的人工智慧应用于各个领域，包括作物监测、土壤分析、预测分析和自主机械。它也用于牲畜监测、疾病检测和智慧农业实践。

地理趋势：受其庞大的农业部门和技术进步的推动，亚太地区是农业人工智慧市场的重要参与者。北美也是一个重要的贡献者，特别是在精准农业技术的采用方面。

竞争格局：该市场的特点是既有为农业提供人工智慧解决方案的老牌企业，也有新创企业。领先的公司正在投资研发以保持竞争力。

挑战与机会：挑战包括人工智慧实施的高昂初始成本以及对农民的教育和培训的需求。此外，机会也正在透过人工智慧解决全球粮食安全问题和发展永续农业实践。

未来展望：农业人工智慧的未来看起来充满希望，预计将持续创新和采用。人工智慧技术将在解决养活不断增长的全球人口同时最大限度地减少环境影响的挑战方面发挥关键作用。

人工智慧在农业市场趋势：

提高效率和生产力

推动人工智慧在农业市场采用的主要因素之一是其效率和生产力的显着提高。传统的耕作方法通常涉及大量的猜测和体力劳动，既耗时又容易出错。机器学习演算法和资料分析等人工智慧技术使农民能够在种植、浇水和收割方面做出更明智的决策。例如，人工智慧驱动的系统可以分析土壤状况、天气模式和作物健康，优化灌溉计划并减少水资源浪费。这种精准农业方法可以提高作物产量和更好的资源管理，使农业从长远来看更具可持续性和盈利能力。

劳动短缺和成本降低

农业劳动力短缺是一个全球性问题，许多地区在确保农业活动稳定和熟练的劳动力方面面临困难。人工智慧和自动化透过减少对体力劳动的依赖，为这项挑战提供了解决方案。例如，机器人可以执行除草、收割甚至水果采摘等任务，这些任务是劳力密集的，而且往往很难配备人员。此外，人工智慧系统可以监控和管理大面积农田，减少对大量现场劳动力的需求。这种自动化不仅解决了劳动力短缺的问题，还有助于降低成本。透过自动化日常任务，农民可以降低营运成本，并将资源集中在农场管理的更具策略性的方面。

技术进步

人工智慧和机器学习技术的快速进步是农业市场人工智慧的关键驱动力。这些技术已经取得了显着的发展，为复杂的农业挑战提供了复杂的解决方案。机器学习演算法可以处理来自各种来源的大量资料，包括天气模式、作物健康和土壤状况，为农民提供精确、即时的见解。将人工智慧整合到自动拖拉机和无人机等农业机械中，可以增强田间作业，降低劳动力成本并提高效率。人工智慧技术的不断发展使它们在农业中越来越容易获得和应用，从而推动了市场的成长。

需要永续农业实践

永续性是现代农业的一个主要问题，人工智慧在促进永续实践方面发挥着至关重要的作用。人工智慧驱动的工具透过优化资源的使用来帮助监测和减少农业对环境的影响。例如，人工智慧系统可以提供特定作物区域所需的水或农药用量的精确资料，从而最大限度地减少浪费和环境危害。此外，人工智慧还可以协助作物轮作和土壤健康管理，这对于长期永续发展至关重要。透过减少碳足迹和促进生态友善的做法，人工智慧不仅是一种农业工具，也是应对气候变迁的一种手段。

政府倡议

世界各国政府都认识到人工智慧在农业革命中的潜力，因此透过措施和资金提供大力支持。这些措施通常包括人工智慧农业技术的补贴、研发补助以及农民教育计画。政府的支持不仅刺激了市场成长，也鼓励农业中人工智慧的创新和采用。透过提供财政和政策支持，政府在推动农业市场人工智慧方面发挥着重要作用，使农民和整个农业产业更容易获得併受益。

北美洲
- 美国
  - 市场走向
  - 市场预测
- 加拿大
  - 市场走向
  - 市场预测
亚太
- 中国
  - 市场走向
  - 市场预测
- 日本
  - 市场走向
  - 市场预测
- 印度
  - 市场走向
  - 市场预测
- 韩国
  - 市场走向
  - 市场预测
- 澳洲
  - 市场走向
  - 市场预测
- 印尼
  - 市场走向
  - 市场预测
- 其他的
  - 市场走向
  - 市场预测
欧洲
- 德国
  - 市场走向
  - 市场预测
- 法国
  - 市场走向
  - 市场预测
- 英国
  - 市场走向
  - 市场预测
- 义大利
  - 市场走向
  - 市场预测
- 西班牙
  - 市场走向
  - 市场预测
- 俄罗斯
  - 市场走向
  - 市场预测
- 其他的
  - 市场走向
  - 市场预测
拉丁美洲
- 巴西
  - 市场走向
  - 市场预测
- 墨西哥
  - 市场走向
  - 市场预测
- 其他的
  - 市场走向
  - 市场预测
中东和非洲
- 市场走向
- 市场细分：按国家/地区
- 市场预测

第 10 章：SWOT 分析

概述
优势
弱点
机会
威胁

第 11 章：价值链分析

第 12 章：波特五力分析

概述
买家的议价能力
供应商的议价能力
竞争程度
新进入者的威胁
替代品的威胁

第 13 章：价格分析

第14章：竞争格局

市场结构
关键参与者
关键参与者简介
- AA 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

简介目录图表

Product Code: SR112024A6604

The global AI in agriculture market size reached US$ 1.8 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 10.1 Billion by 2032, exhibiting a growth rate (CAGR) of 20.51% during 2024-2032. . 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:

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the global, regional, and country levels for 2024-2032. Our report has categorized the market based on offering, technology, and application.

Breakup by Offering:

Software

Hardware

AI-as-a-Service

Services

Software accounts for the majority of the market share

The report has provided a detailed breakup and analysis of the market based on the offering. This includes software, hardware, AI-as-a-service, services. According to the report, software represented the largest segment.

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

A detailed breakup and analysis of the market based on technology has also been provided in the report. This includes machine learning, computer vision, and predictive analytics. According to the report, machine learning accounted for the largest market share.

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

A detailed breakup and analysis of the market based on application has also been provided in the report. This includes precision farming, agriculture robots, livestock monitoring, drone analytics, labor management, and others. According to the report, precision farming accounted for the largest market share.

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

(Please note that this is only a partial list of the key players, and the complete list is provided in the report.)

Latest News:

April, 2022: Deere & Company has formed a joint venture with GUSS Automation, LLC, in Kingsburg, California which is a pioneer in semi-autonomous orchard and vineyard sprayers. This joint venture allows Deere & Company to expand its portfolio of autonomous and semi-autonomous machinery, addressing the specific needs of orchard and vineyard operators. By combining their strengths, the companies aim to enhance productivity, reduce labor costs, and promote sustainable farming practices in specialized agricultural sectors. This partnership exemplifies the growing trend of agricultural equipment manufacturers collaborating with technology firms to bring cutting-edge solutions to farmers and growers.

Key Questions Answered in This Report

1. What was the size of the global AI in agriculture market in 2023?
2. What is the expected growth rate of the global AI in agriculture market during 2024-2032?
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?

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

简介目录图表

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 US$), 2018-2023
Figure 3: Global: AI in Agriculture Market Forecast: Sales Value (in Billion US$), 2024-2032
Figure 4: Global: AI in Agriculture Market: Breakup by Offering (in %), 2023
Figure 5: Global: AI in Agriculture Market: Breakup by Technology (in %), 2023
Figure 6: Global: AI in Agriculture Market: Breakup by Application (in %), 2023
Figure 7: Global: AI in Agriculture Market: Breakup by Region (in %), 2023
Figure 8: Global: AI in Agriculture (Software) Market: Sales Value (in Million US$), 2018 & 2023
Figure 9: Global: AI in Agriculture (Software) Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 10: Global: AI in Agriculture (Hardware) Market: Sales Value (in Million US$), 2018 & 2023
Figure 11: Global: AI in Agriculture (Hardware) Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 12: Global: AI in Agriculture (AI-as-a-Service) Market: Sales Value (in Million US$), 2018 & 2023
Figure 13: Global: AI in Agriculture (AI-as-a-Service) Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 14: Global: AI in Agriculture (Services) Market: Sales Value (in Million US$), 2018 & 2023
Figure 15: Global: AI in Agriculture (Services) Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 16: Global: AI in Agriculture (Machine Learning) Market: Sales Value (in Million US$), 2018 & 2023
Figure 17: Global: AI in Agriculture (Machine Learning) Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 18: Global: AI in Agriculture (Computer Vision) Market: Sales Value (in Million US$), 2018 & 2023
Figure 19: Global: AI in Agriculture (Computer Vision) Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 20: Global: AI in Agriculture (Predictive Analytics) Market: Sales Value (in Million US$), 2018 & 2023
Figure 21: Global: AI in Agriculture (Predictive Analytics) Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 22: Global: AI in Agriculture (Precision Farming) Market: Sales Value (in Million US$), 2018 & 2023
Figure 23: Global: AI in Agriculture (Precision Farming) Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 24: Global: AI in Agriculture (Agriculture Robots) Market: Sales Value (in Million US$), 2018 & 2023
Figure 25: Global: AI in Agriculture (Agriculture Robots) Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 26: Global: AI in Agriculture (Livestock Monitoring) Market: Sales Value (in Million US$), 2018 & 2023
Figure 27: Global: AI in Agriculture (Livestock Monitoring) Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 28: Global: AI in Agriculture (Drone Analytics) Market: Sales Value (in Million US$), 2018 & 2023
Figure 29: Global: AI in Agriculture (Drone Analytics) Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 30: Global: AI in Agriculture (Labor Management) Market: Sales Value (in Million US$), 2018 & 2023
Figure 31: Global: AI in Agriculture (Labor Management) Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 32: Global: AI in Agriculture (Other Applications) Market: Sales Value (in Million US$), 2018 & 2023
Figure 33: Global: AI in Agriculture (Other Applications) Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 34: North America: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 35: North America: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 36: United States: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 37: United States: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 38: Canada: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 39: Canada: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 40: Asia-Pacific: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 41: Asia-Pacific: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 42: China: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 43: China: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 44: Japan: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 45: Japan: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 46: India: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 47: India: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 48: South Korea: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 49: South Korea: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 50: Australia: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 51: Australia: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 52: Indonesia: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 53: Indonesia: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 54: Others: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 55: Others: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 56: Europe: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 57: Europe: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 58: Germany: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 59: Germany: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 60: France: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 61: France: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 62: United Kingdom: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 63: United Kingdom: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 64: Italy: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 65: Italy: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 66: Spain: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 67: Spain: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 68: Russia: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 69: Russia: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 70: Others: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 71: Others: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 72: Latin America: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 73: Latin America: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 74: Brazil: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 75: Brazil: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 76: Mexico: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 77: Mexico: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 78: Others: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 79: Others: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
Figure 80: Middle East and Africa: AI in Agriculture Market: Sales Value (in Million US$), 2018 & 2023
Figure 81: Middle East and Africa: AI in Agriculture Market: Breakup by Country (in %), 2023
Figure 82: Middle East and Africa: AI in Agriculture Market Forecast: Sales Value (in Million US$), 2024-2032
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, 2023 & 2032
Table 2: Global: AI in Agriculture Market Forecast: Breakup by Offering (in Million US$), 2024-2032
Table 3: Global: AI in Agriculture Market Forecast: Breakup by Technology (in Million US$), 2024-2032
Table 4: Global: AI in Agriculture Market Forecast: Breakup by Application (in Million US$), 2024-2032
Table 5: Global: AI in Agriculture Market Forecast: Breakup by Region (in Million US$), 2024-2032
Table 6: Global: AI in Agriculture Market: Competitive Structure
Table 7: Global: AI in Agriculture Market: Key Players

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