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市场调查报告书
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1942301

2026-2034年农业人工智慧（AI）市场规模、份额、趋势及预测（按交付类型、技术、应用和地区划分）

AI in Agriculture Market Size, Share, Trends and Forecast by Offering, Technology, Application, and Region, 2026-2034
出版日期: | 出版商: IMARC | 英文 147 Pages | 商品交期: 2-3个工作天内

价格

2025年，全球农业人工智慧（AI）市场规模为26亿美元。展望未来，IMARC集团预测，到2034年，该市场规模将达到130亿美元，2026年至2034年的复合年增长率（CAGR）为19.49%。北美目前占据市场主导地位，预计2025年将占据超过36.8%的市场。这主要归功于对精密农业技术的需求不断增长，以提高作物产量，以及物联网设备和感测器在数据驱动型农业实践中的应用日益广泛。

全球农业人工智慧市场的发展动力源自于精密农业日益增长的需求，精准农业能够显着提高资源利用率并提升作物产量。人们对气候变迁和永续农业实践的日益关注，以及不断增长的全球粮食需求，进一步推动了人工智慧在农业领域的应用。此外，物联网技术、机器学习和机器人技术的快速创新，实现了自动化决策、即时监控和预测分析，显着提升了营运效率。同时，政府补贴和创投的显着增加，积极推动了智慧农业解决方案的发展，加上人工智慧设备和无人机的日益普及，共同造就了农业人工智慧市场的光明前景。

凭藉先进的技术基础设施和精密农业技术的广泛应用，美国在全球农业人工智慧产业中扮演关键角色。例如，产业报告显示，到2024年，超过70%的美国农民将至少采用一项精密农业技术。在某些情况下，这些先进的农业技术已展现出将作物产量提高30%的潜力。此外，随着研发倡议的显着增加，美国正在推动人工智慧服务的发展，包括自动化灌溉系统、作物监测和产量预测。同时，在政府促进资源效率和永续农业计画的支持下，美国受益于其强大的农业综合企业。此外，主要市场参与者的强大影响力，以及农业公司与技术供应商之间的战略联盟，进一步巩固了美国作为人工智慧驱动型农业创新领域主导国家的地位。

人工智慧在农业领域的市场趋势：

提高效率和生产力

人工智慧在农业市场应用的关键驱动因素之一是其显着提高效率和生产力。据报道，人工智慧在农业的应用可使作物产量提高高达30%。传统耕作方式依赖大量猜测和人工操作，既耗时又容易出错。人工智慧技术，例如机器学习演算法和数据分析，使农民能够在播种、灌溉和收割方面做出更明智的决策。例如，人工智慧系统可以分析土壤状况、天气模式和作物生长情况，从而优化灌溉计划并减少水资源浪费。这种精密农业方法能够提高产量并改善资源管理，使农业在长期发展中更具永续性和盈利。

劳动力短缺和成本削减

农业劳动力短缺是一个全球性问题，许多地区都面临着难以获得稳定、熟练的农业劳动力的困境。据报道，2023年，全球劳动力缺口预计将达到77%，比前一年增加2%。人工智慧和自动化技术透过减少对人力的依赖，为应对这项挑战提供了解决方案。例如，机器人可以执行除草、收割甚至采摘水果等劳力密集任务，而这些任务很难找到合适的人力资源。此外，人工智慧系统能够对大型农场进行监控和管理，从而减少对大规模现场劳动力的需求。这种自动化不仅有助于缓解劳动力短缺，还有助于降低成本。透过自动化日常工作，农民可以降低营运成本，并将资源集中在更具战略意义的农场管理，最终促进农业人工智慧市场的稳定成长。

技术进步

人工智慧和机器学习技术的快速发展是推动农业人工智慧市场成长的主要动力。根据IMARC集团预测，到2024年，全球机器学习市场规模预计将达到310亿美元。这些技术正在快速发展，为应对复杂的农业挑战提供先进的解决方案。机器学习演算法能够处理来自各种来源的大量数据，包括天气模式、作物健康状况和土壤条件，从而为农民提供准确的即时资讯。将人工智慧整合到农业机械（例如自动驾驶拖拉机和无人机）中，可以简化田间作业，降低人事费用，并提高生产力。人工智慧技术的持续发展正在扩大其在农业领域的应用范围和可用性，从而推动农业人工智慧市场的需求成长。

对永续农业实践的需求

现代农业的永续性是一个关键问题，而人工智慧在实现这一目标中发挥关键作用。人工智慧工具透过优化资源利用，监测并减少农业对环境的影响。根据2023年农业实务调查（FPS），62%的农民认识到在农业管理决策中考虑温室气体（GHG）的重要性。例如，人工智慧系统可以精确计算特定面积土地所需的水量和农药用量，从而最大限度地减少浪费和环境损害。此外，人工智慧还可以支援作物轮作和土壤健康管理，这对于长期永续性至关重要。透过减少碳足迹和推广环境友善实践，人工智慧不仅是农业工具，也是应对气候变迁的工具。

目录

第一章：序言

第二章：调查范围与调查方法

  • 调查目标
  • 相关利益者
  • 数据来源
    • 主要讯息
    • 二手资讯
  • 市场估值
    • 自下而上的方法
    • 自上而下的方法
  • 调查方法

第三章执行摘要

第四章 引言

第五章 全球农业人工智慧市场

  • 市场概览
  • 市场表现
  • 新冠疫情的影响
  • 市场预测

第六章 按产品/服务分類的市场区隔

  • 软体
  • 硬体
  • 人工智慧即服务 (AIaaS)
  • 服务

第七章 按技术分類的市场区隔

  • 机器学习
  • 电脑视觉
  • 预测分析

第八章 按应用分類的市场细分

  • 精密农业
  • 农业机器人
  • 牲畜监测
  • 无人机分析
  • 劳动管理
  • 其他的

第九章 按地区分類的市场细分

  • 北美洲
    • 我们
    • 加拿大
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 印尼
    • 其他的
  • 欧洲
    • 德国
    • 法国
    • 英国
    • 义大利
    • 西班牙
    • 俄罗斯
    • 其他的
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 其他的
  • 中东和非洲

第十章 SWOT 分析

第十一章 价值链分析

第十二章 波特五力分析

第十三章：定价分析

第十四章 竞争格局

  • 市场结构
  • 主要企业
  • 主要企业简介
    • AAA 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
Product Code: SR112026A6604

The global AI in agriculture market size was valued at USD 2.6 Billion in 2025. Looking forward, IMARC Group estimates the market to reach USD 13.0 Billion by 2034, exhibiting a CAGR of 19.49% during 2026-2034. North America currently dominates the market, holding a significant market share of over 36.8% in 2025, driven by the increasing need for precision agriculture techniques to enhance crop yield, and the growing adoption of IoT devices and sensors for data-driven farming practices.

The global AI in agriculture market is driven by the growing need for precision farming to significantly upgrade resource consumption and improve crop productivity. Elevating concerns about climate change and sustainable agricultural methods, combined with escalating food requirement worldwide, further bolsters the deployment of AI in this industry. Besides this, rapid innovations in IoT technologies, machine learning (ML), and robotics facilitate automated decision-making, real-time monitoring, and predictive analysis, substantially advancing operational efficacy. In addition, notable increase in government subsidies and ventures actively incentivize smart farming solutions, coupled with the amplifying utilization of AI-based equipment and drones, contributing to creating a positive AI in agriculture market outlook.

The United States exhibits an integral role in the global AI in agriculture industry, driven by advanced technological infrastructure and widespread adoption of precision farming practices. For instance, industry reports indicated that as of 2024, more than 70% of farmers in the United States currently implement at least one precision agriculture technique. In certain scenarios, these advanced farming practices have demonstrated the potential to boost crop yields by as much as 30%. Furthermore, with notable elevation in investments for research and development initiatives, the country incentivizes advancements in AI-based services, encompassing automated irrigation systems, crop monitoring, and yield forecast. Moreover, the U.S. extensively profits from a resilient agribusiness segment, aided by government programs endorsing resource efficacy and sustainable farming. In addition, the strong presence of major market players and tactical partnerships amongst agricultural firms and technology providers further fortify the United States' position as a leading nation in AI-powered agricultural innovations.

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. It has been reported that, AI in farming increases crop yields by up to 30%. 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. According to reports, the global labor shortage in 2023 was 77% an increase of 2% from previous year. 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, thus fostering a stable AI in agriculture market growth.

Technological advancements

The rapid advancements in AI and machine learning technologies are key drivers in the AI in Agriculture market. As per the IMARC Group, the global machine learning market reached USD 31.0 Billion in 2024. 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, thereby increasing the AI in agriculture market demand.

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. The 2023 Farm Practices Survey (FPS) indicated that 62% of farmers thought it important to consider GHGs when making farm business decisions. 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.

AI in Agriculture Industry Segmentation:

Analysis by Offering:

  • Software
  • Hardware
  • AI-as-a-Service
  • Services

Software stands as the largest offering in 2025, holding around 55.9% of the market. The software segment represents the dominant segment in the AI in Agriculture market industry, mainly propelled by the accelerating shift of agricultural firms as well as farmers toward of AI-powered decision support systems, AI-driven farm management software, and data analytics platforms. Such software services provide critical attributes, mainly including irrigation management, crop monitoring, yield prediction, and effective detection of pest, facilitating data-based and accurate agricultural practices. In addition, with the growing deployment of AI algorithms, farmers can significantly improve crop yields while lowering the harmful impact on ecosystem, easily form insight-based decisions, and upgrade resource consumption. Moreover, this segment is anticipated to experience substantial expansion as technological firms continue to design leading-edge software services customized to the transforming demands of the agriculture industry.

Analysis by Technology:

  • Machine Learning
  • Computer Vision
  • Predictive Analytics

Machine learning leads the market with around 53.3% of market share in 2025. 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. In addition to this, machine learning models help optimize resource allocation, predict crop yields, and even identify early signs of diseases or nutrient deficiencies in crops. Furthermore, the scalability and versatility of machine learning make it a fundamental technology driving precision agriculture and sustainable farming practices.

Analysis by Application:

  • Precision Farming
  • Agriculture Robots
  • Livestock Monitoring
  • Drone Analytics
  • Labor Management
  • Others

Precision farming leads the market with around 40.0% of market share in 2025. Precision farming, also widely termed as precision agriculture, dominates this market segment with its robust emphasis on upgrading the practices pertaining to the crop management. Furthermore, this segment mainly envelops the heavy deployment of AI-powered technologies for accurate monitoring of plant health, soil conditions, as well as weather status. Besides this, it facilitates farmers to make data-based decisions associated with pest control, irrigation, and fertilization, as a result leading to amplified resource effectiveness and crop yield. In addition to this, precision farming also facilitates the trend of sustainability by significantly lowering adverse impact on environment and resource wastage, thereby positioning it as a crucial and most impactful segment in expanding the AI in Agriculture market share.

Regional Analysis:

  • 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

In 2025, North America accounted for the largest market share of over 36.8%. North America dominates the market, driven by cutting-edge technological infrastructure and a strong focus on precision farming. Furthermore, heavy investments in AI adoption also fosters AI in agriculture market growth across this region. For instance, in May 2024, -Carbon Robotics, a U.S.-based AI-powered agriculture company, announced receival of significant investment from NVentures to advance agriculture by leveraging AI. Besides this, North America heavily 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.

Key Regional Takeaways:

United States AI in Agriculture Market Analysis

In 2025, United States accounted for 89.00% of the market share in North America. The increasing need for food driven by population growth is creating promising opportunities in the market. As per the United States Census Bureau, the US population estimated at 334,233,854 on January 1, 2023. As the U.S. population grows, so does the demand for food items. However, arable land and water resources are limited, making it essential to maximize agricultural productivity. AI-powered tools like precision farming systems, which use data analytics and predictive modeling, help farmers optimize resource use, enhance crop yields, and reduce waste. In line with this, the integration of Internet of Things (IoT) devices with AI is transforming U.S. agriculture. Field sensors gather real-time information on soil moisture, weather conditions, and crop health, which is processed by AI algorithms to deliver practical insights and recommendations. Companies are focusing on developing autonomous tractors and AI-driven equipment, which streamline planting, harvesting, and monitoring processes, thereby reducing labor costs and increasing efficiency. Furthermore, U.S. government programs and funding are encouraging the adoption of AI in agriculture. For instance, the USDA's Agriculture Innovation Agenda promotes technologies that increased productivity while reducing environmental impact. Federal funding for AI research in agriculture is also surging, which is driving innovation in areas like pest detection and yield prediction. Besides this, AI plays a critical role in addressing the challenges posed by climate change. Predictive models help farmers adapt to changing weather patterns by identifying optimal planting times and crop varieties. Sustainable farming practices, enabled by AI, align with growing consumer and regulatory demands for eco-friendly agriculture.

Asia Pacific AI in Agriculture Market Analysis

The Asia Pacific region is witnessing rapid growth in the adoption of artificial intelligence (AI) in agriculture, driven by factors, such as increasing food demand, technological advancements, climate resilience needs, and supportive government initiatives. The region is home to more than half of the world's population, with countries like India and China experiencing significant population growth. This is leading to higher demand for food production in a region already facing limited arable land and water resources. According to the First Advance Estimates by IBEF for the year 2023 to 2024 (Kharif only), total production of foodgrain in the nation is estimated at 148.5 million tons. AI-driven solutions like precision agriculture and smart irrigation systems optimize resource use, boost yields, and reduce waste. Apart from this, the region has seen a surge in agri-tech start-ups as they are developing AI solutions for agriculture. These advancements, ranging from drone-enabled crop surveillance to AI-driven soil evaluation, are revolutionizing conventional agricultural methods. Japan, for example, is leveraging AI for robotic harvesting and pest detection, addressing labor shortages in agriculture. Additionally, the availability of affordable IoT devices and cloud-based platforms is making AI adoption more accessible for small and medium-sized farmers. Furthermore, the Asia Pacific region is highly vulnerable to climate change, with unpredictable weather patterns affecting agriculture. AI tools enable farmers to analyze historical and real time data for better decision-making, improving resilience to droughts, floods, and other climate-related challenges. AI-enabled pest and disease management tools are also gaining traction in the region, helping reduce crop losses.

Europe AI in Agriculture Market Analysis

The adoption of artificial intelligence (AI) in agriculture across Europe is gaining momentum due to the demand for sustainable farming, labor shortages, technological innovations, and supportive regulatory frameworks. Europe is at the forefront of adopting sustainable agricultural practices, driven by the European Green Deal and Farm to Fork Strategy, which aim to reduce environmental impact and promote resource efficiency. AI-powered tools, such as precision agriculture systems and smart irrigation reduce water usage, optimize fertilizer application, and minimize waste. Additionally, there is a rise in the demand for food products on account of the population growth. As of January 2023, around 448.8 Million individuals dwelled in the European Union (EU). The most populous EU country was Germany with 84.4 Million, 19% of the EU total, as reported by the Eurostat. Apart from this, labor shortages in the agricultural sector are a significant challenge in Europe, particularly in countries like Germany, France, and the UK. AI-powered robotics and autonomous machinery are addressing this issue by automating labor-intensive tasks such as planting, weeding, and harvesting. Major companies in the region are developing AI-driven robots for weed management, reducing dependency on manual labor and chemical herbicides. Moreover, Europe has a thriving ecosystem of agri-tech start-ups and research institutions, which is driving innovation in AI applications. Technologies like drone-based crop monitoring, AI-driven soil analysis, and yield prediction are gaining popularity. For instance, Dutch start-ups are leveraging AI to optimize greenhouse farming, significantly enhancing productivity in controlled environments.

Latin America AI in Agriculture Market Analysis

The adoption of artificial intelligence (AI) in agriculture is accelerating in Latin America, driven by the need to enhance productivity, adapt to climate change, and address resource challenges. With a rising population and a critical role as a global food exporter, Latin America is turning to AI to boost agricultural efficiency. According to reports, Brazilian agribusiness exports hit a record in 2023, reaching USD 166.55 Billion. Technologies like AI-powered crop monitoring and precision farming optimize resource use, helping increase yields and reduce waste. The region faces significant climate challenges, such as droughts and unpredictable weather. AI-powered tools process weather data to deliver practical insights, enabling farmers to make well-informed choices regarding planting and harvesting activities. Moreover, countries like Brazil and Argentina are investing in AI-driven solutions, including drones, sensors, and robotics, to modernize farming practices. Furthermore, public policies and private sector investments are fostering innovation, making AI technologies more accessible to farmers.

Middle East and Africa AI in Agriculture Market Analysis

The Middle East and Africa (MEA) region is experiencing a growing adoption of artificial intelligence (AI) in agriculture, driven by the need for food security, resource optimization, and technological advancements. Rapid population growth and increasing food demand is highlighting the need for efficient farming practices. AI tools like precision agriculture and crop monitoring systems help improve yields and reduce food wastage, crucial in regions with limited arable land. AI-driven irrigation systems are addressing water scarcity, a critical issue in the arid Middle East. As per the report 'Economics of Water Scarcity in MENA: Institutional Solutions' by the end of this decade, the amount of water available per capita annually will fall below the absolute water scarcity threshold of 500 cubic meters per person, per year. Smart irrigation technologies, using AI to monitor soil and weather conditions, benefit in reducing water usage. Furthermore, supportive policies and private investments are facilitating the integration of AI technologies, driving market growth in the region.

Competitive Landscape:

The competitive landscape is exhibited by the robust establishment of advanced startups as well as key technology companies that actively provide innovative AI-powered services. Major industry players are currently emphasizing on yield prediction, precision farming, resource optimization, and plants monitoring. In addition to this, various firms are actively opting for machine learning (ML) algorithms, AI-driven drones, and robotics, to improve both sustainability and productivity. Furthermore, tactical acquisition, mergers, and partnerships are extensively prevalent as companies strive to proliferate their geographical foothold as well as portfolios. For instance, in October 2024, Google announced strategic partnership with Indian-based local startups to boost implementation of AI in key sectors of the nation, including agriculture and sustainability. Besides this, elevating investments in research and development initiatives and the implementation of AI in agribusiness by both agritech firms and large-scale farms further boost competition.

The report provides a comprehensive analysis of the competitive landscape in the AI in agriculture market with detailed profiles of all major companies, including:

  • 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.How big is the AI in agriculture market?
  • 2.What is the future outlook of AI in agriculture market?
  • 3.What are the key factors driving the AI in agriculture market?
  • 4.Which region accounts for the largest AI in agriculture market share?
  • 5.Which are the leading 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

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), 2020-2025
  • Figure 3: Global: AI in Agriculture Market Forecast: Sales Value (in Billion USD), 2026-2034
  • Figure 4: Global: AI in Agriculture Market: Breakup by Offering (in %), 2025
  • Figure 5: Global: AI in Agriculture Market: Breakup by Technology (in %), 2025
  • Figure 6: Global: AI in Agriculture Market: Breakup by Application (in %), 2025
  • Figure 7: Global: AI in Agriculture Market: Breakup by Region (in %), 2025
  • Figure 8: Global: AI in Agriculture (Software) Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 9: Global: AI in Agriculture (Software) Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 10: Global: AI in Agriculture (Hardware) Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 11: Global: AI in Agriculture (Hardware) Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 12: Global: AI in Agriculture (AI-as-a-Service) Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 13: Global: AI in Agriculture (AI-as-a-Service) Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 14: Global: AI in Agriculture (Services) Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 15: Global: AI in Agriculture (Services) Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 16: Global: AI in Agriculture (Machine Learning) Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 17: Global: AI in Agriculture (Machine Learning) Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 18: Global: AI in Agriculture (Computer Vision) Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 19: Global: AI in Agriculture (Computer Vision) Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 20: Global: AI in Agriculture (Predictive Analytics) Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 21: Global: AI in Agriculture (Predictive Analytics) Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 22: Global: AI in Agriculture (Precision Farming) Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 23: Global: AI in Agriculture (Precision Farming) Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 24: Global: AI in Agriculture (Agriculture Robots) Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 25: Global: AI in Agriculture (Agriculture Robots) Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 26: Global: AI in Agriculture (Livestock Monitoring) Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 27: Global: AI in Agriculture (Livestock Monitoring) Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 28: Global: AI in Agriculture (Drone Analytics) Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 29: Global: AI in Agriculture (Drone Analytics) Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 30: Global: AI in Agriculture (Labor Management) Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 31: Global: AI in Agriculture (Labor Management) Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 32: Global: AI in Agriculture (Other Applications) Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 33: Global: AI in Agriculture (Other Applications) Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 34: North America: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 35: North America: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 36: United States: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 37: United States: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 38: Canada: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 39: Canada: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 40: Asia-Pacific: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 41: Asia-Pacific: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 42: China: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 43: China: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 44: Japan: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 45: Japan: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 46: India: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 47: India: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 48: South Korea: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 49: South Korea: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 50: Australia: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 51: Australia: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 52: Indonesia: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 53: Indonesia: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 54: Others: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 55: Others: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 56: Europe: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 57: Europe: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 58: Germany: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 59: Germany: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 60: France: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 61: France: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 62: United Kingdom: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 63: United Kingdom: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 64: Italy: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 65: Italy: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 66: Spain: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 67: Spain: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 68: Russia: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 69: Russia: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 70: Others: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 71: Others: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 72: Latin America: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 73: Latin America: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 74: Brazil: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 75: Brazil: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 76: Mexico: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 77: Mexico: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 78: Others: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 79: Others: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • Figure 80: Middle East and Africa: AI in Agriculture Market: Sales Value (in Million USD), 2020 & 2025
  • Figure 81: Middle East and Africa: AI in Agriculture Market: Breakup by Country (in %), 2025
  • Figure 82: Middle East and Africa: AI in Agriculture Market Forecast: Sales Value (in Million USD), 2026-2034
  • 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, 2025 & 2034
  • Table 2: Global: AI in Agriculture Market Forecast: Breakup by Offering (in Million USD), 2026-2034
  • Table 3: Global: AI in Agriculture Market Forecast: Breakup by Technology (in Million USD), 2026-2034
  • Table 4: Global: AI in Agriculture Market Forecast: Breakup by Application (in Million USD), 2026-2034
  • Table 5: Global: AI in Agriculture Market Forecast: Breakup by Region (in Million USD), 2026-2034
  • Table 6: Global: AI in Agriculture Market: Competitive Structure
  • Table 7: Global: AI in Agriculture Market: Key Players