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市场调查报告书
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1871847

全球作物监测人工智慧市场：预测至 2032 年—按产品、作物类型、部署方式、技术、应用、最终用户和地区进行分析

AI in Crop Monitoring Market Forecasts to 2032 - Global Analysis By Offering (Hardware, Software and Services), Crop Type, Deployment Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Strategystics MRC 的一项研究，预计到 2025 年，全球作物监测人工智慧市场价值将达到 34 亿美元，到 2032 年将达到 127 亿美元，预测期内复合年增长率为 20.3%。

人工智慧（AI）在作物监测中的应用是指利用先进的演算法、机器学习模型和数据分析来解读农业​​数据并优化作物管理。透过整合卫星影像、无人机监控和基于物联网的感测器，人工智慧能够即时监测作物健康状况、土壤状况、病虫害和天气模式。这使得农民能够根据数据做出灌溉、施肥和收割方面的决策，从而提高生产力和永续性。人工智慧驱动的预测分析也能预测产量，并及早发现作物胁迫和病害的征兆，最大限度地减少损失，提高农场整体效率，同时促进精密农业的发展。

改进产量预测与决策

农民们正在利用人工智慧模型分析土壤健康状况、天气模式和作物胁迫情况，以掌握干预时机并优化资源配置。该平台支援从田间到区域尺度的频谱影像、感测器融合和预测分析。与卫星数据、无人机图像和农艺资料库的整合提高了准确性和响应速度。商业农场、合作社和农业科技Start-Ups对数据驱动型精密农业工具的需求日益增长。这些趋势正在推动该平台在以产量为导向、以永续性为驱动的农业生态系统中的应用。

小规模农场面临启动成本高、投资报酬率不确定等问题

许多农民缺乏部署人工智慧解决方案所需的资金、技术专长和数位基础设施。企业在小规模和自给农业模式下证明成本效益和长期价值面临挑战。缺乏特定地点的数据和客製化演算法进一步加剧了性能和可靠性方面的困难。供应商必须提供模组化定价、行动优先介面和特定地点的培训，才能提高采用率。这些限制因素持续阻碍着平台在小规模和资源匮乏的农业领域的成熟。

机器学习和边缘运算的进展

模型可在本地处理感测器数据，从而降低偏远地区和高产量农场的延迟、频宽和对云端的依赖。该平台采用轻量、可扩展的架构，支援异常检测、病害预测和灌溉优化。与物联网设备、行动应用和低功耗处理器的整合增强了其可存取性和田间应用。新兴市场和基础设施有限的地区正在推动对适应性强、弹性高且能够离线运作的解决方案的需求。这些趋势正在促进边缘运算、机器学习驱动的作物监测平台的发展。

模型的可转移性和复杂性

针对特定土壤、气候和作物条件训练的人工智慧模型，在应用于新的地区或农业系统时可能表现不佳。企业在应对多样化的农业环境时，面临平衡模型通用性和准确性的挑战。缺乏标准化资料集、可解释性和农艺检验会降低信任度和采用率。供应商被敦促投资于联邦学习、领域自适应和以农民为中心的设计，以提高模型的稳健性。这些限制持续限制平台在动态且资料匮乏的作物监测环境中的可靠性。

新冠疫情的影响：

疫情扰乱了农业供应链、田间作业和推广服务，同时也加速了作物监测领域的数位转型。封锁措施延缓了播种、收割和投入品的交付，同时也增加了对遥感探测和自主监测的需求。人工智慧平台迅速扩展，透过行动和卫星管道支援病害检测、产量预测和投入优化。各国政府、合作社和农业科技公司对云端基础设施、无人机部署和数位农艺的投资激增。政策制定者和消费者对粮食安全和气候适应能力的认识不断提高。这些变化正在推动对人工智慧驱动、数位化韧性强的作物监测基础设施的长期投资。

预计在预测期内，物联网 (IoT) 领域将占据最大的市场份额。

由于物联网（IoT）技术在作物监测工作流程中具有多功能性、扩充性和整合潜力，预计在预测期内，该领域将占据最大的市场份额。相关平台利用感测器、无人机和成像设备收集土壤湿度、植物健康状况和天气状况的即时数据。与人工智慧引擎、云端仪錶板和行动应用程式的集成，增强了决策和营运管理能力。精密农业和智慧农业计画正在推动对高度互通性、低功耗且能够承受恶劣环境的设备的需求。供应商提供即插即用套件、预测性警报和生命週期分析等功能，以帮助农场层级推广应用。这些特性巩固了物联网作物监测平台在该领域的领先地位。

产量预测板块在预测期内将呈现最高的复合年增长率。

随着人工智慧平台拓展至预测性农艺和作物规划领域，预计产量预测领域将在预测期内达到最高成长率。这些模型利用历史资料、气象资讯和作物影像来估算产量，并优化物流、采购和定价。平台支援多季分析、即时更新以及针对作物类型和地区量身定制的风险建模。与供应链系统、市场仪錶板和保险平台的整合提升了价值并增强了相关人员。合作社、相关企业和政府专案对扩充性、准确且本地化的预测工具的需求日益增长。这些趋势正在推动以产量为中心的作物监测人工智慧平台的整体成长。

占比最大的地区：

由于农业科技（AgTech）的成熟、基础设施的完善以及机构对农业人工智慧的投资，预计北美将在预测期内占据最大的市场份额。各公司正在田间作物、特种作物和温室种植作业中部署平台，以提高产量永续性和合规性。对无人机网路、边缘运算和农艺建模的投资支持了扩充性和创新。主要供应商、研究机构和政策框架的存在正在推动生态系统的深化和应用。各公司正在调整其作物监测策略，使其与美国）的要求、环境、社会和治理（ESG）目标以及气候适应计画保持一致。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于人口压力、气候变迁和数位农业在区域经济中的整合。印度、中国、印尼和越南等国正在稻米、小麦和园艺领域拓展其平台。政府支持计画正在推动农业领域的数位化推广服务、智慧灌溉和人工智慧孵化。本地供应商正在提供以行动端为先导、多语言且符合当地文化需求的解决方案，以满足小规模农户和合作社的需求。都市区农业带对扩充性、全面且具有气候适应性的作物监测基础设施的需求日益增长。这些趋势正在加速亚太地区农业人工智慧创新和应用的发展。

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  • 公司简介
    • 对最多三家其他公司进行全面分析
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  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 介绍
  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球作物监测人工智慧市场（依产品/服务分类）

  • 介绍
  • 硬体
    • 感应器
    • 无人机和无人驾驶飞行器
    • 影像设备
    • GPS和GNSS系统
    • 物联网设备
  • 软体
    • 作物监测平台
    • 预测分析工具
    • 农场管理系统
  • 服务
    • 整合与部署
    • 咨询与支持
    • 培训和维护

6. 全球作物监测人工智慧市场（依作物类型划分）

  • 介绍
  • 粮食
  • 水果和蔬菜
  • 油籽和豆类
  • 其他作物类型

7. 全球作物监测人工智慧市场以部署方式划分

  • 介绍
  • 云端基础的
  • 本地部署

8. 全球作物监测人工智慧市场（按技术划分）

  • 介绍
  • 人工智慧（AI）
  • 机器学习（ML）
  • 物联网 (IoT)
  • 巨量资料分析
  • 地理空间与遥感探测
  • 其他技术

9. 全球作物监测人工智慧市场（按应用领域划分）

  • 介绍
  • 作物健康监测
  • 产量预测
  • 病虫害检测
  • 土壤和养分分析
  • 其他用途

第十章 全球作物监测人工智慧市场（按最终用户划分）

  • 介绍
  • 农业合作社
  • 研究所
  • 政府机构
  • 农业技术公司
  • 其他最终用户

第十一章 全球作物监测人工智慧市场（按地区划分）

  • 介绍
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十二章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十三章：公司简介

  • FlyPix AI
  • Prospera Technologies Ltd.
  • Taranis Inc.
  • Agremo doo
  • Gamaya SA
  • CropX Technologies Ltd.
  • PEAT GmbH（Plantix）
  • OneSoil Inc.
  • Skyx Ltd.
  • Resson Aerospace Corporation
  • Farmwave Inc.
  • AgriTask Ltd.
  • Ceres Imaging Inc.
  • Sentera Inc.
  • PrecisionHawk Inc.
Product Code: SMRC32217

According to Stratistics MRC, the Global AI in Crop Monitoring Market is accounted for $3.4 billion in 2025 and is expected to reach $12.7 billion by 2032 growing at a CAGR of 20.3% during the forecast period. Artificial Intelligence (AI) in crop monitoring refers to the use of advanced algorithms, machine learning models, and data analytics to analyze agricultural data and optimize crop management. By integrating satellite imagery, drone surveillance, and IoT-based sensors, AI enables real-time monitoring of crop health, soil conditions, pest infestations, and weather patterns. It helps farmers make data-driven decisions on irrigation, fertilization, and harvesting, improving productivity and sustainability. AI-powered predictive analytics also forecast yield outcomes and detect early signs of stress or disease, minimizing losses and enhancing overall farm efficiency while promoting precision agriculture practices.

Market Dynamics:

Driver:

Improved yield prediction & decision-making

Farmers use AI models to analyze soil health weather patterns and crop stress for timely interventions and resource optimization. Platforms support multispectral imaging sensor fusion and predictive analytics across field-level and regional deployments. Integration with satellite data drone imagery and agronomic databases enhance accuracy and responsiveness. Demand for data-driven and precision-focused tools is rising across commercial farms cooperatives and agtech startups. These dynamics are propelling platform deployment across yield-centric and sustainability-driven agriculture ecosystems.

Restraint:

High upfront cost & unclear ROI for small farms

Many growers lack access to capital technical expertise or digital infrastructure to adopt AI-based solutions. Enterprises face challenges in demonstrating cost-effectiveness and long-term value across low-acreage and subsistence farming models. Lack of localized data and tailored algorithms further complicates performance and trust. Vendors must offer modular pricing mobile-first interfaces and region-specific training to improve uptake. These constraints continue to hinder platform maturity across smallholder and resource-constrained farming segments.

Opportunity:

Advances in ML and edge computing

Models process sensor data locally to reduce latency bandwidth and cloud dependency across remote and high-volume farms. Platforms support anomaly detection disease prediction and irrigation optimization using lightweight and scalable architectures. Integration with IoT devices mobile apps and low-power processors enhances accessibility and field-level deployment. Demand for adaptive resilient and offline-capable solutions is rising across emerging markets and infrastructure-limited geographies. These trends are fostering growth across edge-enabled and ML-driven crop monitoring platforms.

Threat:

Model transferability & complexity

AI models trained on specific soil climate and crop conditions may underperform when applied to new regions or farming systems. Enterprises face challenges in balancing generalization with precision across heterogeneous agricultural environments. Lack of standardized datasets explainability and agronomic validation degrades trust and adoption. Vendors must invest in federated learning domain adaptation and farmer-centric design to improve model robustness. These limitations continue to constrain platform reliability across dynamic and data-scarce crop monitoring contexts.

Covid-19 Impact:

The pandemic disrupted agricultural supply chains field operations and extension services while accelerating digital transformation across crop monitoring. Lockdowns delayed planting harvesting and input delivery while increasing demand for remote sensing and autonomous monitoring. AI platforms scaled rapidly to support disease detection yield forecasting and input optimization across mobile and satellite channels. Investment in cloud infrastructure drone deployment and digital agronomy surged across governments cooperatives and agtech firms. Public awareness of food security and climate resilience increased across policy and consumer circles. These shifts are reinforcing long-term investment in AI-enabled and digitally resilient crop monitoring infrastructure.

The internet of things (IoT) segment is expected to be the largest during the forecast period

The internet of things (IoT) segment is expected to account for the largest market share during the forecast period due to its versatility scalability and integration potential across crop monitoring workflows. Platforms use sensors drones and imaging devices to collect real-time data on soil moisture plant health and weather conditions. Integration with AI engines cloud dashboards and mobile apps enhances decision-making and operational control. Demand for interoperable low-power and field-hardened devices is rising across precision agriculture and smart farming initiatives. Vendors offer plug-and-play kits predictive alerts and lifecycle analytics to support farm-level deployment. These capabilities are boosting segment dominance across IoT-enabled crop monitoring platforms.

The yield forecasting segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the yield forecasting segment is predicted to witness the highest growth rate as AI platforms expand across predictive agronomy and harvest planning. Models use historical data weather inputs and crop imagery to estimate output and optimize logistics procurement and pricing. Platforms support multi-season analysis real-time updates and risk modeling tailored to crop type and geography. Integration with supply chain systems market dashboards and insurance platforms enhances value and stakeholder alignment. Demand for scalable accurate and regionally adapted forecasting tools is rising across cooperatives agribusinesses and government programs. These dynamics are accelerating growth across yield-focused AI in crop monitoring platforms.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to its agtech maturity infrastructure readiness and institutional investment across AI in agriculture. Enterprises deploy platforms across row crops specialty produce and greenhouse operations to improve yield sustainability and compliance. Investment in drone networks edge computing and agronomic modeling supports scalability and innovation. Presence of leading vendors' research institutions and policy frameworks drives ecosystem depth and adoption. Firms align crop monitoring strategies with USDA mandates ESG goals and climate adaptation programs.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR as population pressure climate volatility and digital agriculture converge across regional economies. Countries like India China Indonesia and Vietnam scale platforms across rice wheat and horticulture segments. Government-backed programs support digital extension services smart irrigation and AI incubation across farming communities. Local providers offer mobile-first multilingual and culturally adapted solutions tailored to smallholder and cooperative needs. Demand for scalable inclusive and climate-resilient crop monitoring infrastructure is rising across urban and rural agricultural zones. These trends are accelerating regional growth across Asia Pacific's AI in agriculture innovation and deployment.

Key players in the market

Some of the key players in AI in Crop Monitoring Market include FlyPix AI, Prospera Technologies Ltd., Taranis Inc., Agremo d.o.o., Gamaya SA, CropX Technologies Ltd., PEAT GmbH (Plantix), OneSoil Inc., Skyx Ltd., Resson Aerospace Corporation, Farmwave Inc., AgriTask Ltd., Ceres Imaging Inc., Sentera Inc. and PrecisionHawk Inc.

Key Developments:

In October 2024, Taranis entered a three-year strategic partnership with Syngenta Crop Protection to deliver AI-powered agronomy solutions to agricultural retailers across the U.S. The collaboration combined Taranis' drone-based scouting and generative AI recommendations with Syngenta's agronomic support, enabling leaf-level insights and precision product selection for growers.

In May 2021, Prospera Technologies was acquired by Valmont Industries Inc., a global leader in irrigation and infrastructure. The acquisition aimed to combine Prospera's computer vision and machine learning tools with Valmont's pivot irrigation systems, creating a unified platform for real-time crop health monitoring and resource optimization.

Offerings Covered:

  • Hardware
  • Software
  • Services

Crop Types Covered:

  • Cereals & Grains
  • Fruits & Vegetables
  • Oilseeds & Pulses
  • Other Crop Types

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises

Technologies Covered:

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Internet of Things (IoT)
  • Big Data Analytics
  • Geospatial and Remote Sensing
  • Other Technologies

Applications Covered:

  • Crop Health Monitoring
  • Yield Forecasting
  • Pest & Disease Detection
  • Soil & Nutrient Analysis
  • Other Applications

End Users Covered:

  • Agricultural Cooperatives
  • Research Institutions
  • Government Agencies
  • Agri-Tech Companies
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI in Crop Monitoring Market, By Offering

  • 5.1 Introduction
  • 5.2 Hardware
    • 5.2.1 Sensors
    • 5.2.2 Drones & UAVs
    • 5.2.3 Imaging Devices
    • 5.2.4 GPS & GNSS Systems
    • 5.2.5 IoT Devices
  • 5.3 Software
    • 5.3.1 Crop Monitoring Platforms
    • 5.3.2 Predictive Analytics Tools
    • 5.3.3 Farm Management Systems
  • 5.4 Services
    • 5.4.1 Integration & Deployment
    • 5.4.2 Consulting & Support
    • 5.4.3 Training & Maintenance

6 Global AI in Crop Monitoring Market, By Crop Type

  • 6.1 Introduction
  • 6.2 Cereals & Grains
  • 6.3 Fruits & Vegetables
  • 6.4 Oilseeds & Pulses
  • 6.5 Other Crop Types

7 Global AI in Crop Monitoring Market, By Deployment Mode

  • 7.1 Introduction
  • 7.2 Cloud-Based
  • 7.3 On-Premises

8 Global AI in Crop Monitoring Market, By Technology

  • 8.1 Introduction
  • 8.2 Artificial Intelligence (AI)
  • 8.3 Machine Learning (ML)
  • 8.4 Internet of Things (IoT)
  • 8.5 Big Data Analytics
  • 8.6 Geospatial and Remote Sensing
  • 8.7 Other Technologies

9 Global AI in Crop Monitoring Market, By Application

  • 9.1 Introduction
  • 9.2 Crop Health Monitoring
  • 9.3 Yield Forecasting
  • 9.4 Pest & Disease Detection
  • 9.5 Soil & Nutrient Analysis
  • 9.6 Other Applications

10 Global AI in Crop Monitoring Market, By End User

  • 10.1 Introduction
  • 10.2 Agricultural Cooperatives
  • 10.3 Research Institutions
  • 10.4 Government Agencies
  • 10.5 Agri-Tech Companies
  • 10.6 Other End Users

11 Global AI in Crop Monitoring Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 FlyPix AI
  • 13.2 Prospera Technologies Ltd.
  • 13.3 Taranis Inc.
  • 13.4 Agremo d.o.o.
  • 13.5 Gamaya SA
  • 13.6 CropX Technologies Ltd.
  • 13.7 PEAT GmbH (Plantix)
  • 13.8 OneSoil Inc.
  • 13.9 Skyx Ltd.
  • 13.10 Resson Aerospace Corporation
  • 13.11 Farmwave Inc.
  • 13.12 AgriTask Ltd.
  • 13.13 Ceres Imaging Inc.
  • 13.14 Sentera Inc.
  • 13.15 PrecisionHawk Inc.

List of Tables

  • Table 1 Global AI in Crop Monitoring Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Crop Monitoring Market Outlook, By Offering (2024-2032) ($MN)
  • Table 3 Global AI in Crop Monitoring Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 4 Global AI in Crop Monitoring Market Outlook, By Sensors (2024-2032) ($MN)
  • Table 5 Global AI in Crop Monitoring Market Outlook, By Drones & UAVs (2024-2032) ($MN)
  • Table 6 Global AI in Crop Monitoring Market Outlook, By Imaging Devices (2024-2032) ($MN)
  • Table 7 Global AI in Crop Monitoring Market Outlook, By GPS & GNSS Systems (2024-2032) ($MN)
  • Table 8 Global AI in Crop Monitoring Market Outlook, By IoT Devices (2024-2032) ($MN)
  • Table 9 Global AI in Crop Monitoring Market Outlook, By Software (2024-2032) ($MN)
  • Table 10 Global AI in Crop Monitoring Market Outlook, By Crop Monitoring Platforms (2024-2032) ($MN)
  • Table 11 Global AI in Crop Monitoring Market Outlook, By Predictive Analytics Tools (2024-2032) ($MN)
  • Table 12 Global AI in Crop Monitoring Market Outlook, By Farm Management Systems (2024-2032) ($MN)
  • Table 13 Global AI in Crop Monitoring Market Outlook, By Services (2024-2032) ($MN)
  • Table 14 Global AI in Crop Monitoring Market Outlook, By Integration & Deployment (2024-2032) ($MN)
  • Table 15 Global AI in Crop Monitoring Market Outlook, By Consulting & Support (2024-2032) ($MN)
  • Table 16 Global AI in Crop Monitoring Market Outlook, By Training & Maintenance (2024-2032) ($MN)
  • Table 17 Global AI in Crop Monitoring Market Outlook, By Crop Type (2024-2032) ($MN)
  • Table 18 Global AI in Crop Monitoring Market Outlook, By Cereals & Grains (2024-2032) ($MN)
  • Table 19 Global AI in Crop Monitoring Market Outlook, By Fruits & Vegetables (2024-2032) ($MN)
  • Table 20 Global AI in Crop Monitoring Market Outlook, By Oilseeds & Pulses (2024-2032) ($MN)
  • Table 21 Global AI in Crop Monitoring Market Outlook, By Other Crop Types (2024-2032) ($MN)
  • Table 22 Global AI in Crop Monitoring Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 23 Global AI in Crop Monitoring Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 24 Global AI in Crop Monitoring Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 25 Global AI in Crop Monitoring Market Outlook, By Technology (2024-2032) ($MN)
  • Table 26 Global AI in Crop Monitoring Market Outlook, By Artificial Intelligence (AI) (2024-2032) ($MN)
  • Table 27 Global AI in Crop Monitoring Market Outlook, By Machine Learning (ML) (2024-2032) ($MN)
  • Table 28 Global AI in Crop Monitoring Market Outlook, By Internet of Things (IoT) (2024-2032) ($MN)
  • Table 29 Global AI in Crop Monitoring Market Outlook, By Big Data Analytics (2024-2032) ($MN)
  • Table 30 Global AI in Crop Monitoring Market Outlook, By Geospatial and Remote Sensing (2024-2032) ($MN)
  • Table 31 Global AI in Crop Monitoring Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 32 Global AI in Crop Monitoring Market Outlook, By Application (2024-2032) ($MN)
  • Table 33 Global AI in Crop Monitoring Market Outlook, By Crop Health Monitoring (2024-2032) ($MN)
  • Table 34 Global AI in Crop Monitoring Market Outlook, By Yield Forecasting (2024-2032) ($MN)
  • Table 35 Global AI in Crop Monitoring Market Outlook, By Pest & Disease Detection (2024-2032) ($MN)
  • Table 36 Global AI in Crop Monitoring Market Outlook, By Soil & Nutrient Analysis (2024-2032) ($MN)
  • Table 37 Global AI in Crop Monitoring Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 38 Global AI in Crop Monitoring Market Outlook, By End User (2024-2032) ($MN)
  • Table 39 Global AI in Crop Monitoring Market Outlook, By Agricultural Cooperatives (2024-2032) ($MN)
  • Table 40 Global AI in Crop Monitoring Market Outlook, By Research Institutions (2024-2032) ($MN)
  • Table 41 Global AI in Crop Monitoring Market Outlook, By Government Agencies (2024-2032) ($MN)
  • Table 42 Global AI in Crop Monitoring Market Outlook, By Agri-Tech Companies (2024-2032) ($MN)
  • Table 43 Global AI in Crop Monitoring Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.