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市場調查報告書
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1871847

全球作物監測人工智慧市場：預測至 2032 年—按產品、作物類型、部署方式、技術、應用、最終用戶和地區進行分析

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個工作天內

價格

根據 Strategystics MRC 的一項研究，預計到 2025 年，全球作物監測人工智慧市場價值將達到 34 億美元，到 2032 年將達到 127 億美元，預測期內複合年成長率為 20.3%。

人工智慧（AI）在作物監測中的應用是指利用先進的演算法、機器學習模型和數據分析來解讀農業​​數據並最佳化作物管理。透過整合衛星影像、無人機監控和基於物聯網的感測器，人工智慧能夠即時監測作物健康狀況、土壤狀況、病蟲害和天氣模式。這使得農民能夠根據數據做出灌溉、施肥和收割方面的決策，從而提高生產力和永續性。人工智慧驅動的預測分析也能預測產量，並及早發現作物脅迫和病害的徵兆，最大限度地減少損失，提高農場整體效率，同時促進精密農業的發展。

改進產量預測與決策

農民們正在利用人工智慧模型分析土壤健康狀況、天氣模式和作物脅迫情況，以掌握干預時機並最佳化資源配置。該平台支援從田間到區域尺度的頻譜影像、感測器融合和預測分析。與衛星數據、無人機圖像和農藝資料庫的整合提高了準確性和響應速度。商業農場、合作社和農業科技Start-Ups對數據驅動型精密農業工具的需求日益成長。這些趨勢正在推動該平台在以產量為導向、以永續性為驅動的農業生態系統中的應用。

小規模農場面臨啟動成本高、投資報酬率不確定等問題

許多農民缺乏部署人工智慧解決方案所需的資金、技術專長和數位基礎設施。企業在小規模和自給農業模式下證明成本效益和長期價值面臨挑戰。缺乏特定地點的數據和客製化演算法進一步加劇了性能和可靠性方面的困難。供應商必須提供模組化定價、行動優先介面和特定地點的培訓，才能提高採用率。這些限制因素持續阻礙著平台在小規模和資源匱乏的農業領域的成熟。

機器學習和邊緣運算的進展

模型可在本地處理感測器數據，從而降低偏遠地區和高產量農場的延遲、頻寬和對雲端的依賴。該平台採用輕量、可擴展的架構，支援異常檢測、病害預測和灌溉最佳化。與物聯網設備、行動應用和低功耗處理器的整合增強了其可存取性和田間應用。新興市場和基礎設施有限的地區正在推動對適應性強、彈性高且能夠離線運作的解決方案的需求。這些趨勢正在促進邊緣運算、機器學習驅動的作物監測平台的發展。

模型的可轉移性和複雜性

針對特定土壤、氣候和作物條件訓練的人工智慧模型，在應用於新的地區或農業系統時可能表現不佳。企業在應對多樣化的農業環境時，面臨平衡模型通用性和準確性的挑戰。缺乏標準化資料集、可解釋性和農藝檢驗會降低信任度和採用率。供應商被敦促投資於聯邦學習、領域自適應和以農民為中心的設計，以提高模型的穩健性。這些限制持續限制平台在動態且資料匱乏的作物監測環境中的可靠性。

新冠疫情的影響：

疫情擾亂了農業供應鏈、田間作業和推廣服務，同時也加速了作物監測領域的數位轉型。封鎖措施延緩了播種、收割和投入品的交付，同時也增加了對遙感探測和自主監測的需求。人工智慧平台迅速擴展，透過行動和衛星管道支援病害檢測、產量預測和投入最佳化。各國政府、合作社和農業科技公司對雲端基礎設施、無人機部署和數位農藝的投資激增。政策制定者和消費者對糧食安全和氣候適應能力的認知不斷提高。這些變化正在推動對人工智慧驅動、數位化韌性強的作物監測基礎設施的長期投資。

預計在預測期內，物聯網 (IoT) 領域將佔據最大的市場佔有率。

由於物聯網（IoT）技術在作物監測工作流程中具有多功能性、擴充性和整合潛力，預計在預測期內，該領域將佔據最大的市場佔有率。相關平台利用感測器、無人機和成像設備收集土壤濕度、植物健康狀況和天氣狀況的即時數據。與人工智慧引擎、雲端儀錶板和行動應用程式的整合，增強了決策和營運管理能力。精密農業和智慧農業計畫正在推動對高度互通性、低功耗且能夠承受惡劣環境的設備的需求。供應商提供即插即用套件、預測性警報和生命週期分析等功能，以幫助農場層級推廣應用。這些特性鞏固了物聯網作物監測平台在該領域的領先地位。

產量預測板塊在預測期內將呈現最高的複合年成長率。

隨著人工智慧平台拓展至預測性農藝和作物規劃領域，預計產量預測領域將在預測期內達到最高成長率。這些模型利用歷史資料、氣象資訊和作物影像來估算產量，並最佳化物流、採購和定價。平台支援多季分析、即時更新以及針對作物類型和地區量身定做的風險建模。與供應鏈系統、市場儀錶板和保險平台的整合提升了價值並增強了相關人員。合作社、相關企業和政府專案對擴充性、準確且本地化的預測工具的需求日益成長。這些趨勢正在推動以產量為中心的作物監測人工智慧平台的整體成長。

佔比最大的地區：

由於農業科技（AgTech）的成熟、基礎設施的完善以及機構對農業人工智慧的投資，預計北美將在預測期內佔據最大的市場佔有率。各公司正在田間作物、特種作物和溫室種植作業中部署平台，以提高產量永續性和合規性。對無人機網路、邊緣運算和農藝建模的投資支持了擴充性和創新。主要供應商、研究機構和政策框架的存在正在推動生態系統的深化和應用。各公司正在調整其作物監測策略，使其與美國）的要求、環境、社會和治理（ESG）目標以及氣候適應計畫保持一致。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於人口壓力、氣候變遷和數位農業在區域經濟中的整合。印度、中國、印尼和越南等國正在稻米、小麥和園藝領域拓展其平台。政府支持計畫正在推動農業領域的數位化推廣服務、智慧灌溉和人工智慧孵化。本地供應商正在提供以行動端為先導、多語言且符合當地文化需求的解決方案，以滿足小規模農戶和合作社的需求。都市區農業帶對擴充性、全面且具有氣候適應性的作物監測基礎設施的需求日益成長。這些趨勢正在加速亞太地區農業人工智慧創新和應用的發展。

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    • 基於產品系列、地域覆蓋和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 引言

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 分析方法
  • 分析材料
    • 原始研究資料
    • 二手研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 介紹
  • 促進要素
  • 抑制因素
  • 市場機遇
  • 威脅
  • 技術分析
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新參與企業的威脅
  • 公司間的競爭

5. 全球作物監測人工智慧市場（依產品/服務分類）

  • 介紹
  • 硬體
    • 感應器
    • 無人機和無人駕駛飛行器
    • 影像設備
    • GPS和GNSS系統
    • 物聯網設備
  • 軟體
    • 作物監測平台
    • 預測分析工具
    • 農場管理系統
  • 服務
    • 整合與部署
    • 諮詢與支持
    • 培訓和維護

6. 全球作物監測人工智慧市場（依作物類型分類）

  • 介紹
  • 糧食
  • 水果和蔬菜
  • 油籽和豆類
  • 其他作物類型

7. 全球作物監測人工智慧市場以部署方式分類

  • 介紹
  • 雲端基礎的
  • 本地部署

8. 全球作物監測人工智慧市場（按技術分類）

  • 介紹
  • 人工智慧（AI）
  • 機器學習（ML）
  • 物聯網 (IoT)
  • 巨量資料分析
  • 地理空間與遙感探測
  • 其他技術

9. 全球作物監測人工智慧市場（按應用領域分類）

  • 介紹
  • 作物健康監測
  • 產量預測
  • 病蟲害檢測
  • 土壤和養分分析
  • 其他用途

第10章 全球作物監測人工智慧市場（按最終用戶分類）

  • 介紹
  • 農業合作社
  • 研究所
  • 政府機構
  • 農業技術公司
  • 其他最終用戶

第11章 全球作物監測人工智慧市場（按地區分類）

  • 介紹
  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第12章：主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併（M&A）
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第13章：公司簡介

  • 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.