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人工智慧驅動的灌溉控制系統市場預測至2034年－全球分析（按感測器類型、灌溉系統、農場類型、應用、最終用戶和地區分類）

AI-Based Irrigation Control Systems Market Forecasts to 2034 - Global Analysis By Sensor Type, Irrigation System, Farm Type, 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 Stratistics MRC 的數據，全球基於人工智慧的灌溉控制系統市場預計將在 2026 年達到 25 億美元，並在預測期內以 13.5% 的複合年成長率成長，到 2034 年達到 69 億美元。

人工智慧灌溉控制系統是一種智慧農業解決方案，它利用人工智慧和機器學習演算法來實現作物灌溉的自動化和最佳化。這些系統利用土壤濕度感測器、氣象站和作物模型的數據，精確計算植物需水量，減少水資源浪費，提高灌溉效率。它們還能與現有灌溉基礎設施無縫整合，僅在需要的時間和地點供水，從而促進永續農業發展。

對全球水資源短缺的擔憂

隨著全球水資源短缺日益加劇，以及對永續農業需求的不斷成長，農民們正在採用基於人工智慧的灌溉系統，這些系統能夠在顯著降低用水量的同時，維持甚至提高作物產量。在面臨嚴重乾旱和用水限制的地區，這些系統能夠根據植物的即時需水量精準供水，這一點變得至關重要。這種朝向更有效率資源管理模式的轉變，正在推動政府對智慧灌溉技術的投資和補貼，進而加速市場成長。

複雜性和技術專長不足

實施和管理以人工智慧為基礎的灌溉系統需要整合感測器、軟體和現有硬體，這十分複雜，對於許多缺乏技術專長的農民來說，往往是推廣應用的一大障礙。人工智慧模型難以根據當地土壤類型和作物品種進行客製化，可能導致性能欠佳，並削弱人們對這項技術的信心。陡峭的學習曲線以及持續監測和軟體更新的需求，對潛在用戶而言都是沉重的負擔，阻礙了市場滲透。

與精密農業平台整合

將人工智慧灌溉控制系統與更廣泛的精密農業和農場管理軟體平台相整合，為建立一個全面的、數據驅動的農業生態系統提供了絕佳機會。這種整體方法將灌溉數據與作物生長、天氣模式和土壤狀況等資訊結合，從而實現更優的決策。隨著數位農業的日益普及和對整合解決方案需求的成長，技術提供商被迫提供可互操作系統，這反過來又推動了市場擴張。

網路攻擊和系統故障的脆弱性

隨著人工智慧灌溉系統聯網程度的提高，它們也更容易受到網路攻擊，這可能威脅到關鍵的農業基礎設施，並導致重大的經濟損失。硬體故障、感測器失靈和軟體漏洞都可能導致系統故障，造成過度灌溉或灌溉不足，進而可能造成作物損害和減產。過度依賴自動化系統而缺乏足夠的人工備份機制，對農業企業構成重大威脅，並可能導致信心下降，阻礙系統推廣應用。

新型冠狀病毒（COVID-19）的影響：

疫情初期，電子元件和感測器價值鏈的瓶頸阻礙了新產品的普及。疫情期間，勞動力短缺和對糧食安全的擔憂加劇，凸顯了自動化、低接觸式灌溉解決方案的價值。疫情後，市場已復甦並持續永續成長，主要得益於農業生產方式向更具韌性和高效性的轉型。

在預測期內，土壤濕度感測器細分市場預計將佔據最大的市場佔有率。

土壤濕度感測器是精準灌溉領域最基礎、應用最廣泛的技術，它提供有效水資源管理所需的核心數據，預計在預測期內將佔據最大的市場佔有率。該領域擁有成熟的市場，各種深度和應用場景的感測器種類繁多，使其成為農民的可靠選擇。感測器精度和價格的不斷提升進一步鞏固了其市場主導地位，其重要性也將持續不減，因為它在支援先進的人工智慧演算法方面發揮著關鍵作用。

在預測期內，滴灌領域預計將呈現最高的複合年成長率。

在預測期內，滴灌領域預計將呈現最高的成長率。這主要歸功於滴灌固有的節水效率，而人工智慧控制的加入進一步最佳化了這一效率，能夠將水和養分精準地輸送到植物根部。這種方法最大限度地減少了蒸發和徑流，使其具有高度永續性，尤其適用於缺水地區。此外，水果、蔬菜和堅果等高價值作物的種植面積不斷擴大，這些作物都非常適合採用滴灌系統，也推動了滴灌技術的普及。這是因為人工智慧的融入提高了盈利，使其成為一項極具吸引力的投資。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率。這主要歸功於美國大規模商業農場的集中分佈以及對農業技術應用的重視。大型灌溉公司的存在以及政府對節水的大力支持進一步鞏固了北美的市場主導地位。

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。這是因為人口快速成長和糧食需求不斷增加，迫使各國採用先進的農業技術。各國政府為因應水資源短缺和改善糧食安全所做的努力，以及對傳統灌溉方式進行現代化改造的普遍需求，都是全部區域成長要素。

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目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章 全球人工智慧灌溉控制系統市場：按感測器類型分類

  • 土壤濕度感測器
  • 氣象感測器
  • 流量感測器
  • 壓力感測器
  • 水位感測器
  • 營養感測器

第6章 全球人工智慧灌溉控制系統市場：依灌溉系統分類

  • 滴灌
  • 噴水灌溉
  • 中心樞軸式灌溉
  • 橫向移動灌注
  • 微灌注
  • 地表灌溉
  • 其他灌溉系統

第7章 全球人工智慧灌溉控制系統市場：按農場類型分類

  • 田間作物
  • 果園
  • 葡萄園
  • 溫室
  • 種植作物
  • 室內垂直農場
  • 其他農場類型

第8章 全球人工智慧灌溉控制系統市場：按應用分類

  • 精準灌溉
  • 土壤濕度監測
  • 預測農作物需水量
  • 水分配管理
  • 遠端灌溉控制
  • 洩漏檢測

第9章 全球人工智慧灌溉控制系統市場：按最終用戶分類

  • 商業農場
  • 農業合作社
  • 溫室經營者
  • 高爾夫球場和體育設施
  • 政府農業機構
  • 農業研究機構
  • 其他最終用戶

第10章 全球人工智慧灌溉控制系統市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • Lindsay Corporation
  • Valmont Industries, Inc.
  • The Toro Company
  • Netafim Ltd.
  • Jain Irrigation Systems Limited
  • Hunter Industries, Inc.
  • Rain Bird Corporation
  • Trimble Inc.
  • Deere & Company
  • CNH Industrial NV
  • AGCO Corporation
  • Kubota Corporation
  • Bosch BASF Smart Farming GmbH
  • Hexagon AB
  • Topcon Corporation
  • Siemens AG
  • Schneider Electric SE
Product Code: SMRC38856

According to Stratistics MRC, the Global AI-Based Irrigation Control Systems Market is accounted for $2.5 billion in 2026 and is expected to reach $6.9 billion by 2034 growing at a CAGR of 13.5% during the forecast period. AI-based irrigation control systems are intelligent agricultural solutions that leverage artificial intelligence and machine learning algorithms to automate and optimize the watering of crops. These systems utilize data from soil moisture sensors, weather stations, and crop models to determine the precise water requirements of plants, reducing waste and improving efficiency. They integrate with existing irrigation infrastructure to deliver water only when and where it is needed, promoting sustainable agriculture.

Market Dynamics:

Driver:

Global Water Scarcity Concerns

Increasing global water scarcity and the need for sustainable agriculture are driving farmers to adopt AI-based irrigation systems that can significantly reduce water consumption while maintaining or improving crop yields. The ability of these systems to deliver precise amounts of water based on real-time plant needs is becoming essential in regions facing severe drought and water restrictions. This shift towards efficient resource management is prompting government investments and subsidies in smart irrigation technologies, thereby accelerating market growth.

Restraint:

Complexity and Lack of Technical Expertise

The complexity of deploying and managing AI-based irrigation systems, which require integration of sensors, software, and existing hardware, often presents a barrier to adoption for many farmers who lack technical expertise. The difficulty in calibrating the AI models to suit local soil types and crop varieties can lead to suboptimal performance, undermining trust in the technology. A steep learning curve and the need for continuous monitoring and software updates can be overwhelming, deterring potential users and slowing market penetration.

Opportunity:

Integration with Precision Agriculture Platforms

The integration of AI irrigation control systems with broader precision agriculture and farm management software platforms presents a significant opportunity to create comprehensive, data-driven agricultural ecosystems. This holistic approach allows for better decision-making by combining irrigation data with insights on crop health, weather patterns, and soil conditions. The increasing adoption of digital farming and the growing demand for integrated solutions are driving technology providers to offer interoperable systems, which in turn fuels market expansion.

Threat:

Vulnerability to Cyberattacks and System Failures

The increasing connectivity of AI irrigation systems makes them vulnerable to cyberattacks, which could compromise critical agricultural infrastructure and lead to significant financial losses. Hardware malfunctions, sensor errors, and software bugs can cause system failures, resulting in either overwatering or underwatering, which can damage crops and reduce yields. The over-reliance on automated systems without adequate manual backup poses a major threat to farm operations, potentially limiting trust and adoption.

Covid-19 Impact:

The pandemic initially caused supply chain bottlenecks for electronic components and sensors, delaying new installations. During the mid-pandemic period, the disruption of labor and the increased focus on food security highlighted the value of automated, low-touch irrigation solutions. Post-pandemic, the market has rebounded with sustained growth, driven by a permanent shift towards resilient and efficient farming practices.

The soil moisture sensors segment is expected to be the largest during the forecast period

The soil moisture sensors segment is expected to account for the largest market share during the forecast period, due to being the most fundamental and widely adopted technology for precision irrigation, providing the core data needed for effective water management. This segment benefits from a well-established market with a variety of sensor types available for different depths and applications, making them a reliable choice for farmers. Continuous advancements in sensor accuracy and affordability are further reinforcing its dominance, while its critical role in supporting advanced AI algorithms ensures its continued prominence.

The drip irrigation segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the drip irrigation segment is predicted to witness the highest growth rate, driven by its inherent water efficiency, which is being further optimized through AI control for precise water and nutrient delivery directly to plant roots. This method minimizes evaporation and runoff, making it highly sustainable and ideal for water-scarce regions. The growing cultivation of high-value crops like fruits, vegetables, and nuts, which are well-suited to drip systems, is also fueling its adoption, as AI integration enhances its profitability and makes it a compelling investment.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to the United States having a high concentration of large-scale commercial farms and a strong focus on agricultural technology adoption. The presence of major irrigation companies and significant government support for water conservation further solidifies the market's dominant position.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to rapidly growing populations and increasing food demand, which are pushing countries to adopt advanced agricultural technologies. Government initiatives to combat water scarcity and improve food security, along with the widespread need to modernize traditional irrigation methods, are key growth drivers across the region.

Key players in the market

Some of the key players in AI-Based Irrigation Control Systems Market include Lindsay Corporation, Valmont Industries, Inc., The Toro Company, Netafim Ltd., Jain Irrigation Systems Limited, Hunter Industries, Inc., Rain Bird Corporation, Trimble Inc., Deere & Company, CNH Industrial N.V., AGCO Corporation, Kubota Corporation, Bosch BASF Smart Farming GmbH, Hexagon AB, Topcon Corporation, Siemens AG and Schneider Electric SE.

Key Developments:

In July 2026, Valmont Industries, Inc. launched a next-generation AI-driven center pivot irrigation system that integrates satellite data for optimized water management.

In June 2026, Netafim Ltd. announced a strategic partnership with an AI platform provider to enhance its drip irrigation systems with predictive analytics for water requirements.

In May 2026, Lindsay Corporation introduced a new crop water requirement forecasting tool utilizing machine learning, integrated into its existing remote irrigation control platform.

In April 2026, Bosch BASF Smart Farming GmbH expanded its smart farming portfolio with a new AI-based irrigation controller designed for greenhouse and indoor vertical farm operations.

Sensor Types Covered:

  • Soil Moisture Sensors
  • Weather Sensors
  • Flow Sensors
  • Pressure Sensors
  • Water Level Sensors
  • Nutrient Sensors

Irrigation Systems Covered:

  • Drip Irrigation
  • Sprinkler Irrigation
  • Center Pivot Irrigation
  • Lateral Move Irrigation
  • Micro Irrigation
  • Surface Irrigation
  • Other Irrigation Systems

Farm Types Covered:

  • Field Crops
  • Orchards
  • Vineyards
  • Greenhouses
  • Plantation Crops
  • Indoor Vertical Farms
  • Other Farm Types

Applications Covered:

  • Precision Irrigation
  • Soil Moisture Monitoring
  • Crop Water Requirement Forecasting
  • Water Distribution Management
  • Remote Irrigation Control
  • Leakage Detection

End Users Covered:

  • Commercial Farms
  • Agricultural Cooperatives
  • Greenhouse Operators
  • Golf Courses and Sports Grounds
  • Government Agricultural Agencies
  • Agricultural Research Institutes
  • Other End Users

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of 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 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • 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

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI-Based Irrigation Control Systems Market, By Sensor Type

  • 5.1 Soil Moisture Sensors
  • 5.2 Weather Sensors
  • 5.3 Flow Sensors
  • 5.4 Pressure Sensors
  • 5.5 Water Level Sensors
  • 5.6 Nutrient Sensors

6 Global AI-Based Irrigation Control Systems Market, By Irrigation System

  • 6.1 Drip Irrigation
  • 6.2 Sprinkler Irrigation
  • 6.3 Center Pivot Irrigation
  • 6.4 Lateral Move Irrigation
  • 6.5 Micro Irrigation
  • 6.6 Surface Irrigation
  • 6.7 Other Irrigation Systems

7 Global AI-Based Irrigation Control Systems Market, By Farm Type

  • 7.1 Field Crops
  • 7.2 Orchards
  • 7.3 Vineyards
  • 7.4 Greenhouses
  • 7.5 Plantation Crops
  • 7.6 Indoor Vertical Farms
  • 7.7 Other Farm Types

8 Global AI-Based Irrigation Control Systems Market, By Application

  • 8.1 Precision Irrigation
  • 8.2 Soil Moisture Monitoring
  • 8.3 Crop Water Requirement Forecasting
  • 8.4 Water Distribution Management
  • 8.5 Remote Irrigation Control
  • 8.6 Leakage Detection

9 Global AI-Based Irrigation Control Systems Market, By End User

  • 9.1 Commercial Farms
  • 9.2 Agricultural Cooperatives
  • 9.3 Greenhouse Operators
  • 9.4 Golf Courses and Sports Grounds
  • 9.5 Government Agricultural Agencies
  • 9.6 Agricultural Research Institutes
  • 9.7 Other End Users

10 Global AI-Based Irrigation Control Systems Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 Lindsay Corporation
  • 13.2 Valmont Industries, Inc.
  • 13.3 The Toro Company
  • 13.4 Netafim Ltd.
  • 13.5 Jain Irrigation Systems Limited
  • 13.6 Hunter Industries, Inc.
  • 13.7 Rain Bird Corporation
  • 13.8 Trimble Inc.
  • 13.9 Deere & Company
  • 13.10 CNH Industrial N.V.
  • 13.11 AGCO Corporation
  • 13.12 Kubota Corporation
  • 13.13 Bosch BASF Smart Farming GmbH
  • 13.14 Hexagon AB
  • 13.15 Topcon Corporation
  • 13.16 Siemens AG
  • 13.17 Schneider Electric SE

List of Tables

  • Table 1 Global AI-Based Irrigation Control Systems Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Based Irrigation Control Systems Market Outlook, By Sensor Type (2023-2034) ($MN)
  • Table 3 Global AI-Based Irrigation Control Systems Market Outlook, By Soil Moisture Sensors (2023-2034) ($MN)
  • Table 4 Global AI-Based Irrigation Control Systems Market Outlook, By Weather Sensors (2023-2034) ($MN)
  • Table 5 Global AI-Based Irrigation Control Systems Market Outlook, By Flow Sensors (2023-2034) ($MN)
  • Table 6 Global AI-Based Irrigation Control Systems Market Outlook, By Pressure Sensors (2023-2034) ($MN)
  • Table 7 Global AI-Based Irrigation Control Systems Market Outlook, By Water Level Sensors (2023-2034) ($MN)
  • Table 8 Global AI-Based Irrigation Control Systems Market Outlook, By Nutrient Sensors (2023-2034) ($MN)
  • Table 9 Global AI-Based Irrigation Control Systems Market Outlook, By Irrigation System (2023-2034) ($MN)
  • Table 10 Global AI-Based Irrigation Control Systems Market Outlook, By Drip Irrigation (2023-2034) ($MN)
  • Table 11 Global AI-Based Irrigation Control Systems Market Outlook, By Sprinkler Irrigation (2023-2034) ($MN)
  • Table 12 Global AI-Based Irrigation Control Systems Market Outlook, By Center Pivot Irrigation (2023-2034) ($MN)
  • Table 13 Global AI-Based Irrigation Control Systems Market Outlook, By Lateral Move Irrigation (2023-2034) ($MN)
  • Table 14 Global AI-Based Irrigation Control Systems Market Outlook, By Micro Irrigation (2023-2034) ($MN)
  • Table 15 Global AI-Based Irrigation Control Systems Market Outlook, By Surface Irrigation (2023-2034) ($MN)
  • Table 16 Global AI-Based Irrigation Control Systems Market Outlook, By Other Irrigation Systems (2023-2034) ($MN)
  • Table 17 Global AI-Based Irrigation Control Systems Market Outlook, By Farm Type (2023-2034) ($MN)
  • Table 18 Global AI-Based Irrigation Control Systems Market Outlook, By Field Crops (2023-2034) ($MN)
  • Table 19 Global AI-Based Irrigation Control Systems Market Outlook, By Orchards (2023-2034) ($MN)
  • Table 20 Global AI-Based Irrigation Control Systems Market Outlook, By Vineyards (2023-2034) ($MN)
  • Table 21 Global AI-Based Irrigation Control Systems Market Outlook, By Greenhouses (2023-2034) ($MN)
  • Table 22 Global AI-Based Irrigation Control Systems Market Outlook, By Plantation Crops (2023-2034) ($MN)
  • Table 23 Global AI-Based Irrigation Control Systems Market Outlook, By Indoor Vertical Farms (2023-2034) ($MN)
  • Table 24 Global AI-Based Irrigation Control Systems Market Outlook, By Other Farm Types (2023-2034) ($MN)
  • Table 25 Global AI-Based Irrigation Control Systems Market Outlook, By Application (2023-2034) ($MN)
  • Table 26 Global AI-Based Irrigation Control Systems Market Outlook, By Precision Irrigation (2023-2034) ($MN)
  • Table 27 Global AI-Based Irrigation Control Systems Market Outlook, By Soil Moisture Monitoring (2023-2034) ($MN)
  • Table 28 Global AI-Based Irrigation Control Systems Market Outlook, By Crop Water Requirement Forecasting (2023-2034) ($MN)
  • Table 29 Global AI-Based Irrigation Control Systems Market Outlook, By Water Distribution Management (2023-2034) ($MN)
  • Table 30 Global AI-Based Irrigation Control Systems Market Outlook, By Remote Irrigation Control (2023-2034) ($MN)
  • Table 31 Global AI-Based Irrigation Control Systems Market Outlook, By Leakage Detection (2023-2034) ($MN)
  • Table 32 Global AI-Based Irrigation Control Systems Market Outlook, By End User (2023-2034) ($MN)
  • Table 33 Global AI-Based Irrigation Control Systems Market Outlook, By Commercial Farms (2023-2034) ($MN)
  • Table 34 Global AI-Based Irrigation Control Systems Market Outlook, By Agricultural Cooperatives (2023-2034) ($MN)
  • Table 35 Global AI-Based Irrigation Control Systems Market Outlook, By Greenhouse Operators (2023-2034) ($MN)
  • Table 36 Global AI-Based Irrigation Control Systems Market Outlook, By Golf Courses and Sports Grounds (2023-2034) ($MN)
  • Table 37 Global AI-Based Irrigation Control Systems Market Outlook, By Government Agricultural Agencies (2023-2034) ($MN)
  • Table 38 Global AI-Based Irrigation Control Systems Market Outlook, By Agricultural Research Institutes (2023-2034) ($MN)
  • Table 39 Global AI-Based Irrigation Control Systems Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.