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2113021

人工智慧驅動的土壤健康分析市場預測至2034年：按組件、部署模式、技術、應用、最終用戶和地區分類的全球分析

AI-Powered Soil Health Analytics Market Forecasts to 2034 - Global Analysis By Component (Solutions, Services and Software Platforms), 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 Stratistics MRC 的數據，全球人工智慧驅動的土壤健康分析市場預計將在 2026 年達到 3 億美元，並在預測期內以 25.0% 的複合年成長率成長，到 2034 年達到 19 億美元。

人工智慧驅動的土壤健康分析是指利用先進的運算系統，將人工智慧、土壤感測器網路和遙感探測數據結合，用於評估、監測和預測土壤狀況。這些平台利用機器學習演算法分析土壤的化學、生物和物理參數，例如養分含量、水分含量、有機碳含量和微生物活性。它們透過雲端軟體平台、行動應用程式和連接到物聯網感測器陣列的邊緣運算設備進行部署。這些系統透過識別土壤劣化模式、最佳化施肥和預測不同農業區域的作物適宜性，為精密農業提供可操作的見解。

精密農業簡介

隨著全球農業日益重視永續投入的最佳化和產量最大化，對人工智慧驅動的土壤健康分析平台的需求正在加速成長。農民們正擴大採用感測器網路和衛星影像，從基於日曆的土壤管理轉向數據驅動的土壤管理。政府對氣候智慧型農業和碳封存監測的補貼正在催生公共採購管道。領先的農業技術公司正在將土壤分析功能整合到綜合農場管理平台中。這些協同趨勢正在推動對利用本地土壤資料庫訓練的人工智慧模型進行持續投資。

感測器基礎設施成本

建構高密度土壤感測器網路需要大量資金投入，這成為小規模農戶廣泛採用該技術的主要障礙。每個監測點都需要安裝濕度探頭、養分感測器和遙測設備，導致每公頃部署成本高昂，許多農業企業難以負擔。農村地區有限的資料通訊通訊能力也限制了即時數據傳輸。此外，不同土壤類型和氣候帶的校準要求也使得標準化變得困難。這些經濟和技術限制使得該技術主要面向大規模商業農場和研究機構。

排碳權檢驗

自願性和監管合規性碳市場的興起為人工智慧驅動的土壤健康分析提供了巨大機遇，該分析能夠量化土壤有機碳固存。農業技術平台正在開發機器學習模型，以預測不同犁地方式和覆蓋作物方案下的碳儲存率。分析服務提供者與碳權登記機構之間的合作正在簡化農場層級碳抵銷的檢驗流程。北美和歐洲各國政府應對氣候變遷的措施正日益強制要求進行土壤碳監測。隨著碳定價機制的日益成熟，土壤健康測量的潛在市場正從農業科學領域擴展到環境金融領域。

透過開放原始碼模型進行轉型

開放原始碼地理空間分析工具和公開衛星土壤濕度資料集的激增，正對專有的AI土壤健康平台構成競爭壓力。研究機構和政府部門正在發布可免費存取的土壤頻譜庫和機器學習模型，以減少對商業解決方案的依賴。這些公開可用的替代方案具有持續改進學術研究成果的優勢，並且可以免費使用。大型農業合作社正在利用開放原始碼框架來建構其內部分析能力。隨著免費工具精度的提高，農民可能不太願意為專有平台支付高昂的價格。

新型冠狀病毒（COVID-19）的影響：

新冠疫情擾亂了田間感測器部署計劃，同時也加速了農業營運的數位轉型。供應鏈受阻導致硬體交付延遲，而遠距辦公的興起則加劇了農民對雲端分析平台的依賴。疫情後農業人手不足，推動了對自動化土壤監測系統的需求，以減少對人工取樣的依賴。政府針對農村數位基礎設施的經濟措施也擴大了感測器網路連接範圍。

在預測期內，解決方案領域預計將佔據最大的市場佔有率。

預計在預測期內，解決方案領域將佔據最大的市場佔有率，這主要得益於能夠將來自多個來源的土壤數據整合到整合分析儀表板中的綜合軟體平台。這些解決方案在採購決策中佔據主導地位，因為它們提供從資料擷取、模型推理到處方產生的一站式功能。領先的農業技術供應商不斷擴展其解決方案組合，利用基於區域土壤資料庫訓練的專有人工智慧模型。農業顧問和大型農場通常會選擇購買整合解決方案的許可，而不是自行組裝各個組件，這有助於在整個預測期內保持穩定的訂閱收入和平台用戶留存率。

預計在預測期內，基於雲端的細分市場將呈現最高的複合年成長率。

在整個預測期內，基於雲端的細分市場預計將呈現最高的成長率，這主要得益於可擴展的運算資源，這些資源能夠對大規模土壤資料集進行複雜的機器學習推理。雲端的採用不僅消除了對農場伺服器基礎設施的需求，還有助於實現模型的自動更新以及農業專家的遠端存取。領先的超大規模雲端服務供應商提供專為農業最佳化的、針對地理空間分析而最佳化的雲端執行個體。訂閱式定價模式降低了農場的初始投資負擔。主要司法管轄區正在推動對雲端儲存農業數據的法規核准，這加速了企業採用雲端技術，並鼓勵供應商投資於平台開發。

市佔率最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率，這得益於其先進的精密農業基礎設施和數位農業技術的高普及率。美國憑藉其眾多大型農業技術公司和廣泛的商業化農業運營，正在推動該地區的需求成長。大量創業投資的湧入正在加速土壤分析新創企業的創新。包括美國農業部「氣候智慧舉措」在內的政府計畫正在創造機構需求。農村地區寬頻基礎設施的改善正在支持感測器網路連接，從而在整個預測期內鞏固北美的市場領導地位。

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，這主要得益於新興經濟體商業農業部門的擴張以及政府主導的數位化項目。中國和印度憑藉大規模的農業人口和日益增強的土壤劣化意識，引領該地區的成長。兩國的智慧農業舉措正在資助部署覆蓋數百萬公頃的感測器網路。本地技術與全球分析服務提供者的合作正在加速知識轉移。對糧食安全的日益關注正在推動全部區域對數據驅動型土壤管理方法的投資。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
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    • 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

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：全球人工智慧土壤健康分析市場：按組件分類

  • 解決方案
  • 服務
  • 軟體平台

第6章：全球人工智慧土壤健康分析市場：依部署模式分類

  • 基於雲端的
  • 現場
  • 混合

第7章 全球人工智慧土壤健康分析市場：按技術分類

  • 人工智慧
  • 機器學習
  • 遙感探測
  • 物聯網感測器
  • 地理資訊系統與地理空間分析
  • 巨量資料分析
  • 預測分析

第8章：全球人工智慧土壤健康分析市場：按應用分類

  • 土壤養分分析
  • 土壤濕度監測
  • 土壤碳評估
  • 土壤肥力圖
  • 作物適宜性分析
  • 土壤鹽度監測
  • 永續土地管理

第9章：全球人工智慧土壤健康分析市場：按最終用戶分類

  • 商業農場
  • 農業合作社
  • 農業顧問
  • 政府機構
  • 研究機構
  • 農業技術公司
  • 其他最終用戶

第10章：全球人工智慧土壤健康分析市場：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

  • CropX Technologies
  • John Deere
  • Trimble Inc.
  • Topcon Corporation
  • AGCO Corporation
  • Raven Industries
  • Hexagon AB
  • IBM Corporation
  • Microsoft Corporation
  • Climate LLC
  • Planet Labs
  • Sentera
  • Taranis
  • Corteva Agriscience
  • Syngenta Group
  • Bayer AG
Product Code: SMRC39033

According to Stratistics MRC, the Global AI-Powered Soil Health Analytics Market is accounted for $0.3 billion in 2026 and is expected to reach $1.9 billion by 2034 growing at a CAGR of 25.0% during the forecast period. AI-powered soil health analytics refer to advanced computational systems that integrate artificial intelligence with soil sensor networks and remote sensing data to assess, monitor, and predict soil conditions. These platforms employ machine learning algorithms to analyze chemical, biological, and physical soil parameters including nutrient content, moisture levels, organic carbon, and microbial activity. They are deployed through cloud-based software platforms, mobile applications, and edge computing devices connected to IoT sensor arrays. The systems generate actionable insights for precision agriculture by identifying soil degradation patterns, optimizing fertilizer application, and forecasting crop suitability across diverse agricultural landscapes.

Market Dynamics:

Driver:

Precision Agriculture Adoption

AI-powered soil health analytics platforms are experiencing accelerating demand as global agriculture intensifies focus on sustainable input optimization and yield maximization. Farmers are increasingly deploying sensor networks and satellite imagery to transition from calendar-based to data-driven soil management practices. Government subsidies for climate-smart agriculture and carbon sequestration monitoring create institutional procurement channels. Major agritech companies are integrating soil analytics into comprehensive farm management platforms. These converging trends generate sustained investment in artificial intelligence models trained on regional soil databases.

Restraint:

Sensor Infrastructure Costs

The substantial capital investment required for dense soil sensor networks represents a significant barrier to widespread adoption among smallholder farmers. Each monitoring point requires installation of moisture probes, nutrient sensors, and telemetry equipment, which elevates per-hectare deployment costs beyond the reach of many agricultural operations. Data connectivity limitations in rural areas constrain real-time transmission capabilities. Additionally, calibration requirements across diverse soil types and climatic zones complicate standardization. These economic and technical constraints limit market penetration primarily to large commercial farms and research institutions.

Opportunity:

Carbon Credit Verification

The emergence of voluntary and compliance carbon markets is creating substantial opportunities for AI soil health analytics in quantifying soil organic carbon sequestration. Agricultural technology platforms are developing machine learning models capable of predicting carbon accumulation rates across different tillage and cover crop regimes. Partnerships between analytics providers and carbon credit registries streamline the verification pipeline for farm-level carbon offsets. Government climate initiatives in North America and Europe increasingly mandate soil carbon monitoring. As carbon pricing mechanisms mature, the addressable market for soil health measurement expands beyond agronomy into environmental finance.

Threat:

Open Source Model Disruption

The proliferation of open-source geospatial analytics tools and publicly available satellite soil moisture datasets poses competitive pressure on proprietary AI soil health platforms. Research institutions and government agencies are releasing freely accessible soil spectral libraries and machine learning models that reduce dependency on commercial solutions. These public alternatives benefit from continuous academic refinement and lack subscription fees. Major agricultural cooperatives are developing in-house analytics capabilities using open-source frameworks. As freely available tools improve accuracy, the willingness of farmers to pay premium prices for proprietary platforms may decline.

Covid-19 Impact:

The COVID-19 pandemic disrupted field sensor installation schedules while accelerating digital transformation across agricultural operations. Supply chain constraints delayed hardware deliveries, yet remote work protocols increased farmer reliance on cloud-based analytics dashboards. Post-pandemic, sustained labor shortages in agriculture have reinforced demand for automated soil monitoring systems that reduce dependence on manual sampling. Government stimulus packages targeting rural digital infrastructure have expanded connectivity for sensor networks.

The solutions segment is expected to be the largest during the forecast period

The solutions segment is expected to account for the largest market share during the forecast period, due to comprehensive software platforms that integrate multi-source soil data into unified analytics dashboards. These solutions dominate procurement decisions because they provide end-to-end capabilities spanning data ingestion, model inference, and prescription generation. Major agritech vendors continue to expand their solution portfolios with proprietary AI models trained on regional soil databases. Agricultural consultants and enterprise farms routinely license integrated solutions rather than assembling discrete components, which reinforces consistent subscription revenue and platform stickiness throughout the forecast period.

The cloud-based segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the cloud-based segment is predicted to witness the highest growth rate, driven by scalable compute resources enabling complex machine learning inference on large soil datasets. Cloud deployments eliminate the need for on-farm server infrastructure while facilitating automatic model updates and remote access for agronomists. Major hyperscale providers are offering agriculture-specific cloud instances optimized for geospatial analytics. Subscription pricing models reduce upfront capital requirements for farms. Regulatory acceptance of cloud-stored agricultural data across major jurisdictions accelerates enterprise adoption and stimulates vendor investment in platform development.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to advanced precision agriculture infrastructure and high adoption rates of digital farming technologies. The United States leads regional demand through concentration of major agritech companies and extensive commercial farming operations. Strong presence of venture capital funding accelerates innovation in soil analytics startups. Government programs including USDA climate-smart initiatives create institutional demand. Favorable broadband expansion in rural areas supports sensor network connectivity, reinforcing North American market leadership throughout the forecast period.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to expanding commercial farming sectors and government digitization programs across emerging economies. China and India drive regional growth through large agricultural populations and increasing awareness of soil degradation. National smart agriculture initiatives fund sensor network deployment across millions of hectares. Local technology partnerships with global analytics providers accelerate knowledge transfer. Rising food security concerns compel investment in data-driven soil management practices throughout the region.

Key players in the market

Some of the key players in AI-Powered Soil Health Analytics Market include CropX Inc., Agrivi d.o.o., Farmers Edge Inc., Teralytic Inc., Cropin Technology Solutions Private Limited, AgroCares B.V., Soil Scout Oy, Sentek Technologies Pty Ltd., Acclima Inc., and Monnit Corporation.

Key Developments:

In June 2026, CropX Inc. launched a next-generation soil health analytics platform integrating satellite imagery with ground sensor networks for real-time carbon sequestration quantification across diverse agricultural soil types and climatic zones.

In June 2026, Agrivi d.o.o. expanded its artificial intelligence soil modeling capabilities with machine learning algorithms predicting nutrient leaching patterns for optimized fertilizer scheduling in large-scale row crop operations across Europe.

In April 2026, Farmers Edge Inc. secured a strategic partnership with a major carbon credit registry to provide automated soil organic carbon verification using its proprietary AI analytics platform for compliance offset programs.

Components Covered:

  • Solutions
  • Services
  • Software Platforms

Deployment Modes Covered:

  • Cloud-Based
  • On-Premise
  • Hybrid

Technologies Covered:

  • Artificial Intelligence
  • Machine Learning
  • Remote Sensing
  • IoT Sensors
  • GIS & Geospatial Analytics
  • Big Data Analytics
  • Predictive Analytics

Applications Covered:

  • Soil Nutrient Analysis
  • Soil Moisture Monitoring
  • Soil Carbon Assessment
  • Soil Fertility Mapping
  • Crop Suitability Analysis
  • Soil Salinity Monitoring
  • Sustainable Land Management

End Users Covered:

  • Commercial Farms
  • Agricultural Cooperatives
  • Agricultural Consultants
  • Government Agencies
  • Research Institutes
  • AgriTech Companies
  • 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-Powered Soil Health Analytics Market, By Component

  • 5.1 Solutions
  • 5.2 Services
  • 5.3 Software Platforms

6 Global AI-Powered Soil Health Analytics Market, By Deployment Mode

  • 6.1 Cloud-Based
  • 6.2 On-Premise
  • 6.3 Hybrid

7 Global AI-Powered Soil Health Analytics Market, By Technology

  • 7.1 Artificial Intelligence
  • 7.2 Machine Learning
  • 7.3 Remote Sensing
  • 7.4 IoT Sensors
  • 7.5 GIS & Geospatial Analytics
  • 7.6 Big Data Analytics
  • 7.7 Predictive Analytics

8 Global AI-Powered Soil Health Analytics Market, By Application

  • 8.1 Soil Nutrient Analysis
  • 8.2 Soil Moisture Monitoring
  • 8.3 Soil Carbon Assessment
  • 8.4 Soil Fertility Mapping
  • 8.5 Crop Suitability Analysis
  • 8.6 Soil Salinity Monitoring
  • 8.7 Sustainable Land Management

9 Global AI-Powered Soil Health Analytics Market, By End User

  • 9.1 Commercial Farms
  • 9.2 Agricultural Cooperatives
  • 9.3 Agricultural Consultants
  • 9.4 Government Agencies
  • 9.5 Research Institutes
  • 9.6 AgriTech Companies
  • 9.7 Other End Users

10 Global AI-Powered Soil Health Analytics 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 CropX Technologies
  • 13.2 John Deere
  • 13.3 Trimble Inc.
  • 13.4 Topcon Corporation
  • 13.5 AGCO Corporation
  • 13.6 Raven Industries
  • 13.7 Hexagon AB
  • 13.8 IBM Corporation
  • 13.9 Microsoft Corporation
  • 13.10 Climate LLC
  • 13.11 Planet Labs
  • 13.12 Sentera
  • 13.13 Taranis
  • 13.14 Corteva Agriscience
  • 13.15 Syngenta Group
  • 13.16 Bayer AG

List of Tables

  • Table 1 Global AI-Powered Soil Health Analytics Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Powered Soil Health Analytics Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI-Powered Soil Health Analytics Market Outlook, By Solutions (2023-2034) ($MN)
  • Table 4 Global AI-Powered Soil Health Analytics Market Outlook, By Services (2023-2034) ($MN)
  • Table 5 Global AI-Powered Soil Health Analytics Market Outlook, By Software Platforms (2023-2034) ($MN)
  • Table 6 Global AI-Powered Soil Health Analytics Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 7 Global AI-Powered Soil Health Analytics Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 8 Global AI-Powered Soil Health Analytics Market Outlook, By On-Premise (2023-2034) ($MN)
  • Table 9 Global AI-Powered Soil Health Analytics Market Outlook, By Hybrid (2023-2034) ($MN)
  • Table 10 Global AI-Powered Soil Health Analytics Market Outlook, By Technology (2023-2034) ($MN)
  • Table 11 Global AI-Powered Soil Health Analytics Market Outlook, By Artificial Intelligence (2023-2034) ($MN)
  • Table 12 Global AI-Powered Soil Health Analytics Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 13 Global AI-Powered Soil Health Analytics Market Outlook, By Remote Sensing (2023-2034) ($MN)
  • Table 14 Global AI-Powered Soil Health Analytics Market Outlook, By IoT Sensors (2023-2034) ($MN)
  • Table 15 Global AI-Powered Soil Health Analytics Market Outlook, By GIS & Geospatial Analytics (2023-2034) ($MN)
  • Table 16 Global AI-Powered Soil Health Analytics Market Outlook, By Big Data Analytics (2023-2034) ($MN)
  • Table 17 Global AI-Powered Soil Health Analytics Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 18 Global AI-Powered Soil Health Analytics Market Outlook, By Application (2023-2034) ($MN)
  • Table 19 Global AI-Powered Soil Health Analytics Market Outlook, By Soil Nutrient Analysis (2023-2034) ($MN)
  • Table 20 Global AI-Powered Soil Health Analytics Market Outlook, By Soil Moisture Monitoring (2023-2034) ($MN)
  • Table 21 Global AI-Powered Soil Health Analytics Market Outlook, By Soil Carbon Assessment (2023-2034) ($MN)
  • Table 22 Global AI-Powered Soil Health Analytics Market Outlook, By Soil Fertility Mapping (2023-2034) ($MN)
  • Table 23 Global AI-Powered Soil Health Analytics Market Outlook, By Crop Suitability Analysis (2023-2034) ($MN)
  • Table 24 Global AI-Powered Soil Health Analytics Market Outlook, By Soil Salinity Monitoring (2023-2034) ($MN)
  • Table 25 Global AI-Powered Soil Health Analytics Market Outlook, By Sustainable Land Management (2023-2034) ($MN)
  • Table 26 Global AI-Powered Soil Health Analytics Market Outlook, By End User (2023-2034) ($MN)
  • Table 27 Global AI-Powered Soil Health Analytics Market Outlook, By Commercial Farms (2023-2034) ($MN)
  • Table 28 Global AI-Powered Soil Health Analytics Market Outlook, By Agricultural Cooperatives (2023-2034) ($MN)
  • Table 29 Global AI-Powered Soil Health Analytics Market Outlook, By Agricultural Consultants (2023-2034) ($MN)
  • Table 30 Global AI-Powered Soil Health Analytics Market Outlook, By Government Agencies (2023-2034) ($MN)
  • Table 31 Global AI-Powered Soil Health Analytics Market Outlook, By Research Institutes (2023-2034) ($MN)
  • Table 32 Global AI-Powered Soil Health Analytics Market Outlook, By AgriTech Companies (2023-2034) ($MN)
  • Table 33 Global AI-Powered Soil Health Analytics 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.