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2035438

人工智慧驅動的產量預測平台市場預測至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 Yield Forecasting Platforms Market Forecasts to 2034 - Global Analysis By Component (Software Platforms, Hardware Integration and Services), Deployment Mode, Technology, Application, End User and By Geography

出版日期: 2026年05月11日 | 出版商:

Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的數據，全球人工智慧驅動的收益預測平台市場預計將在 2026 年達到 28 億美元，在預測期內以 10.8% 的複合年成長率成長，到 2034 年達到 64 億美元。

一個基於人工智慧的產量預測平台，利用機器學習模型，結合歷史生產記錄、衛星影像、氣象數據、土壤健康參數和作物生長監測數據，產生商品穀物、油籽、水果、蔬菜和特種作物生產系統的田間和區域產量預測。與傳統的農業產量估算方法相比，該平台具有更高的預測精度，能夠幫助農民做出明智的銷售決策，幫助商品交易商管理風險，幫助食品公司規劃採購，並幫助政府制定糧食安全政策。

對農產品風險管理的需求

糧食貿易、食品製造和農業金融領域需要準確的作物產量預估訊息，以便進行商品採購避險、評估信用風險以及進行供應規劃投資。這催生了對人工智慧驅動的產量預測平台的巨大商業性需求，這些平台能夠提供比傳統政府作物調查更早、空間定位更精準的產量預測。隨著氣候變遷導致作物產量波動加劇商品價格風險，全球食品供應鏈智慧生態系統中的企業和機構正積極投資，以提高產量預測的準確性。

基於實際測量數據的檢驗要求

檢驗基於人工智慧的產量預測模型的準確性需要大量經GPS收割機監測校準的、具有地理參考的歷史產量數據，但這給數據獲取帶來了障礙。尤其是在發展中農業市場和小規模農業系統中，產量監測設備的普及程度不足以產生訓練可靠的、區域性人工智慧模型所需的高密度歷史真實資料集。因此，人工智慧產量預測的商業性應用僅限於已開發農業市場中已建立精準產量監測基礎設施的大型商業農場。

保險承保中的參數化整合

利用人工智慧驅動的產量預測作為觸發參數的參數化農業作物保險產品，無需現場勘察即可實現自動賠付，這為產量預測平台提供者帶來了巨大的市場機會。保險公司重視客觀的、基於人工智慧的產量偏差檢測，這種檢測方法在作物特定產量預測精度方面優於基於衛星植被指數的參數觸發方法，從而能夠為參數化農業保險項目提供更優的產品設計和定價方案。

與政府對作物產量的估算結果競爭

諸如美國農業部國家農業統計局（NASS）、歐盟作物監測系統以及提供免費公共作物產量預測的國家計畫等成熟的政府農業統計機構，對商業人工智慧產量預測平台構成了市場定位挑戰。為了向市場情報預算有限的農業市場參與企業證明商業訂閱費用的合理性，這些平台必須在預測準確性、及時性或空間解析度方面展現出遠超政府免費估算的性能。

新冠疫情的影響：

新冠疫情造成的供應鏈中斷和對糧食安全的擔憂，加劇了機構投資者對精準農業生產預測的需求，以支持糧食政策和供應管理決策，從而促使政府和私營食品行業相關人員加大對人工智慧作物產量預測技術的投資。疫情後，對糧食安全和氣候變遷導致的大宗商品市場波動的關注，持續推動農業市場各領域參與企業對先進人工智慧產量預測平台功能的商業性需求。

在預測期內，服務業預計將佔據最大佔有率。

預計在預測期內，服務領域將佔據最大的市場佔有率。這主要歸功於公司和機構廣泛採用人工智慧遙感探測技術，並透過管理服務合約進行推廣。這些服務包括提供區域性和作物特定的客製化預測、農藝解讀以及策略決策支援諮詢。對於農產品貿易公司、食品生產商和政府機構而言，這些服務至關重要，它們能夠幫助企業將人工智慧預測結果轉化為可執行的市場洞察，而無需企業自行開發人工智慧或具備遙感資料處理方面的專業知識。

在預測期內，基於雲端的細分市場預計將呈現最高的複合年成長率。

在預測期內，基於雲端的細分市場預計將呈現最高的成長率。這是因為農業市場的參與企業更傾向於使用雲端交付的產量預測平台，這些平台能夠透過統一的控制面板實現多區域、多作物的產量監測和投資組合管理；此外，基於全球匯總訓練數據的雲端平台能夠持續改進模型，從而提高預測精度並擴大地理覆蓋範圍。相較之下，本地部署系統僅限於本地訓練的模型，無法整合全球農業資料。

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。這是因為美國擁有全球商業性程度最高的農業人工智慧預測市場，Descartes Labs、Climate LLC 和 Taranis 等領先的平台公司在北美地區從包括糧食貿易、食品製造和農場管理在內的客戶群體中獲得了可觀的收入。此外，美國大宗商品交易產業根深蒂固地投資於先進的市場資訊系統，這為訂閱高級預測平台提供了支持。

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。這主要歸功於中國、印度和東南亞國家對糧食安全監測基礎設施的大量投資；快速擴張的商業農業部門需要生產風險管理資訊；以及各國政府的農業規劃項目要求提高區域產量預測的準確性。所有這些因素都在推動亞太地區農業政策制定者和商業市場參與企業對機構人工智慧預測平台的需求。

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
- 對主要公司進行SWOT分析（最多3家公司）
區域細分
- 應客戶要求，我們提供主要國家的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
競爭性標竿分析
- 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。

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

IBM Corporation
Microsoft Corporation
Google LLC
Amazon Web Services Inc.
Trimble Inc.
Deere & Company
Corteva Agriscience
Bayer AG
Syngenta Group
Climate LLC（Bayer）
Granular Inc.
Taranis
Descartes Labs
Prospera Technologies
AgEagle Aerial Systems
Planet Labs PBC
CropX Technologies

簡介目錄圖表

Product Code: SMRC35644

According to Stratistics MRC, the Global AI-Powered Yield Forecasting Platforms Market is accounted for $2.8 billion in 2026 and is expected to reach $6.4 billion by 2034 growing at a CAGR of 10.8% during the forecast period. AI-powered yield forecasting platforms refer to cloud-based and on-premise software systems and integration services that apply machine learning models trained on historical production records, satellite imagery, weather data, soil health parameters, and crop growth monitoring inputs to generate field-level and regional crop yield predictions across commercial grain, oilseed, fruit, vegetable, and specialty crop production systems, enabling farmer marketing decisions, commodity trader risk management, food company procurement planning, and government food security policy planning with superior predictive accuracy compared to conventional agronomic yield estimation methods.

Market Dynamics:

Driver:

Agricultural Commodity Risk Management Demand

Grain trading, food manufacturing, and agricultural finance sectors requiring accurate advance crop yield intelligence for commodity procurement hedging, credit risk assessment, and supply planning investment are generating substantial commercial demand for AI yield forecasting platforms providing earlier and more spatially precise yield prediction than conventional government crop condition surveys. Climate change crop production volatility amplifying commodity price risk is intensifying commercial and institutional yield forecasting accuracy investment across the global food supply chain intelligence ecosystem.

Restraint:

Ground Truth Data Validation Requirements

AI yield forecasting model accuracy validation requiring extensive georeferenced historical yield data with calibrated GPS-enabled harvester monitors creates data availability barriers particularly in developing agricultural markets and smallholder farming systems where yield monitoring hardware penetration is insufficient to generate the dense historical ground truth datasets needed for reliable regional AI model training, limiting AI yield forecasting commercial deployment to larger commercial farming operations in developed agricultural markets with established precision yield monitoring infrastructure.

Opportunity:

Insurance Underwriting Parametric Integration

Agricultural crop insurance parametric product development using AI yield forecasting outputs as trigger parameters for automatic indemnity payment without claims adjustment field inspection represents a premium market opportunity for yield forecasting platform providers as insurance underwriters value objective AI-based yield deviation detection exceeding satellite vegetation index-based parametric triggers in crop-specific yield prediction accuracy, enabling superior product design and pricing for parametric agricultural insurance programs.

Threat:

Government Crop Estimate Competition

Well-established government agricultural statistical agency crop production estimate publication programs including USDA NASS, EU crop monitoring, and national programs providing free public crop yield forecasts create market positioning challenges for commercial AI yield forecasting platforms that must demonstrate materially superior prediction accuracy, timeliness, or spatial resolution relative to free government estimates to justify commercial subscription fees for agricultural market participants operating with constrained market intelligence budgets.

Covid-19 Impact:

COVID-19 supply chain disruptions and food security concerns amplifying institutional demand for accurate agricultural production forecasting to inform food policy and supply management decisions generated increased investment in AI crop yield prediction technology from both government and commercial food industry stakeholders. Post-pandemic food security investment elevation and commodity market volatility driven by climate disruptions continue sustaining commercial demand for sophisticated AI yield forecasting platform capability across diverse agricultural market participant segments.

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

The services segment is expected to account for the largest market share during the forecast period, due to dominant enterprise and institutional adoption of AI yield forecasting through managed service subscriptions providing custom regional and crop-specific forecast delivery, agronomic interpretation, and strategic decision support consultation that agricultural trading houses, food manufacturers, and government agencies require to translate AI forecast outputs into actionable market intelligence without requiring internal AI development and remote sensing data processing expertise.

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 agricultural market participant preference for cloud-delivered yield forecasting platform access enabling multi-region and multi-crop yield monitoring portfolio management through unified dashboards, combined with cloud platform continuous model improvement from aggregated global training data delivering superior prediction accuracy and expanding geographic coverage compared to on-premise systems limited to locally trained models without global agricultural data integration capability.

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 hosting the world's most commercially mature AI agricultural forecasting market with leading platform companies including Descartes Labs, Climate LLC, and Taranis generating substantial North American revenue from grain trading, food manufacturing, and farm management customer segments, combined with the US commodity trading sector's deep investment culture in sophisticated market intelligence systems supporting premium forecasting platform subscription.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to China, India, and Southeast Asian countries investing heavily in food security monitoring infrastructure, rapidly expanding commercial agriculture sectors requiring production risk management intelligence, and government agricultural planning programs demanding improved regional yield prediction accuracy generating institutional AI forecasting platform procurement across Asia Pacific agricultural policy and commercial market participant segments.

Key players in the market

Some of the key players in AI-Powered Yield Forecasting Platforms Market include IBM Corporation, Microsoft Corporation, Google LLC, Amazon Web Services Inc., Trimble Inc., Deere & Company, Corteva Agriscience, Bayer AG, Syngenta Group, Climate LLC (Bayer), Granular Inc., Taranis, Descartes Labs, Prospera Technologies, AgEagle Aerial Systems, Planet Labs PBC, and CropX Technologies.

Key Developments:

In March 2026, Descartes Labs launched a global multi-crop AI yield forecasting platform providing 90-day advance county-level yield prediction across corn, soybean, and wheat production with documented mean absolute error improvement of 40 percent versus USDA estimates.

In February 2026, Planet Labs PBC introduced a daily satellite imagery-based crop yield monitoring subscription providing real-time canopy development tracking and AI yield model updates throughout the growing season for commercial grain trading and food procurement clients.

In December 2025, Climate LLC (Bayer) secured a major food company supply planning contract providing field-level US corn and soybean yield forecasting integrated with supply chain planning systems for 90-day procurement strategy optimization.

Components Covered:

Software Platforms
Hardware Integration
Services

Deployment Modes Covered:

Cloud-Based
On-Premise

Technologies Covered:

Machine Learning
Predictive Analytics
Remote Sensing
Big Data Analytics

Applications Covered:

Crop Yield Prediction
Weather Impact Analysis
Soil Data Analytics
Resource Optimization

End Users Covered:

Farmers
Agribusinesses
Government Agencies
Financial Institutions

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

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 Yield Forecasting Platforms Market, By Component

5.1 Software Platforms
5.2 Hardware Integration
5.3 Services

6 Global AI-Powered Yield Forecasting Platforms Market, By Deployment Mode

6.1 Cloud-Based
6.2 On-Premise

7 Global AI-Powered Yield Forecasting Platforms Market, By Technology

7.1 Machine Learning
7.2 Predictive Analytics
7.3 Remote Sensing
7.4 Big Data Analytics

8 Global AI-Powered Yield Forecasting Platforms Market, By Application

8.1 Crop Yield Prediction
8.2 Weather Impact Analysis
8.3 Soil Data Analytics
8.4 Resource Optimization

9 Global AI-Powered Yield Forecasting Platforms Market, By End User

9.1 Farmers
9.2 Agribusinesses
9.3 Government Agencies
9.4 Financial Institutions

10 Global AI-Powered Yield Forecasting Platforms 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 IBM Corporation
13.2 Microsoft Corporation
13.3 Google LLC
13.4 Amazon Web Services Inc.
13.5 Trimble Inc.
13.6 Deere & Company
13.7 Corteva Agriscience
13.8 Bayer AG
13.9 Syngenta Group
13.10 Climate LLC (Bayer)
13.11 Granular Inc.
13.12 Taranis
13.13 Descartes Labs
13.14 Prospera Technologies
13.15 AgEagle Aerial Systems
13.16 Planet Labs PBC
13.17 CropX Technologies

簡介目錄圖表

List of Tables

Table 1 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Region (2023-2034) ($MN)
Table 2 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Component (2023-2034) ($MN)
Table 3 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Software Platforms (2023-2034) ($MN)
Table 4 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Hardware Integration (2023-2034) ($MN)
Table 5 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Services (2023-2034) ($MN)
Table 6 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Deployment Mode (2023-2034) ($MN)
Table 7 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Cloud-Based (2023-2034) ($MN)
Table 8 Global AI-Powered Yield Forecasting Platforms Market Outlook, By On-Premise (2023-2034) ($MN)
Table 9 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Technology (2023-2034) ($MN)
Table 10 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Machine Learning (2023-2034) ($MN)
Table 11 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Predictive Analytics (2023-2034) ($MN)
Table 12 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Remote Sensing (2023-2034) ($MN)
Table 13 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Big Data Analytics (2023-2034) ($MN)
Table 14 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Application (2023-2034) ($MN)
Table 15 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Crop Yield Prediction (2023-2034) ($MN)
Table 16 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Weather Impact Analysis (2023-2034) ($MN)
Table 17 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Soil Data Analytics (2023-2034) ($MN)
Table 18 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Resource Optimization (2023-2034) ($MN)
Table 19 Global AI-Powered Yield Forecasting Platforms Market Outlook, By End User (2023-2034) ($MN)
Table 20 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Farmers (2023-2034) ($MN)
Table 21 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Agribusinesses (2023-2034) ($MN)
Table 22 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Government Agencies (2023-2034) ($MN)
Table 23 Global AI-Powered Yield Forecasting Platforms Market Outlook, By Financial Institutions (2023-2034) ($MN)