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1755926

2032 年作物產量預測市場機器學習預測：按組件、部署模型、農場規模、最終用戶和地區進行的全球分析

Machine Learning for Crop Yield Prediction Market Forecasts to 2032 - Global Analysis By Component (Software and Service), Deployment Model (Cloud-based and On-premises), Farm Size, 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，全球作物產量預測機器學習市場預計在 2025 年將達到 9.0056 億美元，到 2032 年將達到 41.7542 億美元，預測期內的複合年成長率為 24.5%。

作物產量預測機器學習利用先進的演算法分析大量農業數據，例如天氣模式、土壤特性、衛星影像和作物歷史產量，從而產生準確的作物產量預測。此外，機器學習可以幫助農民和農學家做出數據驅動的決策，最大限度地利用資源，並透過發現傳統模型所忽略的複雜模式和關係來提高糧食生產效率。這些預測模型可以隨著時間的推移進行調整，隨著新數據的出現而變得更加準確，最終在氣候變遷和全球需求不斷成長的情況下，支持永續的農業實踐和糧食安全。

據印度農業研究理事會（ICAR）稱，LASSO-SVR等混合機器學習模型在預測印度各地小麥產量方面表現出很高的準確度，在帕蒂亞拉，歸一化均方根誤差（nRMSE）值低至0.6%。

人口成長導致糧食需求增加

隨著世界人口接近100億，預計到2050年，糧食需求將增加60-70%。農業面臨巨大的壓力，需要在不增加耕地的情況下提高作物產量。透過準確預測作物產量，機器學習可以大大幫助農民實現產量最大化，並採取主動措施減少損失。此外，相關人員可以透過及時預測來規劃配送、物流和倉儲，從而提高糧食供應和價格穩定性。

高品質在地化資料的可用性有限。

準確的機器學習預測需要大量高品質、多樣化且針對當地情況的數據，包括土壤成分、作物類型、種植計劃、病蟲害發生以及當前天氣狀況。在許多地方，尤其是在開發中國家，此類詳細資訊難以取得、已過時或未記錄。此外，農村地區衛星和無人機數據的解析度和頻率可能較低，從而影響模型準確性。如果沒有可靠的資料輸入，機器學習演算法就無法充分發揮其潛力，這限制了其在產量預測中的應用。

結合衛星和遙感探測技術

由於美國國家航空暨太空總署 (NASA)、歐洲太空總署 (ESA) 以及 Planet 和空中巴士等私人公司在遙感探測和衛星影像方面的進步，作物監測正變得越來越準確和頻繁。機器學習演算法可以處理這些海量資料集，從而識別作物脅迫、生長模式以及病蟲害侵染的早期徵兆。此外，透過將機器學習與衛星資料結合，可以實現在廣闊而多樣化的地區進行準確且可擴展的產量預測。隨著高解析度影像擷取途徑的不斷改善，機器學習在農業預測中的機會將不斷擴大。

科技公司對數據的壟斷

小型新興企業和無力承擔昂貴數據合約或專有平台的本地參與企業感到，大型跨國科技公司日益佔據關鍵農業數據（例如衛星圖像、天氣預報和農場分析）的主導地位，對他們構成了威脅。這導致了壟斷環境的形成，創新依賴於少數「安全隔離網閘」，小型或本地機器學習服務供應商難以競爭，甚至難以生存。此外，少數公司對農業數據的過度控制可能會限制開放獲取，降低透明度，並阻礙技術收益在農民和公共機構之間的公平分配，最終減緩機器學習在作物產量預測中的廣泛應用。

COVID-19的影響

新冠疫情顯著加速了機器學習在作物產量預測中的應用，因為供應鏈中斷和勞動力短缺凸顯了對更精準、更自動化的農業管理工具的需求。由於糧食生產的不確定性日益增加，以及進入田地的途徑受限，農民和相關企業轉向數據驅動技術，以最大限度地利用資源並更好地預測產量。然而，這也存在一些弊端，包括技術採用緩慢、部分地區研發支出減少以及資料收集程序中斷。此外，新冠疫情推動了整個市場走向更深層的數位轉型，凸顯了韌性十足、技術驅動的農業系統至關重要。

預計預測期內雲端基礎的細分市場將佔比最大

預計在預測期內，雲端基礎將佔據最大的市場佔有率。在現代農業技術領域，雲端基礎的解決方案是優於傳統本地系統的首選方案，因為它們能夠實現即時數據處理、遠端監控以及與物聯網設備的整合，從而提高預測準確性和決策能力。此外，雲端服務促進了各相關人員之間的協作，並支援持續更新和改進。這些平台使農民和相關企業無需進行大量的領先基礎設施投資即可獲得強大的分析和機器學習模型。

預計在預測期內，研究機構部門的複合年成長率最高。

預計研究機構領域將在預測期內達到最高成長率。政府和私營機構正在大力投資農業研發，這推動了這一成長。例如，專注於人工智慧和機器學習在農業領域應用的國家跨學科資訊物理系統計畫已獲得印度政府366億印度盧比的資助。旁遮普農業大學和BITS-Pilani等機構之間的夥伴關係也正在尋求將機器人、人工智慧、無人機和物聯網感測器應用於農業，以提高永續性和永續性。此外，這些努力凸顯了研究機構在開發用於作物產量預測的機器學習應用方面的重要性。

比最大的地區

預計北美地區將在預測期內佔據最大的市場佔有率。這種優勢歸功於該地區從氣象站、物聯網感測器和衛星影像大規模收集農業數據，這些數據顯著提高了機器學習模型的準確性。此外，公共和私營部門的大量投資，包括美國政府在農業人工智慧技術方面高達 2 億美元的投資，正在加速數據主導農業實踐和精密農業的發展。綜合起來，這些因素使北美在採用和應用機器學習技術進行作物產量預測方面處於領先地位。

複合年成長率最高的地區

預計亞太地區在預測期內的複合年成長率最高。中國和印度等國政府正在大力投資農業技術，以改善糧食安全和永續性，這推動了這一成長。例如，印度的數位農業計畫和中國20層樓高的人工智慧垂直農場，都顯示該地區對將人工智慧應用於農業的熱情。此外，這些計畫正在激發創新，加速該地區機器學習技術的採用，從而增強作物產量預測能力。

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  • 公司簡介
    • 對其他市場參與企業（最多 3 家公司）進行全面分析
    • 主要企業的SWOT分析（最多3家公司）
  • 地理細分
    • 根據客戶興趣對主要國家進行市場估計、預測和複合年成長率（註：基於可行性檢查）
  • 競爭基準化分析
    • 透過產品系列、地理分佈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 前言

  • 概述
  • 相關利益者
  • 研究範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 二手研究資料
    • 先決條件

第3章市場走勢分析

  • 介紹
  • 驅動程式
  • 限制因素
  • 機會
  • 威脅
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

第5章 全球作物產量預測機器學習市場（依組成部分）

  • 介紹
  • 軟體
    • 預測分析軟體
    • 人工智慧作物監測軟體
    • 天氣和氣候數據分析軟體
    • 遙感探測和衛星影像軟體
    • 農場管理軟體
  • 服務
    • 諮詢和顧問服務
    • 實施和整合服務
    • 培訓和支援服務
    • 數據分析和客製化建模服務
    • 雲端基礎的農業AI服務

6. 全球作物產量預測機器學習市場（依部署模式）

  • 介紹
  • 雲端基礎
  • 本地

7. 全球作物產量預測機器學習市場（依農場規模）

  • 介紹
  • 小的
  • 中等的
  • 大的

8. 全球作物產量預測機器學習市場（依最終用戶）

  • 介紹
  • 農業工人
  • 農業合作社
  • 研究所
  • 政府
  • 其他

9. 全球作物產量預測機器學習市場（按地區）

  • 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
    • 其他歐洲國家
  • 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10章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第11章 公司概況

  • BASF SE
  • International Business Machines（IBM）
  • Keymakr Inc.
  • Microsoft Azure
  • Raven Industries Inc.
  • FarmWise Labs Inc.
  • Bayer AG
  • Agrograph Inc.
  • Ceres Imaging Inc.
  • Aerobotics Ltd.
  • Cropin Technology Solutions Pvt. Ltd.
  • Sentera Inc.
  • Trace Genomics Inc.
  • Xyonix Inc
  • Corteva Inc
  • AgriWebb Pty Ltd
  • CropX Inc.
  • IUNU Inc.
  • Terramera Inc.
Product Code: SMRC29820

According to Stratistics MRC, the Global Machine Learning for Crop Yield Prediction Market is accounted for $900.56 million in 2025 and is expected to reach $4175.42 million by 2032 growing at a CAGR of 24.5% during the forecast period. Machine learning for crop yield prediction leverages advanced algorithms to analyze large volumes of agricultural data-such as weather patterns, soil properties, satellite imagery, and historical crop yields-to generate accurate forecasts of crop productivity. Moreover, farmers and agronomists can make data-driven decisions, maximize resource use, and improve the efficiency of food production by using machine learning to find intricate patterns and relationships that traditional models might miss. Despite climate variability and rising global demand, these predictive models can adjust over time, becoming more accurate as new data becomes available, and eventually support sustainable farming methods and food security.

According to the Indian Council of Agricultural Research (ICAR), hybrid machine learning models, such as LASSO-SVR, have demonstrated high accuracy in predicting wheat yields across various Indian regions, with normalized Root Mean Square Error (nRMSE) values as low as 0.6% in Patiala.

Market Dynamics:

Driver:

Increasing food demand as a result of population growth

The demand for food is expected to increase by 60-70% by 2050 as the world's population approaches 10 billion. The agricultural industry is under tremendous pressure to increase crop yields without increasing the amount of arable land. By precisely forecasting crop yields, machine learning can be extremely helpful in enabling farmers to take preventative action to maximize output and reduce losses. Additionally, stakeholders can improve food availability and price stability by planning for distribution, logistics, and storage with the help of timely predictions.

Restraint:

Restricted availability of localized and high-quality data

Large amounts of high-quality, varied, and localized data-such as soil composition, crop type, planting schedules, pest incidence, and current weather conditions-are necessary for accurate machine learning predictions. In many places, particularly developing nations, such detailed information is unobtainable, out-of-date, or inconsistently documented. Furthermore, the accuracy of the model may also be impacted by the lack of resolution or frequency of satellite and drone data in rural areas. ML algorithms cannot function at their best without trustworthy data inputs, which restricts their applicability in yield forecasting.

Opportunity:

Combining satellite and remote sensing technologies

The precision and frequency of crop monitoring has increased due to advances in remote sensing and satellite imaging, such as those from NASA, ESA (European Space Agency), and private companies like Planet and Airbus. ML algorithms can process these large datasets to identify crop stress, growth patterns, and early signs of pest or disease outbreaks. Moreover, accurate and scalable yield forecasts across large and diverse geographies are made possible by the integration of ML with satellite data, and the opportunities for ML in agricultural forecasting will only grow as access to high-resolution imagery continues to improve.

Threat:

Monopolization of data by tech companies

Smaller startups and local players who cannot afford costly data subscriptions or proprietary platforms are threatened by the increasing dominance of large multinational technology firms over access to key agricultural data, such as satellite imagery, weather feeds, and farm analytics. This leads to a monopolistic environment where innovation becomes dependent on a few gatekeepers, making it difficult for smaller or regional ML service providers to compete or even survive. Additionally, excessive control over agricultural data by a few corporations may limit open access, reduce transparency, and impede the equitable distribution of technological benefits to farmers and public institutions, ultimately slowing down the spread of ML for crop yield prediction.

Covid-19 Impact:

The COVID-19 pandemic significantly accelerated the adoption of machine learning for crop yield prediction as disruptions in supply chains and labor shortages highlighted the need for more precise and automated agricultural management tools. Amidst the heightened uncertainty in food production and restricted field access, farmers and agribusinesses resorted to data-driven technologies in order to maximize resource utilization and more accurately predict yields. But there were drawbacks as well, like slower technology adoption, less money for R&D in some areas, and disruptions in data collection procedures. Furthermore, the market was pushed toward greater digital transformation overall by COVID-19, which also highlighted the vital significance of resilient, technologically enabled agricultural systems.

The cloud-based segment is expected to be the largest during the forecast period

The cloud-based segment is expected to account for the largest market share during the forecast period. In contemporary agricultural technology landscapes, cloud-based solutions are the preferred option over traditional on-premises systems because they enable real-time data processing, remote monitoring, and integration with IoT devices, improving predictive accuracy and decision-making. Additionally, cloud services facilitate collaboration across various stakeholders and enable continuous updates and improvements. These platforms enable farmers and agribusinesses to access powerful analytics and machine learning models without the need for significant upfront infrastructure investment.

The research institutions segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the research institutions segment is predicted to witness the highest growth rate. Governments and private organizations have made significant investments in agricultural research and development, which is driving this growth. For example, the National Mission on Interdisciplinary Cyber-Physical Systems, which focuses on AI and ML applications in agriculture, has received ₹3,660 crore from the Indian government. In order to improve productivity and sustainability, partnerships between organizations like Punjab Agricultural University and BITS-Pilani also seek to incorporate robotics, AI, drones, and Internet of Things sensors into agriculture. Moreover, the importance of research institutions in developing machine learning applications for crop yield prediction is highlighted by these initiatives.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share. This dominance is explained by the region's large-scale agricultural data collection from weather stations, IoT sensors, and satellite imagery, all of which greatly improve machine learning model accuracy. Furthermore, significant public and private sector investments-including a noteworthy $200 million investment by the US government in AI technology for agriculture-have accelerated the development of data-driven agricultural practices and precision farming. North America is positioned as a leader in the adoption and application of machine learning technologies for crop yield prediction due to these factors taken together.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Governments in nations like China and India are making large investments in agricultural technology in an effort to improve food security and sustainability, which is what is driving this growth. India's Digital Agriculture Mission and China's unveiling of a 20-story AI-powered vertical farm, for example, demonstrate the region's dedication to incorporating AI into agriculture. Moreover, these programs are promoting innovation, speeding up the region's adoption of machine learning technologies, and enhancing crop yield forecasts.

Key players in the market

Some of the key players in Machine Learning for Crop Yield Prediction Market include BASF SE, International Business Machines (IBM), Keymakr Inc., Microsoft Azure, Raven Industries Inc., FarmWise Labs Inc., Bayer AG, Agrograph Inc., Ceres Imaging Inc., Aerobotics Ltd., Cropin Technology Solutions Pvt. Ltd., Sentera Inc., Trace Genomics Inc., Xyonix Inc, Corteva Inc, AgriWebb Pty Ltd, CropX Inc., IUNU Inc. and Terramera Inc.

Key Developments:

In May 2025, Tech Company IBM and Deutsche Bank DB have expanded their long-term partnership with a new agreement that gives Deutsche Bank more access to IBM's wide range of software tools. This includes IBM's automation software, hybrid cloud services, and its watsonx artificial intelligence (AI) platform. Deutsche Bank will also get the latest version of IBM Storage Protect, which will improve how the bank protects and manages its data.

In April 2025, BASF and the University of Toronto have signed a Master Research Agreement (MRA) to streamline innovation projects and increase collaboration between BASF and Canadian researchers. This partnership is part of a regional strategy to extend BASF's collaboration with universities in North America into Canada. This is a great achievement for BASF, as it marks the company's first MRA with a Canadian university.

In September 2024, FarmWiseTM and RDO Equipment Co., a dealer of intelligently connected agriculture, construction, environmental, irrigation, positioning, and surveying equipment from leading manufacturers, including John Deere, announce an exclusive partnership to deliver FarmWise's Vulcan precision weeding and cultivation implement to vegetable growers in the Southwest regions of the United States.

Components Covered:

  • Software
  • Service

Deployment Models Covered:

  • Cloud-based
  • On-premises

Farm Sizes Covered:

  • Small
  • Medium
  • Large

End Users Covered:

  • Farmers
  • Agricultural Cooperatives
  • Research Institutions
  • Government Agencies
  • 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 End User Analysis
  • 3.7 Emerging Markets
  • 3.8 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 Machine Learning for Crop Yield Prediction Market, By Component

  • 5.1 Introduction
  • 5.2 Software
    • 5.2.1 Predictive Analytics Software
    • 5.2.2 AI-Powered Crop Monitoring Software
    • 5.2.3 Weather and Climate Data Analytics Software
    • 5.2.4 Remote Sensing and Satellite Imaging Software
    • 5.2.5 Farm Management Software
  • 5.3 Service
    • 5.3.1 Consulting and Advisory Services
    • 5.3.2 Implementation and Integration Services
    • 5.3.3 Training and Support Services
    • 5.3.4 Data Analytics and Custom Modeling Services
    • 5.3.5 Cloud-Based Agricultural AI Services

6 Global Machine Learning for Crop Yield Prediction Market, By Deployment Model

  • 6.1 Introduction
  • 6.2 Cloud-based
  • 6.3 On-premises

7 Global Machine Learning for Crop Yield Prediction Market, By Farm Size

  • 7.1 Introduction
  • 7.2 Small
  • 7.3 Medium
  • 7.4 Large

8 Global Machine Learning for Crop Yield Prediction Market, By End User

  • 8.1 Introduction
  • 8.2 Farmers
  • 8.3 Agricultural Cooperatives
  • 8.4 Research Institutions
  • 8.5 Government Agencies
  • 8.6 Other End Users

9 Global Machine Learning for Crop Yield Prediction Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 BASF SE
  • 11.2 International Business Machines (IBM)
  • 11.3 Keymakr Inc.
  • 11.4 Microsoft Azure
  • 11.5 Raven Industries Inc.
  • 11.6 FarmWise Labs Inc.
  • 11.7 Bayer AG
  • 11.8 Agrograph Inc.
  • 11.9 Ceres Imaging Inc.
  • 11.10 Aerobotics Ltd.
  • 11.11 Cropin Technology Solutions Pvt. Ltd.
  • 11.12 Sentera Inc.
  • 11.13 Trace Genomics Inc.
  • 11.14 Xyonix Inc
  • 11.15 Corteva Inc
  • 11.16 AgriWebb Pty Ltd
  • 11.17 CropX Inc.
  • 11.18 IUNU Inc.
  • 11.19 Terramera Inc.

List of Tables

  • Table 1 Global Machine Learning for Crop Yield Prediction Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Machine Learning for Crop Yield Prediction Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Machine Learning for Crop Yield Prediction Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global Machine Learning for Crop Yield Prediction Market Outlook, By Predictive Analytics Software (2024-2032) ($MN)
  • Table 5 Global Machine Learning for Crop Yield Prediction Market Outlook, By AI-Powered Crop Monitoring Software (2024-2032) ($MN)
  • Table 6 Global Machine Learning for Crop Yield Prediction Market Outlook, By Weather and Climate Data Analytics Software (2024-2032) ($MN)
  • Table 7 Global Machine Learning for Crop Yield Prediction Market Outlook, By Remote Sensing and Satellite Imaging Software (2024-2032) ($MN)
  • Table 8 Global Machine Learning for Crop Yield Prediction Market Outlook, By Farm Management Software (2024-2032) ($MN)
  • Table 9 Global Machine Learning for Crop Yield Prediction Market Outlook, By Service (2024-2032) ($MN)
  • Table 10 Global Machine Learning for Crop Yield Prediction Market Outlook, By Consulting and Advisory Services (2024-2032) ($MN)
  • Table 11 Global Machine Learning for Crop Yield Prediction Market Outlook, By Implementation and Integration Services (2024-2032) ($MN)
  • Table 12 Global Machine Learning for Crop Yield Prediction Market Outlook, By Training and Support Services (2024-2032) ($MN)
  • Table 13 Global Machine Learning for Crop Yield Prediction Market Outlook, By Data Analytics and Custom Modeling Services (2024-2032) ($MN)
  • Table 14 Global Machine Learning for Crop Yield Prediction Market Outlook, By Cloud-Based Agricultural AI Services (2024-2032) ($MN)
  • Table 15 Global Machine Learning for Crop Yield Prediction Market Outlook, By Deployment Model (2024-2032) ($MN)
  • Table 16 Global Machine Learning for Crop Yield Prediction Market Outlook, By Cloud-based (2024-2032) ($MN)
  • Table 17 Global Machine Learning for Crop Yield Prediction Market Outlook, By On-premises (2024-2032) ($MN)
  • Table 18 Global Machine Learning for Crop Yield Prediction Market Outlook, By Farm Size (2024-2032) ($MN)
  • Table 19 Global Machine Learning for Crop Yield Prediction Market Outlook, By Small (2024-2032) ($MN)
  • Table 20 Global Machine Learning for Crop Yield Prediction Market Outlook, By Medium (2024-2032) ($MN)
  • Table 21 Global Machine Learning for Crop Yield Prediction Market Outlook, By Large (2024-2032) ($MN)
  • Table 22 Global Machine Learning for Crop Yield Prediction Market Outlook, By End User (2024-2032) ($MN)
  • Table 23 Global Machine Learning for Crop Yield Prediction Market Outlook, By Farmers (2024-2032) ($MN)
  • Table 24 Global Machine Learning for Crop Yield Prediction Market Outlook, By Agricultural Cooperatives (2024-2032) ($MN)
  • Table 25 Global Machine Learning for Crop Yield Prediction Market Outlook, By Research Institutions (2024-2032) ($MN)
  • Table 26 Global Machine Learning for Crop Yield Prediction Market Outlook, By Government Agencies (2024-2032) ($MN)
  • Table 27 Global Machine Learning for Crop Yield Prediction 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.