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市场调查报告书
商品编码
1755926

2032 年作物产量预测市场机器学习预测：按组件、部署模型、农场规模、最终用户和地区进行的全球分析

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个工作天内

价格

根据 Stratistics MRC 的数据，全球作物产量预测机器学习市场预计在 2025 年将达到 9.0056 亿美元，到 2032 年将达到 41.7542 亿美元，预测期内的复合年增长率为 24.5%。

作物产量预测机器学习利用先进的演算法分析大量农业数据，例如天气模式、土壤特性、卫星影像和作物历史产量，从而产生准确的作物产量预测。此外，机器学习可以帮助农民和农学家做出数据驱动的决策，最大限度地利用资源，并透过发现传统模型所忽略的复杂模式和关係来提高粮食生产效率。这些预测模型可以随着时间的推移进行调整，随着新数据的出现而变得更加准确，最终在气候变迁和全球需求不断增长的情况下，支持永续的农业实践和粮食安全。

据印度农业研究理事会（ICAR）称，LASSO-SVR等混合机器学习模型在预测印度各地小麦产量方面表现出很高的准确度，在帕蒂亚拉，归一化均方根误差（nRMSE）值低至0.6%。

人口成长导致粮食需求增加

随着世界人口接近100亿，预计到2050年，粮食需求将增加60-70%。农业面临巨大的压力，需要在不增加耕地的情况下提高作物产量。透过准确预测作物产量，机器学习可以大大帮助农民实现产量最大化，并采取主动措施减少损失。此外，相关人员可以透过及时预测来规划配送、物流和仓储，从而提高粮食供应和价格稳定性。

高品质在地化资料的可用性有限。

准确的机器学习预测需要大量高品质、多样化且针对当地情况的数据，包括土壤成分、作物类型、种植计划、病虫害发生以及当前天气状况。在许多地方，尤其是在开发中国家，此类详细资讯难以取得、已过时或未记录。此外，农村地区卫星和无人机数据的分辨率和频率可能较低，从而影响模型准确性。如果没有可靠的资料输入，机器学习演算法就无法充分发挥其潜力，这限制了其在产量预测中的应用。

结合卫星和遥感探测技术

由于美国国家航空暨太空总署 (NASA)、欧洲太空总署 (ESA) 以及 Planet 和空中巴士等私人公司在遥感探测和卫星影像方面的进步，作物监测正变得越来越准确和频繁。机器学习演算法可以处理这些海量资料集，从而识别作物胁迫、生长模式以及病虫害侵染的早期征兆。此外，透过将机器学习与卫星资料结合，可以实现在广阔而多样化的地区进行准确且可扩展的产量预测。随着高解析度影像撷取途径的不断改善，机器学习在农业预测中的机会将不断扩大。

科技公司对数据的垄断

小型新兴企业和无力承担昂贵数据合约或专有平台的本地参与企业感到，大型跨国科技公司日益占据关键农业数据（例如卫星图像、天气预报和农场分析）的主导地位，对他们构成了威胁。这导致了垄断环境的形成，创新依赖于少数「安全隔离网闸」，小型或本地机器学习服务供应商难以竞争，甚至难以生存。此外，少数公司对农业数据的过度控制可能会限制开放获取，降低透明度，并阻碍技术收益在农民和公共机构之间的公平分配，最终减缓机器学习在作物产量预测中的广泛应用。

COVID-19的影响

新冠疫情显着加速了机器学习在作物产量预测中的应用，因为供应链中断和劳动力短缺凸显了对更精准、更自动化的农业管理工具的需求。由于粮食生产的不确定性日益增加，以及进入田地的途径受限，农民和相关企业转向数据驱动技术，以最大限度地利用资源并更好地预测产量。然而，这也存在一些弊端，包括技术采用缓慢、部分地区研发支出减少以及资料收集程序中断。此外，新冠疫情推动了整个市场走向更深层的数位转型，凸显了韧性十足、技术驱动的农业系统至关重要。

预计预测期内云端基础的细分市场将占比最大

预计在预测期内，云端基础将占据最大的市场占有率。在现代农业技术领域，云端基础的解决方案是优于传统本地系统的首选方案，因为它们能够实现即时数据处理、远端监控以及与物联网设备的集成，从而提高预测准确性和决策能力。此外，云端服务促进了各相关人员之间的协作，并支援持续更新和改进。这些平台使农民和相关企业无需进行大量的领先基础设施投资即可获得强大的分析和机器学习模型。

预计在预测期内，研究机构部门的复合年增长率最高。

预计研究机构领域将在预测期内达到最高成长率。政府和私营机构正在大力投资农业研发，这推动了这一成长。例如，专注于人工智慧和机器学习在农业领域应用的国家跨学科资讯物理系统计画已获得印度政府366亿印度卢比的资助。旁遮普农业大学和BITS-Pilani等机构之间的伙伴关係也正在寻求将机器人、人工智慧、无人机和物联网感测器应用于农业，以提高永续性和永续性。此外，这些努力凸显了研究机构在开髮用于作物产量预测的机器学习应用方面的重要性。

比最大的地区

预计北美地区将在预测期内占据最大的市场占有率。这种优势归功于该地区从气象站、物联网感测器和卫星影像大规模收集农业数据，这些数据显着提高了机器学习模型的准确性。此外，公共和私营部门的大量投资，包括美国政府在农业人工智慧技术方面高达 2 亿美元的投资，正在加速数据主导农业实践和精密农业的发展。综合起来，这些因素使北美在采用和应用机器学习技术进行作物产量预测方面处于领先地位。

复合年增长率最高的地区

预计亚太地区在预测期内的复合年增长率最高。中国和印度等国政府正在大力投资农业技术，以改善粮食安全和永续性，这推动了这一成长。例如，印度的数位农业计画和中国20层楼高的人工智慧垂直农场，都显示该地区对将人工智慧应用于农业的热情。此外，这些计画正在激发创新，加速该地区机器学习技术的采用，从而增强作物产量预测能力。

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  • 公司简介
    • 对其他市场参与企业（最多 3 家公司）进行全面分析
    • 主要企业的SWOT分析（最多3家公司）
  • 地理细分
    • 根据客户兴趣对主要国家进行市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 透过产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 研究范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 二手研究资料
    • 先决条件

第三章市场走势分析

  • 介绍
  • 驱动程式
  • 限制因素
  • 机会
  • 威胁
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

第五章 全球作物产量预测机器学习市场（依组成部分）

  • 介绍
  • 软体
    • 预测分析软体
    • 人工智慧作物监测软体
    • 天气和气候数据分析软体
    • 遥感探测和卫星影像软体
    • 农场管理软体
  • 服务
    • 咨询和顾问服务
    • 实施和整合服务
    • 培训和支援服务
    • 数据分析和客製化建模服务
    • 云端基础的农业AI服务

6. 全球作物产量预测机器学习市场（依部署模式）

  • 介绍
  • 云端基础
  • 本地

7. 全球作物产量预测机器学习市场（依农场规模）

  • 介绍
  • 小的
  • 中等的
  • 大的

8. 全球作物产量预测机器学习市场（依最终用户）

  • 介绍
  • 农业工人
  • 农业合作社
  • 研究所
  • 政府
  • 其他的

9. 全球作物产量预测机器学习市场（按地区）

  • 介绍
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十一章 公司概况

  • 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.