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Report
Machine Learning for Crop Yield Prediction Market, Opportunity, Growth Drivers, Industry Trend Analysis and Forecast, 2024-2032
Market Report I 2024-08-08 I 240 Pages I Global Market Insights
Machine Learning for Crop Yield Prediction Market size will expand at over 26.5% CAGR between 2024 and 2032, propelled by the shift towards precision agriculture and data-driven farming practices, combined with advancements in AI technology.
Precision agriculture leverages detailed data to refine farming practices, while cutting-edge AI algorithms boost the precision of crop yield forecasts. This collaboration not only sharpens predictions but also enhances the management of agricultural resources. Farmers increasingly embrace these technologies to bolster yields and respond to evolving conditions, positioning the market for considerable expansion.
For example, in April 2024, ClimateAi launched ClimateLens Monitor Yield Outlook, an AI-centric platform that delivers weekly crop yield forecasts and insights into climate influences on major commodities such as corn, soybeans, and wheat. This platform aids in foreseeing and mitigating supply risks stemming from severe weather. The platform's prowess in foreseeing and mitigating supply risks due to severe weather underscores the growing dependence on sophisticated machine learning technologies to elevate agricultural productivity.
The machine learning for crop yield prediction industry is segmented based on component, deployment model, farm size, end user, and region.
In terms of farm size, the large farm segment is set for remarkable growth by 2032. Major farming operations reap substantial benefits from cutting-edge machine learning technologies, leveraging data insights to optimize crop yields. These tools enable better prediction models, efficient resource management, and improved decision-making. The scalability and integration capabilities of machine learning tools make them indispensable for large agricultural enterprises aiming to enhance productivity and profitability.
The market share of research institution end-users will witness a considerable rise by 2032, attributed to their pivotal role in advancing agricultural technology. These institutions drive innovation by developing and testing new machine learning models that improve crop yield predictions. Their extensive research efforts, collaborations with technology providers, and access to cutting-edge resources contribute to their dominance in the market.
Asia Pacific machine learning for crop yield prediction market will register a notable share through 2024-2032, owing to its diverse agricultural landscape and rapid adoption of advanced technologies. The emphasis on precision agriculture and data-driven decision-making drives the demand for machine learning solutions. With substantial investments in agricultural technology and increasing awareness of the benefits of data analytics, APAC is at the forefront of this market. The region's dynamic growth and innovation make it a vital contributor to global market expansion.
Report Content
Chapter 1 Methodology and Scope
1.1 Research design
1.1.1 Research approach
1.1.2 Data collection methods
1.2 Base estimates and calculations
1.2.1 Base year calculation
1.2.2 Key trends for market estimation
1.3 Forecast model
1.4 Primary research and validation
1.4.1 Primary sources
1.4.2 Data mining sources
1.5 Market scope and definition
Chapter 2 Executive Summary
2.1 Industry 360 synopsis, 2021 - 2032
Chapter 3 Industry Insights
3.1 Industry ecosystem analysis
3.2 Supplier landscape
3.2.1 Software providers
3.2.2 Hardware providers
3.2.3 Service provider
3.2.4 System integrators
3.2.5 End-user
3.3 Profit margin analysis
3.4 Technology and innovation landscape
3.5 Patent analysis
3.6 Key news and initiatives
3.7 Regulatory landscape
3.8 Impact forces
3.8.1 Growth drivers
3.8.1.1 Growth in agritech startups
3.8.1.2 High accuracy provided by machine learning models
3.8.1.3 Integration of precision agriculture tools in the agriculture industry
3.8.1.4 Rapid technological investments by prominent players
3.8.2 Industry pitfalls and challenges
3.8.2.1 Data quality and availability challenges
3.8.2.2 High computational requirements of ML models
3.9 Growth potential analysis
3.10 Porter's analysis
3.11 PESTEL analysis
Chapter 4 Competitive Landscape, 2023
4.1 Introduction
4.2 Company market share analysis
4.3 Competitive positioning matrix
4.4 Strategic outlook matrix
Chapter 5 Market Estimates and Forecast, By Component, 2021 - 2032 ($Bn)
5.1 Key trends
5.2 Software
5.2.1 Predictive modelling software
5.2.2 Data analytics platform
5.2.3 Others
5.3 Services
5.3.1 Professional
5.3.2 Managed
Chapter 6 Market Estimates and Forecast, By Deployment Model, 2021 - 2032 ($Bn)
6.1 Key trends
6.2 Cloud-based
6.3 On-premises
Chapter 7 Market Estimates and Forecast, By Farm Size, 2021 - 2032 ($Bn)
7.1 Key trends
7.2 Small
7.3 Medium
7.4 Large
Chapter 8 Market Estimates and Forecast, By End User, 2021 - 2032 ($Bn)
8.1 Key trends
8.2 Farmers
8.3 Agricultural cooperatives
8.4 Research institutions
8.5 Government agencies
8.6 Others
Chapter 9 Market Estimates and Forecast, By Region, 2021 - 2032 ($Bn)
9.1 Key trends
9.2 North America
9.2.1 U.S.
9.2.2 Canada
9.3 Europe
9.3.1 UK
9.3.2 Germany
9.3.3 France
9.3.4 Italy
9.3.5 Spain
9.3.6 Russia
9.3.7 Nordics
9.3.8 Rest of Europe
9.4 Asia Pacific
9.4.1 China
9.4.2 India
9.4.3 Japan
9.4.4 South Korea
9.4.5 ANZ
9.4.6 Southeast Asia
9.4.7 Rest of Asia Pacific
9.5 Latin America
9.5.1 Brazil
9.5.2 Mexico
9.5.3 Argentina
9.5.4 Rest of Latin America
9.6 MEA
9.6.1 South Africa
9.6.2 Saudi Arabia
9.6.3 UAE
9.6.4 Rest of MEA
Chapter 10 Company Profiles
10.1 Ag Leader Technology
10.2 Blue River Technology (John Deere)
10.3 Ceres Imaging
10.4 Corteva
10.5 Cropin Technology Solutions Pvt. Ltd.
10.6 Descartes Labs Inc.
10.7 Farmers Edge Inc.
10.8 FlyPard Analytics GmbH.
10.9 Lindsay Corporation
10.10 Microsft Azure Farmbeats
10.11 OneSoil
10.12 Planet Labs PBC
10.13 SAP
10.14 Taranis
10.15 Trimble, Inc.
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