작물 수량 예측용 기계학습 시장, 기회, 성장 촉진요인, 산업 동향 분석과 예측(2024-2032년)

The Machine Learning for Crop Yield Prediction Market stood at USD 581 million in 2023, with a projected growth at a CAGR of 26.5% from 2024 to 2032. This expansion is driven by improvements in data quality from satellite imagery and the enhanced precision of machine learning technologies. High-resolution multispectral satellite images and drones provide detailed insights into crop health, soil conditions, and environmental variables, boosting the accuracy of machine learning (ML) models. Integrating these data sources improves model reliability, benefiting the agriculture sector significantly.

Agritech startups are at the forefront of innovation in the agricultural industry, creating advanced ML algorithms to predict crop yields. These startups leverage large datasets-encompassing weather patterns, soil characteristics, and crop health-to develop more accurate and reliable prediction models. Their ability to quickly adopt cutting-edge machine learning techniques and access the latest technology positions them to deliver highly effective solutions, which improve agricultural processes and support sustainable farming practices. This contributes to food security and economic stability for farmers and global communities.

The market is segmented into software and services by component. In 2023, the software segment held a significant share, valued at approximately USD 413 million. These software solutions have become crucial as they integrate seamlessly with IoT devices and big data platforms, enabling real-time data collection and analysis to improve the precision of yield forecasts. The rising focus on precision agriculture is driving demand for sophisticated software capable of handling complex datasets and generating actionable insights. As a result, software developers are producing more advanced and user-friendly products, which will continue to fuel market growth.

Based on the deployment model, the market is divided into cloud-based and on-premises solutions. The cloud-based segment is expected to surpass USD 3.2 billion by 2032. Cloud platforms offer scalable resources, allowing users to modify computing power and storage as needed, which is essential for handling large datasets and complex models used in crop yield prediction. Additionally, cloud-based solutions reduce the need for significant upfront investments in hardware and infrastructure. Users can subscribe or pay based on resource usage, making this an economical choice for many organizations. Cloud platforms also offer easy access to ML tools and datasets from any location, fostering collaboration among researchers, farmers, and agritech companies. This accessibility enhances workflows and facilitates the exchange of insights and innovations, leading to better decision-making in the crop yield prediction sector.

In 2023, North America led the Machine Learning for Crop Yield Prediction market, accounting for approximately 41% of the market share. The region benefits from a wealth of agricultural data sourced from satellite imagery, IoT sensors, and meteorological stations. This abundance of data improves the accuracy of ML models, resulting in more precise crop yield predictions. Moreover, investments from both public and private sectors in AI and ML technologies are driving the development of innovative agricultural solutions.

Governments in the Asia-Pacific region are also encouraging agricultural innovation through funding, subsidies, and policies designed to improve productivity and sustainability. These efforts are accelerating the adoption of advanced agricultural technologies, fostering the development of more efficient and resilient farming practices. By leveraging AI and ML, the region is tackling its unique agricultural challenges, enhancing crop yields, and ensuring long-term food security and environmental sustainability.

Table of Contents

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 Microsoft Azure Farmbeats
• 10.11 OneSoil
• 10.12 Planet Labs PBC
• 10.13 SAP
• 10.14 Taranis
• 10.15 Trimble, Inc.