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市场调查报告书
商品编码
1570931

作物产量预测市场机器学习、机会、成长动力、产业趋势分析与预测，2024-2032

Machine Learning for Crop Yield Prediction Market, Opportunity, Growth Drivers, Industry Trend Analysis and Forecast, 2024-2032
出版日期: | 出版商: Global Market Insights Inc. | 英文 240 Pages | 商品交期: 2-3个工作天内

价格
简介目录

2023 年，机器学习资料产量预测市场规模为 5.81 亿美元，预计 2024 年至 2032 年复合年增长率为 26.5%。高解析度多光谱卫星影像和无人机可以提供有关作物健康、土壤状况和环境变量的详细见解，从而提高机器学习 (ML) 模型的准确性。整合这些资料来源可以提高模型的可靠性，使农业部门受益匪浅。

农业科技新创公司处于农业创新的前沿，创建先进的机器学习演算法来预测作物产量。这些新创公司利用大型资料集（包括天气模式、土壤特征和作物健康）来开发更准确、更可靠的预测模型。他们快速采用尖端机器学习技术和获取最新技术的能力使他们能够提供高效的解决方案，从而改善农业流程并支援永续农业实践。这有助于农民和全球社区的粮食安全和经济稳定。

市场依组件分为软体和服务。 2023 年，软体部门占了很大份额，价值约 4.13 亿美元。这些软体解决方案变得至关重要，因为它们与物联网设备和巨量资料平台无缝集成，能够实现即时资料收集和分析，从而提高产量预测的精确度。对精准农业的日益关注正在推动对能够处理复杂数据集并产生可行见解的复杂软体的需求。因此，软体开发商正在生产更先进和用户友好的产品，这将继续推动市场成长。

根据部署模型，市场分为基于云端的解决方案和本地解决方案。到2032 年，基于云端的细分市场预计将超过32 亿美元。至关重要。此外，基于云端的解决方案减少了对硬体和基础设施的大量前期投资的需求。用户可以根据资源使用订阅或付费，这对许多组织来说是一种经济的选择。云端平台还可以从任何位置轻鬆存取机器学习工具和资料集，从而促进研究人员、农民和农业科技公司之间的协作。这种可访问性增强了工作流程，促进了见解和创新的交流，从而在作物产量预测领域做出更好的决策。

2023年，北美在作物产量预测机器学习市场处于领先地位，约占41%的市占率。该地区受益于来自卫星图像、物联网感测器和气象站的大量农业资料。如此丰富的资料提高了机器学习模型的准确性，从而实现更精确的作物产量预测。此外，公共和私营部门对人工智慧和机器学习技术的投资正在推动创新农业解决方案的发展。

亚太地区各国政府也透过旨在提高生产力和永续性的资金、补贴和政策来鼓励农业创新。这些努力正在加速先进农业技术的采用，促进更有效率、更有弹性的农业实践的发展。透过利用人工智慧和机器学习，该地区正在应对其独特的农业挑战，提高作物产量，并确保长期粮食安全和环境永续性。

目录

第 1 章：方法与范围

第 2 章：执行摘要

第 3 章：产业洞察

  • 产业生态系统分析
  • 供应商格局
    • 软体供应商
    • 硬体提供者
    • 服务商
    • 系统整合商
    • 终端用户
  • 利润率分析
  • 技术和创新格局
  • 专利分析
  • 重要新闻和倡议
  • 监管环境
  • 衝击力
    • 成长动力
      • 农业科技新创企业的成长
      • 机器学习模型提供的高精度
      • 精准农业工具在农业产业的整合
      • 知名企业的快速技术投资
    • 产业陷阱与挑战
      • 数据品质和可用性挑战
      • 机器学习模型的计算要求高
  • 成长潜力分析
  • 波特的分析
  • PESTEL分析

第 4 章：竞争格局

  • 介绍
  • 公司市占率分析
  • 竞争定位矩阵
  • 战略展望矩阵

第 5 章：市场估计与预测：按组成部分，2021 - 2032 年

  • 主要趋势
  • 软体
    • 预测建模软体
    • 数据分析平台
    • 其他的
  • 服务
    • 专业的
    • 託管

第 6 章：市场估计与预测：按部署模型，2021 - 2032 年

  • 主要趋势
  • 基于云端
  • 本地

第 7 章：市场估计与预测：按农场规模，2021 - 2032 年

  • 主要趋势
  • 小的
  • 中等的
  • 大的

第 8 章：市场估计与预测：按最终用户划分，2021 - 2032 年

  • 主要趋势
  • 农民
  • 农业合作社
  • 研究机构
  • 政府机构
  • 其他的

第 9 章：市场估计与预测：按地区，2021 - 2032

  • 主要趋势
  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 俄罗斯
    • 北欧人
    • 欧洲其他地区
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 韩国
    • 澳新银行
    • 东南亚
    • 亚太地区其他地区
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
    • 拉丁美洲其他地区
  • MEA
    • 南非
    • 沙乌地阿拉伯
    • 阿联酋
    • MEA 的其余部分

第 10 章：公司简介

  • Ag Leader Technology
  • Blue River Technology (John Deere)
  • Ceres Imaging
  • Corteva
  • Cropin Technology Solutions Pvt. Ltd.
  • Descartes Labs Inc.
  • Farmers Edge Inc.
  • FlyPard Analytics GmbH.
  • Lindsay Corporation
  • Microsoft Azure Farmbeats
  • OneSoil
  • Planet Labs PBC
  • SAP
  • Taranis
  • Trimble, Inc.
简介目录
Product Code: 10736

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.