作物產量預測市場機器學習、機會、成長動力、產業趨勢分析與預測，2024-2032

2023 年，機器學習資料產量預測市場規模為 5.81 億美元，預計 2024 年至 2032 年複合年成長率為 26.5%。高解析度多光譜衛星影像和無人機可以提供有關作物健康、土壤狀況和環境變量的詳細見解，從而提高機器學習 (ML) 模型的準確性。整合這些資料來源可以提高模型的可靠性，使農業部門受益匪淺。

農業科技新創公司處於農業創新的前沿，創建先進的機器學習演算法來預測作物產量。這些新創公司利用大型資料集（包括天氣模式、土壤特徵和作物健康）來開發更準確、更可靠的預測模型。他們快速採用尖端機器學習技術和獲取最新技術的能力使他們能夠提供高效的解決方案，從而改善農業流程並支援永續農業實踐。這有助於農民和全球社區的糧食安全和經濟穩定。

市場依組件分為軟體和服務。 2023 年，軟體部門佔了很大佔有率，價值約 4.13 億美元。這些軟體解決方案變得至關重要，因為它們與物聯網設備和巨量資料平台無縫整合，能夠實現即時資料收集和分析，從而提高產量預測的精確度。對精準農業的日益關注正在推動對能夠處理複雜資料集並產生可行見解的複雜軟體的需求。因此，軟體開發商正在生產更先進和方便用戶使用的產品，這將繼續推動市場成長。

根據部署模型，市場分為基於雲端的解決方案和本地解決方案。到2032 年，基於雲端的細分市場預計將超過32 億美元。至關重要。此外，基於雲端的解決方案減少了對硬體和基礎設施的大量前期投資的需求。用戶可以根據資源使用訂閱或付費，這對許多組織來說是一種經濟的選擇。雲端平台還可以從任何位置輕鬆存取機器學習工具和資料集，從而促進研究人員、農民和農業科技公司之間的協作。這種可訪問性增強了工作流程，促進了見解和創新的交流，從而在作物產量預測領域做出更好的決策。

2023年，北美在作物產量預測機器學習市場處於領先地位，約佔41%的市佔率。該地區受益於來自衛星圖像、物聯網感測器和氣象站的大量農業資料。如此豐富的資料提高了機器學習模型的準確性，從而實現更精確的作物產量預測。此外，公共和私營部門對人工智慧和機器學習技術的投資正在推動創新農業解決方案的發展。

亞太地區各國政府也透過旨在提高生產力和永續性的資金、補貼和政策來鼓勵農業創新。這些努力正在加速先進農業技術的採用，促進更有效率、更有彈性的農業實踐的發展。透過利用人工智慧和機器學習，該地區正在應對其獨特的農業挑戰，提高作物產量，並確保長期糧食安全和環境永續性。

目錄

第 1 章：方法與範圍

第 2 章：執行摘要

第 3 章：產業洞察

• 產業生態系統分析
• 供應商格局
  • 軟體供應商
  • 硬體提供者
  • 服務商
  • 系統整合商
  • 終端用戶
• 利潤率分析
• 技術和創新格局
• 專利分析
• 重要新聞和舉措
• 監管環境
• 衝擊力
  • 成長動力
    • 農業科技新創企業的成長
    • 機器學習模型提供的高精度
    • 精準農業工具在農業產業的整合
    • 知名企業的快速技術投資
  • 產業陷阱與挑戰
    • 數據品質和可用性挑戰
    • 機器學習模型的計算要求高
• 成長潛力分析
• 波特的分析
• PESTEL分析

第 4 章：競爭格局

• 介紹
• 公司市佔率分析
• 競爭定位矩陣
• 戰略展望矩陣

第 5 章：市場估計與預測：按組成部分，2021 - 2032 年

• 主要趨勢
• 軟體
  • 預測建模軟體
  • 數據分析平台
  • 其他
• 服務
  • 專業的
  • 託管

第 6 章：市場估計與預測：按部署模型，2021 - 2032 年

• 主要趨勢
• 基於雲端
• 本地

第 7 章：市場估計與預測：按農場規模，2021 - 2032 年

• 主要趨勢
• 小的
• 中等的
• 大的

第 8 章：市場估計與預測：按最終用戶分類，2021 - 2032 年

• 主要趨勢
• 農民
• 農業合作社
• 研究機構
• 政府機構
• 其他

第 9 章：市場估計與預測：按地區，2021 - 2032

• 主要趨勢
• 北美洲
  • 美國
  • 加拿大
• 歐洲
  • 英國
  • 德國
  • 法國
  • 義大利
  • 西班牙
  • 俄羅斯
  • 北歐人
  • 歐洲其他地區
• 亞太地區
  • 中國
  • 印度
  • 日本
  • 韓國
  • 澳新銀行
  • 東南亞
  • 亞太地區其他地區
• 拉丁美洲
  • 巴西
  • 墨西哥
  • 阿根廷
  • 拉丁美洲其他地區
• MEA
  • 南非
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國
  • MEA 的其餘部分

第 10 章：公司簡介

• 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.

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.