by
According to the Foreclaro Global Research, the
Global
Machine Learning for Crop Yield Prediction Market size was valued at USD 1154.2 million in 2024. The report “Global
Machine Learning for Crop Yield Prediction Market Segmentation By
Crop Type (Cereals (Wheat, Rice, Maize, etc.), Fruits & Vegetables, Oilseeds
& Pulses, Fiber Crops (Cotton, Jute), Others (Sugarcane, Tobacco, etc.)),
By Technology (Supervised Learning, Unsupervised Learning, Deep Learning, Reinforcement
Learning, Ensemble Learning), By Application (Yield Forecasting, Crop Health
Monitoring, Climate Impact Assessment, Precision Agriculture, Irrigation
Management, Resource Optimization), By Deployment Type (Cloud-Based, On-Premise,
Edge-Based (IoT-integrated)), By End Users (Agricultural Research Institutes, Government
& Policy Makers, Farmers & Growers, Agritech Companies, Agri-Insurance
Providers, Cooperatives & Agro-based Industries)- Industry Trends and Forecast to 2033” gives a detailed insight into
current market dynamics and provides analysis on future market growth.
The Machine Learning for Crop Yield Prediction
marketplace is reworking worldwide agriculture by allowing data-pushed,
precision-primarily based totally farming practices. Using algorithms that
manner massive datasets together with weather patterns, soil characteristics,
satellite tv for pc television for computer imagery, and incidental crop data, machine
learning (ML) models can as it should be forecast crop yields. These insights
empower farmers, agritech companies, and policymakers to optimize planting schedules,
manage irrigation and fertilization, and reduce crop loss due to pests or
climate variability. The integration of ML with IoT, drones, and far-off
sensing technology has increased adoption throughout evolved and rising
agricultural economies. Governments and research establishments are increasingly
helping AI-pushed farming tasks to improve food safety and sustainable
agricultural development. Moreover, the emergence of cloud-primarily based
totally structures and cellular applications has made ML gear extra accessible,
even to small and medium-scale farmers. As weather-demanding situations
intensify, system mastering gives an essential answer for enhancing
productivity, minimizing resource waste, and ensuring sure resilient global food
delivery chain.
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Global
Machine Learning for Crop Yield Prediction Market Report Highlights
· The
international marketplace is experiencing robust growth, pushed through growing
demand for precision agriculture, climate-resilient farming, and real-time
selection aid for farmers.
· Adoption
of supervised and deep learning knowledge of algorithms, coupled with IoT
sensors, satellite tv for pc imagery, and drone data, is revolutionizing yield
prediction accuracy.
· Cloud-primarily
based totally deployment models are main because of scalability, smooth data
integration, and cost-effectiveness, mainly amongst agritech startups and
cooperatives.
· Cereals
(wheat, maize, rice) and oilseeds are the dominant crop sorts leveraging
ML-primarily based totally prediction gear because of their large-scale
manufacturing and international food relevance.
· Countries
like India and China are hastily integrating ML in agriculture, supported
through authorities initiatives, tech infrastructure, and virtual literacy.
· Major
agencies, which include CropIn, IBM, Microsoft, The Yield, and Climate
Corporation, are investing in AI gear, regularly via strategic partnerships
with research institutions and governments.
Foreclaro
Global Research has segmented the Machine Learning for Crop Yield Prediction
Market report based on Crop Type, technology, Components, Functionality, End
User Industry and region:
Cereals (Wheat, Rice, Maize, etc.)
Fruits & Vegetables
Oilseeds & Pulses
Fiber Crops (Cotton, Jute)
Others (Sugarcane, Tobacco, etc.)),
Machine Learning for Crop Yield
Prediction Market, Technology Outlook (Revenue - USD Million, 2020 - 2033)
Supervised Learning
Unsupervised Learning
Deep Learning
Reinforcement Learning
Ensemble Learning
Machine Learning for Crop Yield
Prediction Market, Application Outlook (Revenue - USD Million, 2020 - 2033)
Yield Forecasting
Crop Health Monitoring
Climate Impact Assessment
Precision Agriculture
Irrigation Management
Resource Optimization
Machine Learning for Crop Yield
Prediction Market, Deployment Type Outlook (Revenue - USD Million, 2020 - 2033)
Cloud-Based
On-Premise
Edge-Based (IoT-integrated)
Machine Learning for Crop Yield
Prediction Market, End Users Outlook (Revenue - USD Million, 2020 - 2033)
Agricultural Research Institutes
Government & Policy Makers
Farmers & Growers
Agritech Companies
Agri-Insurance Providers
Cooperatives & Agro-based Industries
Machine Learning for Crop Yield
Prediction Market, Regional Outlook (Revenue - USD Million, 2020 - 2033)
North
America
·
United
States
·
Canada
·
Mexico
Europe
·
United
Kingdom
·
Germany
·
France
·
Spain
·
Italy
·
Rest
of Europe
Asia
Pacific
·
China
·
India
·
Japan
·
Australia
·
South
Korea
·
Rest
of Asia Pacific
Latin
America
·
Brazil
·
Argentina
·
Rest
of Latin America
Middle East
& Africa
·
Saudi
Arabia
·
South
Africa
·
Rest
of MEA
Global Machine Learning for Crop Yield Prediction Market
Key Players
·
Ag Leader Technology
·
Blue River Technology
·
Corteva
·
SAP
·
Microsoft Azure
·
Taranis
·
Ceres Imaging
·
Microsoft
·
IBM Corporation
·
Agro Scout
