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
Global Machine Learning for Crop
Yield Prediction Market size was valued at USD 1154.2 million in 2024 and is expected to grow
at a CAGR of 21.5% during the forecast period of 2025 to 2033.
Global Machine Learning for Crop Yield Prediction Market
Overview
The Global Machine Learning for
Crop Yield Prediction Market is swiftly growing, driven by the growing want for
precision agriculture and sustainable farming practices. Machine learning
fashions examine big datasets from satellite tv for pc imagery, IoT sensors,
climate forecasts, and soil conditions to as it should be are predict crop
yields. These insights assist farmers in making knowledgeable choices on
irrigation, fertilization, and harvest timing, in the long run enhancing productivity
and decreasing aid waste. The marketplace advantages from improvements in AI,
massive data analytics, and cloud computing. Governments, agritech startups,
and big agricultural corporations are actively adopting ML equipment to improve
food protection and optimize farming practices worldwide.
Global Machine Learning for Crop Yield Prediction Market
Scope
|
Factors |
Description |
|
Years Considered |
·
Historical Period: 2020-2023 ·
Base Year: 2024 ·
Forecast Period: 2025-2033 |
|
Segments |
·
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 |
|
Countries Catered |
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 |
|
Key Companies |
·
Corteva ·
SAP ·
Taranis |
|
Market Trends |
·
Integration with Remote Sensing and IoT
Devices ·
Adoption of Deep Learning and Hybrid Models |
Global Machine Learning for Crop Yield Prediction Market
Dynamics
The Global Machine Learning for
Crop Yield Prediction Market is driven by the growing demand for precision
agriculture and the desire to enhance agricultural productivity amid changing
weather conditions. Machine learning (ML) permits the evaluation of complicated
datasets derived from satellite tv for pc imagery, IoT devices, climate
stations, and soil sensors to offer correct and well-timed yield predictions.
This technology allows farmers to optimize aid utilization, lessen crop loss,
and improve decision-making for planting, irrigation, and harvesting. One of
the number one driver is the growing international populace and the ensuing
stress to provide extra meals the usage of constrained arable land and water
resources. Additionally, the proliferation of less expensive sensors and
cloud-primarily based totally structures makes ML extra available to farmers
and agribusinesses.
Key developments consist of the
combination of ML with far-off sensing technologies, drone-primarily based
totally facts collection, and the use of deep mastering algorithms for sample
popularity and forecasting. Governments and agricultural studies establishments
are increasingly investing in AI-based agricultural projects to aid food
security. However, the marketplace faces restraints along with constrained access
to terrific localized facts, specifically in developing regions, and worries
approximately data privacy and ownership. Challenges additionally consist of
the excessive initial value of deployment, virtual illiteracy amongst farmers,
and inconsistent net connectivity in rural areas. Despite those issues, the
developing emphasis on weather-clever agriculture and growing partnerships
among era organizations and agri-stakeholders offer good-sized possibilities
for market growth in the coming years.
Global Machine Learning for
Crop Yield Prediction Market Segment Analysis
The Global Machine Learning for
Crop Yield Prediction Market is segmented throughout numerous categories,
reflecting the huge software and adaptableness of ML technology in cutting-edge
agriculture. Based on crop type, the marketplace consists of cereals (along
with wheat, rice, and maize), which dominate because of their worldwide
nutritional significance and large-scale cultivation. Fruits & greens
additionally shape a substantial segment, driven by the need for higher
disorder prediction and pleasant yield estimation. Oilseeds & pulses, fiber
plants like cotton and jute, and different plants, along with sugarcane and
tobacco, are increasingly adopting ML gear to optimize developing situations
and forecast yields accurately. By technology, the marketplace is segmented
into supervised learning, that's extensively used for predicting yields the use
of categorized ancient datasets; unsupervised learning, beneficial in figuring
out hidden styles in unlabelled facts; deep learning, such as neural networks
for high-dimensional photo and sensor facts analysis; reinforcement learning,
used for adaptive decision-making in dynamic farming environments; and ensemble
learning, which mixes a couple of fashions to enhance prediction accuracy and
decrease variance.
In terms of software, ML is used
in yield forecasting, crop fitness monitoring, weather effect assessment,
precision agriculture, irrigation management, and aid optimization. These
packages assist farmers in making knowledgeable decisions, improving productivity,
and decreasing losses due to unpredictable climate or pest outbreaks. By
deployment type, the marketplace is classified into cloud-primarily based
totally systems, providing scalability and remote accessibility; on-premise
solutions, desired for data-intensive environments; and edge-primarily based
totally deployment, in which IoT-incorporated structures permit real-time,
neighbourhood processing in farms with restricted net access. Among cease
users, agricultural studies institutes and authorities our bodies use ML for
policy-making and food safety planning. Farmers and growers undertake it for
everyday operational decisions, at the same time as agritech groups combine ML
into systems for industrial solutions. Agri-coverage carriers make use of predictive
analytics for hazard assessment, and cooperatives and agro-industries put in
force it to use to optimize delivery chains and manipulate sources efficiently.
This multi-dimensional segmentation underscores the transformative function of
ML in using the destiny of sustainable, data-driven agriculture.
Global Machine Learning for
Crop Yield Prediction Market Regional Analysis
The Global Machine Learning for
Crop Yield Prediction Market indicates robust near-term dynamics, with North
America main region because of superior agricultural infrastructure,
substantial use of precision farming technologies, and the robust presence of
tech giants like IBM and Microsoft. The United States performs a pivotal role, driven
by large-scale industrial farming and substantial funding in AI-pushed
agriculture. Europe follows closely, with international locations like Germany,
the Netherlands, and France selling clever farming via government-funded AI
projects and sustainable agriculture policies. The Asia-Pacific region is
witnessing the quickest growth, fuelled by the growing adoption of agritech
solutions in countries like India, China, and Australia. Government help for
virtual agriculture, mixed with big agricultural economies and growing cell
phone penetration amongst farmers, is driving demand. Meanwhile, the Middle
East & Africa vicinity is rising slowly, with adoption especially confined
to pilot tasks and research-led projects.
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
Recent Developments
In January 2025, IIT Indore
launched the AgriHub Centre of Excellence, bringing together academia,
industry, NGOs, and farmer cooperatives. Equipped with NVIDIA DGX hardware and
high-capacity storage, the hub supports at least 11 ML/deep learning
agricultural projects aimed at converting raw agri-data into actionable
insights for farmers.
In November 2024, The
Yield (Australia) & UTS Data Science Institute collaborated with UTS to
develop advanced ML-driven models for predicting optimal harvest timing in
vineyards across Australia and New Zealand. These models evaluate on-farm
sensor data and historical climate trends, allowing predictions up to every two
weeks or daily during harvest—for improved yield quality and timing.
Research Methodology
At Foreclaro Global Research, our
research methodology is firmly rooted in a comprehensive and systematic
approach to market research. We leverage a blend of reliable public and
proprietary data sources, including industry reports, government publications,
company filings, trade journals, investor presentations, and credible online
databases. Our analysts critically evaluate and triangulate information to
ensure accuracy, consistency, and depth of insights. We follow a top-down and
bottom-up data modelling framework to estimate market sizes and forecasts,
supplemented by competitive benchmarking and trend analysis. Each research
output is tailored to client needs, backed by transparent data validation
practices, and continuously refined to reflect dynamic market conditions.
