AI in Agriculture Market Trends, Analysis & Forecast, 2032
As per a recent research report, AI in Agriculture Market surpass USD 10 Bn by 2032.
The rapid growth and adoption of AI in agriculture can be attributed
to several key drivers. Firstly, the global population continues to rise,
putting immense pressure on the agriculture industry to produce more food. AI
technologies like precision farming and smart irrigation systems enable farmers
to optimize crop yields, minimize resource wastage, and make data-driven
decisions. By leveraging AI algorithms and machine learning, farmers can
analyze vast amounts of data related to soil health, weather patterns, and crop
characteristics, enhancing productivity and reducing costs.
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Secondly, the need for sustainable and environmentally friendly
practices has become a top priority. AI in agriculture enables the
implementation of precision agriculture techniques, which minimize the use of
water, pesticides, and fertilizers by precisely targeting their application.
This reduces the negative impact on the environment, lowers production costs,
and increases profitability. These factors are reshaping the agriculture
industry, promoting efficiency, sustainability, and profitability through the
adoption of AI-driven solutions.
The services segment will promote market growth through 2032. AI is
driving significant advancements in agriculture services, revolutionizing the
way farming is conducted. Through machine learning and data analytics, AI
enables the development of predictive models for crop management, pest control,
and yield optimization. This technology also enables the automation of various
agricultural tasks, including planting, harvesting, and monitoring, through the
use of robotics and autonomous vehicles.
The AI in agriculture industry is segmented into precision farming, crop
and soil monitoring, intelligent spraying, weather data and forecast, livestock
health monitoring, and industrial robot. The crop and soil monitoring segment
is expected to grow at a robust pace during 2023 and 2032. AI brings
significant improvements to crop and soil monitoring in agriculture. Through
the integration of sensors, drones, and satellite imagery, AI enables real-time
and accurate data collection on crop health, soil moisture, nutrient levels,
and pest infestations. AI algorithms analyze this data to identify patterns,
anomalies, and potential risks. With the aid of computer vision, AI can detect
and diagnose crop diseases and nutrient deficiencies, allowing for timely
interventions.
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Asia Pacific AI
in agriculture market size will expand considerably over 2023-2032,
Governments, research institutions, and industry players are collaborating to
drive innovation and technology adoption. Initiatives such as funding programs,
research grants, and policy support are encouraging the development and
deployment of AI solutions in agriculture. Countries like China, India, Japan,
and Australia are investing in AI-driven projects, including precision farming,
crop monitoring, and smart irrigation systems. Additionally, knowledge-sharing
platforms, conferences, and workshops are fostering awareness and knowledge
exchange among stakeholders. These collective efforts are propelling the growth
of the AI in agriculture market in the region.
Partial chapters of report table of contents
(TOC):
Chapter 2 Executive Summary
2.1 AI in agriculture
market 360ยบ synopsis, 2018 - 2032
2.2 Business trends
2.2.1 Total Addressable
Market (TAM), 2023- 2032
2.3 Regional trends
2.4 Component trends
2.5 Technology trends
2.6 Application trends
Chapter 3 AI In Agriculture Market
Industry Insights
3.1 Impact on COVID-19
impact
3.2 Russia-Ukraine war
impact
3.3 Industry ecosystem
analysis
3.4 Technology &
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 Supportive
initiatives by government
3.8.1.2 Need to deliver
sufficient amount of crops
3.8.1.3 Increasing
implementation of agriculture robots
3.8.1.4 Growing interest
towards precision farming
3.8.2 Industry pitfalls
& challenges
3.8.2.1 Lack of
understanding of the practical application of AI tools
3.9 Profit margin
analysis
3.10 Vendor matrix
analysis
3.11 Growth potential
analysis
3.12 Porter’s analysis
3.13 PESTEL analysis
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