Dublin, Aug. 10, 2020 (GLOBE NEWSWIRE) -- The "Artificial Intelligence in Agriculture - Global Market Outlook (2018-2027)" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence in Agriculture market accounted for $0.65 billion in 2018 and is expected to reach $4.43 billion by 2027 growing at a CAGR of 23.7% during the forecast period. Some of the key factors propelling the market growth include increasing implementation of data generation through sensors and aerial images for crops, rising crop productivity through deep-learning technology, and government support for the adoption of modern agricultural techniques. However, high cost of gathering precise field data hampers the market growth.

Artificial intelligence (AI) is a program based technology, owing to which digital computers and robots can perform real-time tasks by using cognitive human intelligence such as speech recognition, visual perception, decision making etc. AI in agriculture is commonly used for precision farming, crop monitoring, crop analytics, etc. It has various applications ranging from rural automatons, computerized water system frameworks, facial acknowledgment, and driverless tractors. This artificial intelligence in agriculture applications is done in a relationship with an alternate sort of sensors, radars, GPS frameworks, other cutting-edge contraptions dependent on AI.

By application, the drone analytics segment is expected to grow at a significant rate during the forecast period, due to its extensive use for diagnosing and mapping to evaluate crop health and to make real-time decisions. Favourable government mandates for the use of drones in agriculture are also expected to fuel the growth of the drone analytics market. Moreover, increasing awareness among farm owners regarding the advantages of drone analytics associated with AI technology is expected to further fuel the growth of the AI in agriculture market.

On the basis of geography, The AI in agriculture market in Asia Pacific is expected to witness the significant growth during the forecast period, owing to the wide-scale adoption of AI technologies in agriculture farms. AI is increasingly applied in the agriculture sector in developing countries, such as India and China. The increasing adoption of deep learning and computer vision algorithm for agriculture applications is also expected to fuel the growth of the AI in agriculture market in the Asia Pacific region.

Some of the key players in artificial intelligence in agriculture market include Farmers Edge, Granular, Ageagle, Descartes Labs, Gamaya, Ec2Ce, Precision Hawk, IBM, Deere & Company, Microsoft, The Climate Corporation, Prospera, Taranis, Awhere, Right-To-Win, Vision Robotics, Autonomous Tractor Corporation, Tule Technologies, Resson, and Cropx Technologies.

Technologies Covered:

  • Computer Vision
  • Machine Learning & Deep Learning
  • Predictive Analytics

Components Covered:

  • Software
  • Hardware
  • Services
  • AI-as-a-Service

Applications Covered:

  • Crops, Fruits, Vegetables, and Other Plants
  • Facial Recognition
  • Agricultural Drones
  • Agriculture Robots
  • Livestock Monitoring
  • Driverless Tractors
  • Drone Analytics
  • Labor Management
  • Other Applications

Farming Types Covered:

  • Indoor Farming
  • Field Farming
  • Livestock Farming

Deployments Covered:

  • Hybrid
  • Cloud
  • On-premise

Regions Covered:

North America

  • US
  • Canada
  • Mexico


  • Germany
  • France
  • Italy
  • UK
  • Spain
  • Rest of Europe

Asia Pacific

  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • Rest of Asia Pacific

South America

  • Argentina
  • Brazil
  • Chile
  • Rest of South America

Middle East & Africa

  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2017, 2018, 2019, 2023 and 2027
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic analysis: Drivers and Constraints, Product/Technology Analysis, Porter's five forces analysis, SWOT analysis, etc.
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Key Topics Covered:

1 Executive Summary

2 Preface
2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
2.4.1 Data Mining
2.4.2 Data Analysis
2.4.3 Data Validation
2.4.4 Research Approach
2.5 Research Sources
2.5.1 Primary Research Sources
2.5.2 Secondary Research Sources
2.5.3 Assumptions

3 Market Trend Analysis
3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Technology Analysis
3.7 Application Analysis
3.8 Emerging Markets
3.9 Impact of Covid-19

4 Porters Five Force Analysis
4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry

5 Global Artificial Intelligence in Agriculture Market, By Technology
5.1 Introduction
5.2 Computer Vision
5.3 Machine Learning & Deep Learning
5.4 Predictive Analytics

6 Global Artificial Intelligence in Agriculture Market, By Component
6.1 Introduction
6.2 Software
6.2.1 AI Solution
6.2.2 AI Platform
6.3 Hardware
6.3.1 Storage Device
6.3.2 Processor
6.3.3 Network
6.4 Services
6.4.1 Support & Maintenance
6.4.2 Deployment & Integration
6.4.3 Managed
6.4.4 Professional
6.5 AI-as-a-Service

7 Global Artificial Intelligence in Agriculture Market, By Application
7.1 Introduction
7.2 Crops, Fruits, Vegetables, and Other Plants
7.2.1 Precision Farming Field Mapping Yield Monitoring Weather Tracking & Forecasting Crop Scouting Irrigation Management
7.2.2 Crop Growth Assessment
7.2.3 Farm Machinery Automation
7.3 Facial Recognition
7.4 Agricultural Drones
7.5 Agriculture Robots
7.6 Livestock Monitoring
7.6.1 Animal Health Monitoring
7.6.2 Animal Growth Monitoring
7.7 Driverless Tractors
7.8 Drone Analytics
7.9 Labor Management
7.10 Other Applications
7.10.1 Soil Management Nutrient Monitoring Moisture Monitoring
7.10.2 Smart Greenhouse Management
7.10.3 Fish Farming Management

8 Global Artificial Intelligence in Agriculture Market, By Farming Type
8.1 Introduction
8.2 Indoor Farming
8.3 Field Farming
8.4 Livestock Farming

9 Global Artificial Intelligence in Agriculture Market, By Deployment
9.1 Introduction
9.2 Hybrid
9.3 Cloud
9.4 On-premise

10 Global Artificial Intelligence in Agriculture Market, By Geography
10.1 Introduction
10.2 North America
10.2.1 US
10.2.2 Canada
10.2.3 Mexico
10.3 Europe
10.3.1 Germany
10.3.2 UK
10.3.3 Italy
10.3.4 France
10.3.5 Spain
10.3.6 Rest of Europe
10.4 Asia Pacific
10.4.1 Japan
10.4.2 China
10.4.3 India
10.4.4 Australia
10.4.5 New Zealand
10.4.6 South Korea
10.4.7 Rest of Asia Pacific
10.5 South America
10.5.1 Argentina
10.5.2 Brazil
10.5.3 Chile
10.5.4 Rest of South America
10.6 Middle East & Africa
10.6.1 Saudi Arabia
10.6.2 UAE
10.6.3 Qatar
10.6.4 South Africa
10.6.5 Rest of Middle East & Africa

11 Key Developments
11.1 Agreements, Partnerships, Collaborations and Joint Ventures
11.2 Acquisitions & Mergers
11.3 New Product Launch
11.4 Expansions
11.5 Other Key Strategies

12 Company Profiling
12.1 Farmers Edge
12.2 Granular
12.3 Ageagle
12.4 Descartes Labs
12.5 Gamaya
12.6 Ec2Ce
12.7 Precision Hawk
12.8 IBM
12.9 Deere & Company
12.10 Microsoft
12.11 The Climate Corporation
12.12 Prospera
12.13 Taranis
12.14 Awhere
12.15 Right-To-Win
12.16 Vision Robotics
12.17 Autonomous Tractor Corporation
12.18 Tule Technologies
12.19 Resson
12.20 Cropx Technologies

For more information about this report visit https://www.researchandmarkets.com/r/b231tk

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