Technology Buz

Artificial Intelligence in Agriculture Market Size, CAGR, Company Share, Revenue and Forecast 2025

Pressmeddelande   •   Jan 14, 2020 04:36 CET

The global artificial intelligence in agriculture market size is expected to reach USD 2.9 billion by 2025, anticipated to register a CAGR of 25.4% from 2019 to 2025. 

Artificial intelligence solutions in the agricultural industry are emerging in various forms, such as soil and crop monitoring, agricultural robots, and predictive analytics. Farmers and agribusiness corporations are increasingly using soil sampling and artificial intelligence -enabled sensors for data gathering for better analysis and processing. The availability of these processed data has paved the way for the deployment of artificial intelligence in agriculture and farming.

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Key suggestions from the report:

  • Surge in the deployment of predictive analytics solutions in the farming and agriculture industry has created massive potential for artificial intelligence solutions in the agricultural industry
  • Several benefits of artificial intelligence, such as prevention of cost overruns, weather forecasting, remote monitoring, weed detection, and irrigation management encourages the implementation of artificial intelligence in agriculture for increased productivity
  • Market players are focusing on providing advanced artificial intelligence and big data in the agricultural industry, which offers advanced features such as crop scouting, field mapping, and yield monitoring
  • Key market players are considering partnerships and acquisitions of startups providing innovative products to expand their range of artificial intelligence solutions for the agricultural industry
  • Key competitors in the market are IBM Corporation; Microsoft; Deere & Company; AgEagle Aerial Systems Inc.; The Climate Corporation; Granular, Inc.; Descartes Labs, Inc.; Prospera Technologies; Taranis; aWhere Inc.; GAMAYA; ec2ce; PrecisionHawk; VineView; and Tule Technologies Inc.; among others.

Rapidly increasing global population is one of the key factors driving the need for artificial intelligence in agriculture. The global population is expected to reach 9.8 billion by 2050, according to the UN. Subsequently, food production must increase significantly as well. Artificial intelligence enables efficient and potential farming techniques for increased crop productivity and yield. For instance, the artificial intelligence Sowing App developed by Microsoft sends sowing advisories on the optimal date for crop sowing to farmers. It enhances the farmers' efficiency in terms of planting and forecasting weather conditions.

The Asia Pacific market is expected to witness substantial growth over the forecast period, owing to increasing adoption of artificial intelligence -enabled solutions and services by agriculture-technology-based companies in emerging economies. Emerging economies such as India and China have started implementing artificial intelligence technologies such as machine learning and computer vision to increase crop yield. Favorable regulations and standards in these countries encourage the implementation of modern techniques in farming and agriculture. For instance, in July 2019, the government of India began the use of artificial intelligence for yield estimation and crop cutting to cut down the cost of farming and increase productivity.

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Component Insights

The market is classified on the basis of component into hardware, software, and service. The software segment is expected to have a significant market share among components. Major players such as IBM, Microsoft, and Deere & Company offer AI-based solutions for the agriculture industry. AI-based software boost crop productivity and yield by implementing prediction-based analytics and computer vision.

Moreover, increasing penetration of predictive analytics-based software boosts the growth of the software segment. Prominent predictive analytics-based software include Watson Decision Platform by IBM Corporation, AI Sowing App by Microsoft, and See and Spray pesticide and herbicide distribution systems by Deere & Company. These artificial intelligence solutions help farmers determine the optimal dates for crop sowing; detect crop diseases; monitor crop yield; and determine the required amount of land, fertilizers, water, and pesticides. Advantages of AI-enabled software for potential application areas, such as precision farming and drone analytics, further drives the growth of the software segment in the AI in agriculture market.

Application Insights

On the basis of application, the market has been classified into precision farming, drone analytics, agriculture robots, livestock monitoring, and others. The precision farming segment is expected to account for a significant market share over the projected period. Precision farming is one of the fastest-growing AI-enabled applications in agriculture. It helps farmers minimize costs and optimize resources effectively.

Precision farming uses AI for data collection, interpretation, and analysis of digital data. For instance, GPS-equipped combine harvesters deploy artificial intelligence to track the harvest yield for field variability analysis, such as differences in water, soil makeup, or fungus, to produce georeferenced data. The analysis and predictions enable farmers to customize fertilizers or pesticides accordingly. Agriculture robots controlled by an AI system combine artificial intelligence, field sensors, and data analytics and can be effectively used for a variety of applications. These robots are efficient harvesting systems as they have the ability to weed and hoe. Increasing adoption of artificial intelligence in agriculture and new developments in robotics drive the agriculture robots segment.

Technology Insights

By way of technology, the market is segmented into machine learning and deep learning, predictive analytics, and computer vision. Several agribusiness corporations adopt predictive analytics to deploy artificial intelligence. For instance, AgEagle Aerial Systems Inc.; Microsoft; and Granular, Inc.; have worked on a prediction-based analytics technology to develop AI-enabled solutions and platforms for farming and agriculture.

The significant challenges faced by the agriculture industry are pesticide control, weed management, irrigation and drainage management, weather tracking, and crop disease infestations. Predictive analysis helps farmers analyze and address these challenges with the use of image analysis and neural networks. Furthermore, drone-enabled agricultural solutions have been introduced to support predictive analytics. For instance, AgEagle Aerial Systems Inc., focused on using artificial intelligence to enhance crop yield production, offers drone analytical solutions for the identification of concerned areas in crop fields and irrigation management. Since predictive analytics provides more efficiency in agricultural applications, the segment is expected to witness a steady CAGR over the forecast period. Moreover, by applying machine learning to sensor data, farm management systems are evolving into real artificial intelligence systems, increasing the scope of production improvement. Therefore, the machine learning and deep learning segment is also expected to witness growth.

Regional Insights

The market in North America accounted for a share of more than 35.0% in 2018, owing to the leading industrial automation industry and adoption of artificial intelligence solutions in the region. North America is characterized by improved purchasing power of the population, continuous investments in automation, considerable investments in IIoT, and increasing focus from governments on in-house AI equipment production. The market also benefits from the presence of numerous agricultural technology providers exploring artificial intelligence solutions, including IBM Corporation; Deere & Company; Microsoft; Granular, Inc.; and The Climate Corporation.

The Asia Pacific market is expected to demonstrate the highest CAGR over the forecast period. Its growth is attributed to increasing adoption of artificial intelligence technologies in agriculture. Emerging economies such as India and China are leveraging the adoption of artificial intelligence solutions such as remote monitoring technology and predictive analysis in the food industry. Furthermore, the rising demand to create smart cities in these economies is encouraging agribusiness companies to adopt AI-leveraging solutions and services.

Artificial Intelligence in Agriculture Market Share Insights

Key industry participants in the market include IBM Corporation; Microsoft; Deere & Company; AgEagle Aerial Systems Inc.; The Climate Corporation; Granular, Inc.; Descartes Labs, Inc.; Prospera Technologies; Taranis; aWhere Inc.; GAMAYA; ec2ce; PrecisionHawk; VineView; and Tule Technologies Inc.

Vendors providing artificial intelligence solutions for agriculture focus on increasing their customer base to gain a competitive edge in the market by adopting several strategic initiatives such as collaborations, acquisitions, mergers, and partnerships. For instance, in May 2019, Deere & Company partnered with Cultivating New Frontiers in Agriculture (CNFA), an international agricultural development organization to increase productivity and income for smallholder farmers by implementing mechanization in the agriculture industry. In October 2018, The Climate Corporation collaborated with three agriculture-tech companies, SoilOptix; AgCon Aerial Corp.; and A&L Canada Laboratories Inc., to deliver new capabilities for farmers and expand its digital agriculture platform, Climate FieldView.

Segmentation

  • Component Outlook (Revenue, USD Million, 2014 - 2025)
    • Hardware
    • Software
    • Service
  • Technology Outlook (Revenue, USD Million, 2014 - 2025)
    • Machine Learning & Deep Learning
    • Predictive Analytics
    • Computer Vision
  • Application Outlook (Revenue, USD Million, 2014 - 2025)
    • Precision Farming
    • Drone Analytics
    • Agriculture Robots
    • Livestock Monitoring
    • Others
  • Regional Outlook (Revenue, USD Million, 2014 - 2025)
    • North America
      • U.S.
      • Canada
      • Mexico
    • Europe
      • U.K.
      • Germany
      • France
    • Asia Pacific
      • China
      • Japan
      • India
    • South America
      • Brazil
    • Middle East and Africa (MEA)

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Table of Contents

Chapter 1 Methodology and Scope
1.1 Research Methodology
1.2 Research Scope and Assumptions
1.3 List to Data Sources
Chapter 2 Executive Summary
2.1 AI in Agriculture Market - Industry Snapshot & Key Buying Criteria, 2014 - 2025
2.2 Global
2.2.1 Global AI in Agriculture Market, 2014 - 2025
2.2.2 Global AI in Agriculture Market, by Component, 2014 - 2025
2.2.3 Global AI in Agriculture Market, by technology, 2014 - 2025
2.2.4 Global AI in Agriculture Market, by application, 2014 - 2025
2.2.5 Global AI in Agriculture Market, by Region, 2014 - 2025
Chapter 3 Market Variables, Trends & Scope
3.1 Market Segmentation & Scope
3.2 Technological Overview
3.3 AI in Agriculture Size and Growth Prospects
3.4 Value Chain Analysis
3.5 Market Dynamics
3.5.1 Market Drivers
3.5.1.1 Need for increasing the crop productivity owing to the exponential growth in world’s population
3.5.1.2 Favorable government initiatives for modern agriculture techniques
3.5.2 Market Restraint
3.5.2.1 Lack of skilled workforce
3.5.3 Market Opportunity
3.5.3.1 Increasing adoption of unmanned aerial vehicles in agriculture and farming
3.5.4 Market Challenge
3.5.4.1 Data analysis from the unstructured farms and lands
3.6 Penetration & Key Opportunities Mapping
3.7 Industry Type Analysis - Porter’s
3.7.1 Supplier Power
3.7.2 Buyer Power
3.7.3 Substitution Threat
3.7.4 Threat from new entrant
3.7.5 Competitive rivalry
3.8 AI in Agriculture - PEST Analysis
3.8.1 Political
3.8.2 Economic
3.8.3 Social
3.8.4 Technological
Chapter 4 AI in Agriculture: Component Outlook
4.1 Hardware
4.1.1 Hardware AI in Agriculture Market, BY Region, 2014 - 2025
4.2 Software
4.2.1 Software AI in Agriculture Market, by region, 2014 - 2025
4.3 Service
4.3.1 Service AI in Agriculture Market, by region, 2014 - 2025
Chapter 5 AI in Agriculture: Technology Outlook
5.1 Machine Learning & Deep Learning
5.1.1 Machine Learning & Deep Learning AI in Agriculture Market, by region, 2014 - 2025
5.2 Predictive Analytics
5.2.1 Predictive Analytics AI in Agriculture Market, by region, 2014 - 2025
5.3 Computer Vision
5.3.1 Computer Vision AI in Agriculture Market, by region, 2014 - 2025
Chapter 6 AI in Agriculture: Application Outlook
6.1 Precision Farming
6.1.1 Precision Farming AI in Agriculture Market, BY Region, 2014 - 2025
6.2 Drone Analytics
6.2.1 Drone Analytics AI in Agriculture Market, by region, 2014 - 2025
6.3 Agriculture Robots
6.3.1 Agriculture Robots AI in Agriculture Market, by region, 2014 - 2025
6.4 Livestock Monitoring
6.4.1 Livestock Monitoring AI in Agriculture Market, by region, 2014 - 2025
6.5 Others
6.5.1 Others AI in Agriculture Market, by region, 2014 - 2025
Chapter 7 AI in Agriculture: Regional Outlook
7.1 North America
7.1.1 Regional Trends
7.1.2 North America AI in Agriculture Market, by component, 2014 - 2025
7.1.3 North America AI in Agriculture Market, by technology, 2014 - 2025
7.1.4 North America AI in Agriculture Market, by application, 2014 - 2025
7.1.5 U.S.
7.1.5.1 U.S. AI in Agriculture Market, by component, 2014 - 2025
7.1.5.2 U.S. AI in Agriculture Market, by technology, 2014 - 2025
7.1.5.3 U.S. AI in Agriculture Market, by application, 2014 - 2025
7.1.6 Canada
7.1.6.1 Canada AI in Agriculture Market, by component, 2014 - 2025
7.1.6.2 Canada AI in Agriculture Market, by technology, 2014 - 2025
7.1.6.3 Canada AI in Agriculture Market, by application, 2014 - 2025
7.1.7 Mexico
7.1.7.1 Mexico AI in Agriculture Market, by component, 2014 - 2025
7.1.7.2 Mexico AI in Agriculture Market, by technology, 2014 - 2025
7.1.7.3 Mexico AI in Agriculture Market, by application, 2014 - 2025
7.2 Europe
7.2.1 Regional Trends
7.2.2 Europe AI in Agriculture Market, by component, 2014 - 2025
7.2.3 Europe AI in Agriculture Market, by technology, 2014 - 2025
7.2.4 Europe AI in Agriculture Market, by application, 2014 - 2025
7.2.5 Germany
7.2.5.1 Germany AI in Agriculture Market, by component, 2014 - 2025
7.2.5.2 Germany AI in Agriculture Market, by technology, 2014 - 2025
7.2.5.3 Germany AI in Agriculture Market, by application, 2014 - 2025
7.2.6 U.K.
7.2.6.1 U.K. AI in Agriculture Market, by component, 2014 - 2025
7.2.6.2 U.K. AI in Agriculture Market, by technology, 2014 - 2025
7.2.6.3 U.K. AI in Agriculture Market, by application, 2014 - 2025
7.2.7 France
7.2.7.1 France AI in Agriculture Market, by component, 2014 - 2025
7.2.7.2 France AI in Agriculture Market, by technology, 2014 - 2025
7.2.7.3 France AI in Agriculture Market, by application, 2014 - 2025
7.3 Asia Pacific
7.3.1 Regional Trends
7.3.2 Asia Pacific AI in Agriculture Market, by component, 2014 - 2025
7.3.3 Asia Pacific AI in Agriculture Market, by technology, 2014 - 2025
7.3.4 Asia Pacific AI in Agriculture Market, by application, 2014 - 2025
7.3.5 China
7.3.5.1 China AI in Agriculture Market, by component, 2014 - 2025
7.3.5.2 China AI in Agriculture Market, by technology, 2014 - 2025
7.3.5.3 China AI in Agriculture Market, by application, 2014 - 2025
7.3.6 Japan
7.3.6.1 Japan AI in Agriculture Market, by component, 2014 - 2025
7.3.6.2 Japan AI in Agriculture Market, by technology, 2014 - 2025
7.3.6.3 Japan AI in Agriculture Market, by application, 2014 - 2025
7.3.7 India
7.3.7.1 India AI in Agriculture Market, by component, 2014 - 2025
7.3.7.2 India AI in Agriculture Market, by technology, 2014 - 2025
7.3.7.3 India AI in Agriculture Market, by application, 2014 - 2025
7.4 South America
7.4.1 Regional Trends
7.4.2 South America AI in Agriculture Market, by component, 2014 - 2025
7.4.3 South America AI in Agriculture Market, by technology, 2014 - 2025
7.4.4 South America AI in Agriculture Market, by application, 2014 - 2025
7.4.5 Brazil
7.4.5.1 Brazil AI in Agriculture Market, by component, 2014 - 2025
7.4.5.2 Brazil AI in Agriculture Market, by technology, 2014 - 2025
7.4.5.3 Brazil AI in Agriculture Market, by application, 2014 - 2025
7.5 MEA
7.5.1 Regional Trends
7.5.2 MEA AI in Agriculture Market, by component, 2014 - 2025
7.5.3 MEA AI in Agriculture Market, by technology, 2014 - 2025
7.5.4 MEA AI in Agriculture Market, by application, 2014 - 2025
Chapter 8 Competitive Landscape
8.1 IBM Corporation
8.1.1 Company overview
8.1.2 Financial performance
8.1.3 Product benchmarking
8.1.4 Recent developments
8.2 Microsoft
8.2.1 Company overview
8.2.2 Financial performance
8.2.3 Product benchmarking
8.2.4 Recent developments
8.3 Deere & Company
8.3.1 Company overview
8.3.2 Financial performance
8.3.3 Product benchmarking
8.3.4 Recent developments
8.4 AgEagle Aerial Systems Inc.
8.4.1 Company overview
8.4.2 Financial performance
8.4.3 Product benchmarking
8.4.4 Recent developments
8.5 The Climate Corporation
8.5.1 Company overview
8.5.2 Product benchmarking
8.5.3 Recent developments
8.6 Granular, Inc.
8.6.1 Company overview
8.6.2 Product benchmarking
8.6.3 Recent developments
8.7 Descartes Labs, Inc.
8.7.1 Company overview
8.7.2 Product benchmarking
8.7.3 Recent developments
8.8 Prospera Technologies
8.8.1 Company overview
8.8.2 Product benchmarking
8.8.3 Recent developments
8.9 Taranis
8.9.1 Company overview
8.9.2 Product benchmarking
8.9.3 Recent developments
8.10 aWhere Inc.
8.10.1 Company overview
8.10.2 Product benchmarking
8.10.3 Recent developments
8.11 GAMAYA
8.11.1 Company overview
8.11.2 Product benchmarking
8.11.3 Recent developments
8.12 ec2ce
8.12.1 Company overview
8.12.2 Product benchmarking
8.12.3 Recent developments
8.13 PrecisionHawk
8.13.1 Company overview
8.13.2 Product benchmarking
8.13.3 Recent developments
8.14 VineView
8.14.1 Company overview
8.14.2 Product benchmarking
8.14.3 Recent developments
8.15 Tule Technologies Inc
8.15.1 Company overview
8.15.2 Product benchmarking
8.15.3 Recent developments

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