AI Opportunities in the Agriculture Market, 2019-2024 - Increasing Demand for Monitoring of Livestock & Drones in Agricultural Farms
DUBLIN, Jan. 30, 2020 /PRNewswire/ -- The "Artificial Intelligence in Agriculture Market Research Report: By Type, Technology, Application, Geographical Outlook - Global Industry Analysis and Growth Forecast to 2024" report has been added to ResearchAndMarkets.com's offering.
As per the United Nations (UN) report, the world population, which is currently 7.7 billion, is predicted to reach 8.6 billion by 2030. This surge in the population is sure to increase the demand for agricultural products. This demand is primarily rising in countries including India, China, Brazil, and the U.S. because of the rapid urbanization, changing consumption habits of the populace, and increasing disposable income.
With the increasing population, the current sources of agricultural production will not be enough, due to which there is a growing need for increasing the productivity. For this reason, the key agricultural product-producing countries are incorporating artificial intelligence (AI) into their agricultural practices.
AI, the imitation of human intelligence, empowers machines, especially computer systems, with capabilities such as self-correction, learning, and reasoning. In the agricultural sector, AI can be implemented for farming and gardening, in order to increase the precision and efficacy in maintaining, planting, and harvesting the crops.
The global AI in agriculture market generated revenue of $584 million in 2018 and is predicted to witness a CAGR of 38.3% in the coming years.
The major applications of AI in the agricultural sector include drone analytics, agricultural robots, livestock monitoring, and precision farming. Among these, the highest demand for AI was created by the precision farming application in 2018, and it is also going to be at the top in the coming years. This is because of the rising popularity of precision farming among the agrarian community, as there is a surging need for optimum yield using the limited available resources, which will eventually result in a reduction in the cost of crop production.
Among the above-mentioned applications, the demand for drone analytics in agricultural farms is projected to grow significantly in the near future. This is because drones that are enabled with AI are able to fly autonomously in an obstacle-filled environment. Moreover, drones are increasingly being used in the agricultural sector for assisting in irrigation schedules, estimating yield data, scanning soil health, and applying fertilizers.
For instance, there is a rising demand for drones in the Xinjiang province of China for spraying pesticides in cotton fields, as by using drones, over 1,544 square miles of cotton fields can be sprayed at once, making the process time-efficient and improving the agricultural output. Because of all these advantages, several government initiatives are encouraging the adoption of drones for modernizing agricultural practices.
The demand for AI in the agricultural sector is also increasing due to the growing use of robotics in the field. Due to the increasing population and lack of skilled farm workers, the automation of agricultural processes has resulted in easier, modernized, and sophisticated farming practices via the deployment of robots.
Furthermore, agricultural stakeholders are majorly focusing on refining the productivity using advanced farming practices and reducing the carbon footprint created by the entire agricultural process. Due to these factors, manufacturers in the robotics niche are coming up with offerings, which are equipped with AI, for operating in the dynamic and unstructured agricultural environment.
Key Topics Covered
Chapter 1. Research Background
Chapter 2. Research Methodology
Chapter 3. Executive Summary
Chapter 4. Introduction
4.1 Definition of Market Segments
4.1.1 By Type
4.1.1.1 Product
4.1.1.1.1 Hardware
4.1.1.1.2 Software
4.1.1.2 Service
4.1.1.2.1 Professional
4.1.1.2.2 Managed
4.1.2 By Technology
4.1.2.1 Machine Learning
4.1.2.2 Computer Vision
4.1.2.3 Predictive Analytics
4.1.3 By Application
4.1.3.1 Agricultural Robots
4.1.3.2 Precision Farming
4.1.3.3 Drone Analytics
4.1.3.4 Livestock Monitoring
4.1.3.5 Others
4.2 Value Chain Analysis
4.3 Market Dynamics
4.3.1 Trends
4.3.1.1 Increasing Use of Robotics in Agriculture
4.3.1.2 Increasing Use of Smart Sensors in Agriculture
4.3.2 Drivers
4.3.2.1 Growing Demand for Agricultural Production
4.3.2.2 Rising Adoption of Internet of Things (IoT)
4.3.2.3 Increasing Demand for Monitoring of Livestock
4.3.2.4 Increasing Demand for Drones in Agricultural Farms
4.3.2.5 Impact Analysis of Drivers on Market Forecast
4.3.3 Restraints
4.3.3.1 Lack of Awareness and High Cost of AI Solutions
4.3.3.2 Impact Analysis of Restraints on Market Forecast
4.3.4 Opportunities
4.3.4.1 Growth Opportunities from Developing Countries
4.3.4.2 AI Powered Chatbots
4.4 Porter's Five Forces Analysis
Chapter 5. Global Market Size and Forecast
5.1 By Type
5.1.1 By Product
5.1.2 By Service
5.2 By Technology
5.3 By Application
5.4 By Region
Chapter 6. North America Market Size and Forecast
Chapter 7. Europe Market Size and Forecast
Chapter 8. APAC Market Size and Forecast
Chapter 9. LATAM Market Size and Forecast
Chapter 10. MEA Market Size and Forecast
Chapter 11. Competitive Landscape
11.1 Analysis of Key Players in the Market
11.2 List of Key Players and Their Offerings
11.3 Competitive Benchmarking of Key Players
11.4 Global Strategic Developments of Key Players
11.4.1 Mergers and Acquisitions
11.4.2 Product Launches
11.4.3 Partnerships
Chapter 12. Company Profiles
12.1 International Business Machines (IBM) Corporation
12.1.1 Business Overview
12.1.2 Product and Service Offerings
12.1.3 Key Financial Summary
12.1.4 SWOT Analysis
12.2 Microsoft Corporation
12.3 Bayer AG
12.4 Deere & Company
12.5 A.A.A Taranis Visual Ltd.
12.6 AgEagle Aerial Systems Inc.
12.7 AGCO Corporation
12.8 Raven Industries Inc.
12.9 Ag Leader Technology
12.1 Trimble Inc.
12.11 Google LLC
12.12 Gamaya S.A.
12.13 Granular Inc.
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