US: 1.646.568.9797 (24x5)
UK: 44.330.808.0580 (24x5)
Worldwide: 91.703.051.960 (24x7)
Industry Insights, Market Size, CAGR, High-Level Analysis: Artificial Intelligence in Agriculture Market The global artificial intelligence in agriculture market size was valued at USD 608.9 million in 2018 and is anticipated to register a CAGR of 25.4% from 2019 to 2025. Artificial intelligence techniques for farming help increase productivity and yield. Therefore, agribusiness corporations adopt artificial intelligence technologies in terms of predictive analytics-based solutions. AI-based applications and techniques help control pests, yield healthier crops, monitor the soil, and improve agriculture-related tasks in the entire food supply chain. Artificial intelligence is increasingly being adopted in the agriculture industry for the improvement of harvest quality and accuracy since it helps analyze farm data. The global population is expected to reach 9.8 billion by 2050, according to the UN. Rapidly growing population drives the need for bringing AI in the agriculture industry. Limited arable land availability and need for increased food production for food security drive the need for a green revolution fueled by artificial intelligence, Internet of Things (IoT), and big data. AI-enabled applications cater to several areas in the agriculture industry, such as predictive and recommendation analytics, identifying plant diseases, detecting pest infestations, and soil monitoring. Artificial intelligence solutions comprising robots, drones, and ground-based wireless sensors are increasingly being deployed in the agriculture industry. For instance, in November 2017, Microsoft collaborated with the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT) to build an AI Sowing App. Furthermore, NatureFresh Farms, a U.S.-based tech company, is working on developing an AI technology to analyze plant information at scale to produce accurate harvest and yield forecasts. This AI technology predicts how long the blossom would take to ripen by using an artificial intelligence algorithm. Furthermore, automation in agriculture helps allocate resources such as fertilizers and water, determine the optimal date for crop sowing, and detect weeds, thereby driving the demand for artificial intelligence solutions. It also helps recommend how many seeds must be sowed by using historical long-term weather reports, production data, commodity pricing predictions, and seed information, among other inputs. The multiple benefits of artificial intelligence-based applications encourage several tech giants and start-ups to develop IoT-enabled devices for the deployment of AI applications for agriculture on a large scale. Component Insights of Artificial Intelligence in Agriculture Market 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 of Artificial Intelligence in Agriculture Market 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 of Artificial Intelligence in Agriculture Market 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 of Artificial Intelligence in Agriculture Market 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. Market Share Insights of Artificial Intelligence in Agriculture Market 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. Segmentations, Sub Segmentations, CAGR, & High-Level Analysis overview of Artificial Intelligence in Agriculture Market Research Report This report provides forecasts for revenue growth at the global, regional, and country levels and analyses of the latest industry trends in each of the sub-segments from 2014 to 2025. For the purpose of this study, this market research report has segmented the global artificial intelligence in agriculture market report based on component, technology, application, and region: Component Outlook (Revenue, USD Million, 2019 - 2030) Hardware Software Service Technology Outlook (Revenue, USD Million, 2019 - 2030) Machine Learning & Deep Learning Predictive Analytics Computer Vision Application Outlook (Revenue, USD Million, 2019 - 2030) Precision Farming Drone Analytics Agriculture Robots Livestock Monitoring Others
Table of Contents Chapter 1 Methodology and Scope 1.1 Research Methodology 1.2 Research Scope and Assumptions 1.3 List to Data SourcesChapter 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 | read more...