Applied AI in Agriculture Market size was valued at USD 1567.5 million in 2022. The Artificial Intelligence AI in Agriculture market industry is projected to grow from USD 1567.5 in 2022 to USD 1,988.1 by 2023, exhibiting a compound annual growth rate (CAGR) of 29.3% during the forecast period (2023-2032)
Applied AI in agriculture provides various advantages to the farmers for instance real-time insight from the fields, plant health, monitoring soil quality, pesticide process, and automated irrigation, all of which are allowing to enhance the overall harvest quality as well as accuracy. Artificial intelligence in agriculture has various applications focused on optimizing the efficiency of the production of crops such as livestock monitoring, precision farming agriculture robots, drone analytics, and labor management.
FIGURE 1: GLOBAL ARTIFICIAL INTELLIGENCE MARKET SIZE 2019-2032 (USD MILLION)
Source: Secondary Research, Primary Research, MRFR Database, and Analyst Review
In the artificial intelligence driven market, machine learning has become a critical technology with broad applications. Machine learning algorithms allow the analysis of huge data sets gathered from drones, satellites, and sensors providing farmers with insight for informed decision making. These algorithms are able to identify trends, patterns, as well as anomalies within data regarding soil quality, crop health, weather conditions, and more. Machine learning's capability of predicting is specifically valuables. By training models on real- and historical-time data, farmers can anticipate factors such as pest infestations, outbreaks, and optimal planting times. Machine learning maximizes resource allocation, for instance, fertilizers and water, leading to enhanced yields as well as reduced waste.
Drones play crucial roles in the artificial intelligence driven agriculture market. Equipped with innovative cameras and sensors, drones provide a bird eye view, allowing farmers to gather high-resolution data and imagery. AI processes this data for monitoring crop health, identifying pests, as well as access irrigation required. Drones provide quick and comprehensive data gathering over large areas, an advantage in precision agriculture.
The Applied AI in Agriculture Market, in this report, has been segmented based on Technology into Machine Learning, Computer Vision, and Predictive Analytics. Machine learning holds the largest share, this is due to factors such as the cost of machine learning (ML) technology becoming more affordable, making it easier for farmers to adopt. There are also a number of cloud-based ML platforms available that make it easy for farmers to get started with ML. Machine learning (ML) can be used to make sense of the vast amounts of data that agriculture generates. This data can include weather data, soil data, and crop data. ML can be used to identify patterns and trends in this data that can be used to improve crop yields, reduce the use of pesticides and fertilizers, and conserve water. Machine learning (ML) can be used to automate tasks that are typically done by farmers, such as monitoring crops, managing irrigation, and detecting pests and diseases. This can free up farmers' time so they can focus on other tasks, such as planning and marketing.
FIGURE 2: GLOBAL Applied AI in Agriculture MARKET, BY Technology, 2022 VS 2032 (USD MILLION)
The Applied AI in Agriculture Market, in this report, has been segmented based on Software and AI-as-a-Service. The Software segments hold the largest share due to the increasing adoption of precision agriculture. Precision agriculture is a farming practice that uses data and technology to optimize crop yields and efficiency. This includes using sensors to collect data on soil conditions, weather, and crop health, and then using AI to analyze this data to make better decisions about planting, irrigating, fertilizing, and harvesting.
The Applied AI in Agriculture Market, in this report, has been segmented based on Drone Analytics and Precision Farming. Drone’s analytics hold the largest share. Drone analytics is a process of collecting data on crops and soil conditions using drones. This data can be used to identify problems early, such as pests and diseases, and to make better decisions about planting, irrigating, and fertilizing.
The North America market is witnessing major growth because of the growing requirement for real time data in the agriculture market. This technology enables them to access critical information about livestock, crops, as well as environmental conditions. By leveraging artificial intelligence powered drones, powered sensors, and other monitoring tools, farmers can collect data on factors such as pest infestations, soil moisture, and temperature. The capability for gathering as well as analyzing real-time data allow farmers to make good decisions. For example, AI is capable of predicting disease outbreaks allowing farmers to take preventive steps before the problem escalates. The utilization of artificial intelligence-powered predictive analytics allows farmers to anticipate issues as well as implement timely measures, thereby reducing crop losses as well as optimizing yields. Moreover, artificial intelligence driven systems can monitor as well as manage fertilizer application, crop health, and irrigation, leading to resource efficiency as well as sustainable agriculture practices. As the agriculture industry embraces artificial intelligence capabilities, the market for artificial intelligence in agriculture in North America region the market for AI in agriculture is soaring.
FIGURE 3: Applied AI in Agriculture Market SIZE BY REGION 2022 VS 2032
The Europe Applied AI in the agriculture market is witnessing substantial growth, specifically in agriculture robotics. The integration of AI with robotics is transforming the agricultural landscape by enabling advanced solutions to withstand challenges. Robotics in agriculture, integrated by AI, have a broad range of applications such as precision spraying, automated planting, precision spraying, monitoring livestock, and harvesting. A major driver of this growth in the adoption of robotics is due to labor shortages in the agriculture industry. The deployment of artificial intelligence driven robots can majorly minimize the dependency on manual labor for intense and time-consuming work. These robots can work continuously, even in adverse conditions or weather, contributing to enhancing productivity and efficiency. Moreover, artificial intelligence allows robots to operate autonomously along with making real time decisions which are based on data from cameras, sensors, and other sources. This intelligence level allows for accurate and targeted actions, reducing wastage of resources as well as environmental impact. The integration between robotics and AI also leads to enhanced data gathering along with analysis, offering insight farmers can use for optimizing their practices and improving crop yields. As Europe's agriculture sector is continuously adopting robotics the market in the region is experiencing substantial growth.
In Asia, the application of AI in agriculture is quickly gaining momentum and transforming the industry. The region's broad agricultural landscapes as well as a major population make artificial intelligence a valuable tool for solving complex challenges faced in agriculture sectors. Asia is experiencing remarkable growth in the application of AI in agriculture, with a major emphasis on IOT. The convergence of IOT and AI is reshaping the agriculture sector by providing growers and farmers innovative tools for data driven decision making as well as resource management. The implementation of Internet of Things devices in agriculture is resulting in the creation of smart farms. These farms are installed with devices and sensors that gather real time data on factors for instance with devices and sensors that gather real time data on various factors such as temperature, humidity, and crop health. Artificial intelligence algorithms analyze data to offer insight into optimal irrigation schedules, planting times, as well as pest control measures. The growth of the Internet of Things in Asia’s agricultural sectors is driven by factors for instance required to meet the demand of increasing population, ensure food security, and optimize resource utilization. By harnessing the power of IOT and AI, farmers can obtain higher yields, and minimize waste and inputs such as pesticides and water. Moreover, IOT-enabled devices enable remote monitoring as well as control empowering farmers to manage their operations more effectively. These technologies are specifically important in regions having limited access to resources. As the integration of IOT and AI continues to expand around the Asian agriculture industry, the region stands to gain enhanced, sustainability, resilience, and productivity in face of evolving agricultural challenges.
The Applied AI in the Agriculture market is highly competitive, with a number of established players competing for market share. The market is primarily driven by the The growing adoption of cloud computing. There are several domestic, regional, and global players operating in the Applied AI in Agriculture market who continuously strive to gain a significant share of the overall market. During the study, MRFR has analyzed some of the major players in the Air Start Unit market who have contributed to the market growth. These include Some notable players in the Applied AI in Agriculture market include Microsoft, IBM, Google, Amazon.com, Inc., Deere & Company., TechTarget, Vision Robotics Corporation., DroneDeploy., PrecisionHawk, and AGCO Corporation., Others.
Among these, Microsoft is a prominent solution provider of wide range of AI solutions for agriculture. The company offers a range of AI solutions that can be used to detect pests and diseases in crops, and to track livestock. IBM is another AI solution that can be used to monitor crops, predict yields, and manage irrigation. Similarly, Deere & Company's AI solutions can be used to monitor crops and drive autonomously.
Microsoft
IBM
Amazon
John Deere
CropX
Vision Robotics
DroneDeploy
PrecisionHawk
AGCO Corporation
May 2022, The Alliance for a Green Revolution in Africa (AGRA) and Microsoft have expanded their partnership to advance digital agricultural transformation in Africa. The partnership, which was formalized through a Memorandum of Understanding (MoU) signed on the sidelines of the World Economic Forum in Davos in 2022, builds on previous work between AGRA and Microsoft that focused on supporting AGRA's digital transformation efforts to improve food security in Africa.
December 2022 Amazon Web Services (AWS), a subsidiary of Amazon.com, Inc., announced eight new capabilities for Amazon SageMaker, its fully managed machine learning (ML) service. Amazon SageMaker allows developers, data scientists, and business analysts to build, train, and deploy ML models quickly and easily.
September 2022 DroneDeploy and Corteva Agriscience announced a partnership to help farmers make better management decisions year-round. Corteva operates one of the world's largest agricultural drone fleets, and DroneDeploy is a leading provider of drone software. Together, the two companies will offer farmers a suite of tools that can be used to monitor crops, identify problems, and optimize yields.
Machine Learning
Computer Vision
Predictive Analytics
Software
AI-as-a-Service
Drone Analytics
Precision Farming
US
Canada
Mexico
Germany
France
UK
Italy
Spain
Rest of Europe
China
Japan
India
Australia
South Korea
Australia
Rest of Asia-Pacific
UAE
Saudi Arabia
South Africa
Rest of MEA
Brazil
Argentina
Rest of South America
| Report Attribute/Metric | Details |
| Market Size 2022 | USD 1567.53 million |
| Market Size 2023 | USD 1,988.1 million |
| Market Size 2032 | USD 25999.5 million |
| Compound Annual Growth Rate (CAGR) | 29.3 xx% (2023-2032) |
| Base Year | 2022 |
| Market Forecast Period | 2023-2032 |
| Historical Data | 2019- 2021 |
| Market Forecast Units | Value (USD million) |
| Report Coverage | Revenue Forecast, Market Competitive Landscape, Growth Factors, and Trends |
| Segments Covered | Technology, Offering, and Application |
| Geographies Covered | Europe, North America, Asia-Pacific, Middle East & Africa, and South America |
| Countries Covered | The U.S, Germany, Canada, U.K., Italy, France, Spain, Japan, China, Australia, India, South Korea, and Brazil |
| Key Companies Profiled | Microsoft, IBM, Google, Amazon.com, Inc., Deere & Company., TechTarget, Vision Robotics Corporation., DroneDeploy., PrecisionHawk, and AGCO Corporation., Others. |
| Key Market Opportunities | The need to improve crop quality The decreasing cost of AI solutions |
| Key Market Dynamics | The growing adoption of cloud computing The increasing adoption of precision agriculture |
Applied AI in Agriculture Market Highlights:
Frequently Asked Questions (FAQ) :
Applied AI in Agriculture market was valued at USD 1,567.5 Million in 2022.
The global market is projected to grow at a CAGR of 29.3% during the forecast period, 2023-2032.
North America had the largest share of the global market.
The key players in the market are Microsoft, IBM, Google, Amazon.com, Inc. and Deere & Company.
Research Analyst Level II With a technical background in information technology & semiconductors, shubham has 4.5+ years of experience in market research and analytics with the tasks of data mining, analysis, and project execution. he is the poc for our clients, for their consulting projects running under the ict/semiconductor domain. shubham holds a bachelor’s in information and technology and a master of business administration (mba). shubham has executed over 150 research projects for our clients under the brand name market research future in the last 2 years. his core skill is building the research respondent relation for gathering the primary information from industry and market estimation for niche markets. he is having expertise in conducting secondary & primary research, market estimations, market projections, competitive analysis, analysing current market trends and market dynamics, deep-dive analysis on market scenarios, consumer behaviour, technological impact analysis, consulting, analytics, etc. he has worked on fortune 500 companies' syndicate and consulting projects along with several government projects. he has worked on the projects of top tech brands such as ibm, google, microsoft, aws, meta, oracle, cisco systems, samsung, accenture, vmware, schneider electric, dell, hp, ericsson, and so many others. he has worked on metaverse, web 3.0, zero-trust security, cyber-security, blockchain, quantum computing, robotics, 5g technology, high-performance computing, data centers, ai, automation, it equipment, sensors, semiconductors, consumer electronics and so many tech domain projects.
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