Certified Global Research Member
Key Questions Answered
- Global Market Outlook
- In-depth analysis of global and regional trends
- Analyze and identify the major players in the market, their market share, key developments, etc.
- To understand the capability of the major players based on products offered, financials, and strategies.
- Identify disrupting products, companies, and trends.
- To identify opportunities in the market.
- Analyze the key challenges in the market.
- Analyze the regional penetration of players, products, and services in the market.
- Comparison of major players’ financial performance.
- Evaluate strategies adopted by major players.
- Recommendations
Why Choose Market Research Future?
- Vigorous research methodologies for specific market.
- Knowledge partners across the globe
- Large network of partner consultants.
- Ever-increasing/ Escalating data base with quarterly monitoring of various markets
- Trusted by fortune 500 companies/startups/ universities/organizations
- Large database of 5000+ markets reports.
- Effective and prompt pre- and post-sales support.
There are multiple ways via which different firms seek to establish and improve on their market share in the constantly changing landscape of applied artificial intelligence (AI) in agriculture market share. Specifically, technological innovations become central as corporations aim at developing and offering artificial intelligence products with advanced features and capabilities aligned towards specific needs within this sector. This involves exploiting breakthroughs made using machine learning algorithms including image recognition techniques which have enhanced resource utilization efficiency for sustainable cropping systems based upon minimal input rates; yield optimization programs designed around modern analytic tools such as big data analytics used together alongside traditional crop modelling approaches which combine simulation modeling outputs from software packages like CERES maize or APSIM soybean over subsequent seasons until convergence occurs; etc.
Market share positioning depends on pricing strategies on a large scale within Artificial Intelligence (AI) applied to Agriculture trade. While some firms focus on leadership through cost reduction by providing less costly options than rivals do targeting discerning customers who want cheap but quality stuffs others sell highly priced premium offerings featuring attributes such as predictive analysis, site-specific agriculture or comprehensive datasets. By targeting specific agribusinesses in preference of top quality AI solutions, these businesses develop niche markets willing to pay a premium for sophisticated personalized artificial intelligence solutions.
Collaborations and strategic partnerships are key determinants of market share positioning in the Applied AI in Agriculture Market. Companies often form alliances with agricultural technology providers, equipment manufacturers, or research institutions to better integrate and use their AI solutions within the farm ecosystem. When these all come into place, there will be a broader base of services that can work together rather than work against one another and it can increase market reach by farmers and address specific needs of different types of farming systems. Additionally, large scale farming operations or agricultural cooperatives act as stable income streams besides enhancing further the market presence.
In order to gain more shares on the market dominated by applied artificial Intelligence (AI) in agriculture; several organizations have adopted different approaches aimed at ensuring customer satisfaction among end-users. Building long lasting relationships through user-friendly interfaces that provide actionable information along with responsive assistance is considered as one way towards maintaining farmer priorities and delivering value to them through appropriate strategies developed over time. Businesses that focus more on user experience gain customer loyalty while those who emphasize on great experiences benefit from greater word-of-mouth outreaches that lead to positive feedbacks expanding their territories. Therefore they target their products specifically at certain agricultural tasks or uses thus giving them a competitive advantage.
The cornerstone of market share positioning strategies is innovation. Investing in research and development enables companies to stay ahead of agricultural trends and bring new features into their products such as AI-powered pest detection, automated irrigation systems, or crop health monitoring. Pioneering new solutions in the market allows companies that are industry leaders to attract early adopters from the agriculture sector thus giving them an upper hand over others. That is why continuous improvement and adaptation to emerging technologies are crucial for sustained success in the ever-evolving Applied AI in Agriculture Market.
Covered Aspects:
| Report Attribute/Metric | Details |
|---|---|
| Market Opportunities | The need to improve crop quality The decreasing cost of AI solutions |
| Market Dynamics | The growing adoption of cloud computing The increasing adoption of precision agriculture |
Applied AI in Agriculture Market Highlights:
Applied AI in Agriculture Market Overview
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
Global Artificial Intelligence Market Trends
Use of Machine learning
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.
Adoption of Drones
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.
Global Air Start Unit Market- Segment Insights
Applied AI in Agriculture Market - Technology Insights
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)
Applied AI in Agriculture Market - Offering Insights
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.
Applied AI in Agriculture Market - Application Insights
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.
Applied AI in Agriculture Market - Regional Insights
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.
Applied AI in Agriculture Market - Key Market Players and Competitive Insights
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.
Applied AI in Agriculture Market - Key Companies Include
-
Microsoft
-
IBM
-
Google
-
Amazon
-
John Deere
-
CropX
-
Vision Robotics
-
DroneDeploy
-
PrecisionHawk
-
AGCO Corporation
Applied AI in Agriculture Market - Industry developments
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.
Applied AI in Agriculture Market - Market Segmentation
Applied AI in Agriculture Market - Technology Outlook
-
Machine Learning
-
Computer Vision
-
Predictive Analytics
Applied AI in Agriculture Market - Offering Outlook
-
Software
-
AI-as-a-Service
Applied AI in Agriculture Market - Application Outlook
-
Drone Analytics
-
Precision Farming
Applied AI in Agriculture Market - Regional Outlook
-
North America
-
US
-
Canada
-
Mexico
-
-
Europe
-
Germany
-
France
-
UK
-
Italy
-
Spain
-
Rest of Europe
-
-
Asia-Pacific
-
China
-
Japan
-
India
-
Australia
-
South Korea
-
Australia
-
Rest of Asia-Pacific
-
-
Middle East & Africa
-
UAE
-
Saudi Arabia
-
South Africa
-
Rest of MEA
-
-
South America
-
Brazil
-
Argentina
-
Rest of South America
-
Leading companies partner with us for data-driven Insights
Kindly complete the form below to receive a free sample of this Report
Tailored for You
- Dedicated Research on any specifics segment or region.
- Focused Research on specific players in the market.
- Custom Report based only on your requirements.
- Flexibility to add or subtract any chapter in the study.
- Historic data from 2014 and forecasts outlook till 2040.
- Flexibility of providing data/insights in formats (PDF, PPT, Excel).
- Provide cross segmentation in applicable scenario/markets.
