The applied ai in agriculture global market report 2024 from The Business Research Company provides comprehensive market statistics, including global market size, regional shares, competitor market share, detailed segments, trends, and opportunities. This report offers an in-depth analysis of current and future industry scenarios, delivering a complete perspective for thriving in the industrial automation software market.
Applied AI In Agriculture Market, 2024 report by The Business Research Company offers comprehensive insights into the current state of the market and highlights future growth opportunities.
Market Size –
The applied AI in agriculture market size has grown exponentially in recent years. It will grow from $2.23 billion in 2023 to $2.89 billion in 2024 at a compound annual growth rate (CAGR) of 29.4%. The growth in the historic period can be attributed to data availability and collection, technological advancements, labor shortages, climate change and sustainability, and environmental concerns.
The applied AI in agriculture market size is expected to see exponential growth in the next few years. It will grow to $8.12 billion in 2028 at a compound annual growth rate (CAGR) of 29.5%. The growth in the forecast period can be attributed to increasing demand for precision farming, government initiatives, and subsidies, increasing demand for food security, investment in agritech startups, and data-driven decision-making. Major trends in the forecast period include precision agriculture growth, AI-powered predictive analytics, automation and robotics, smart irrigation systems, and AI-enhanced crop breeding.
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Scope Of Applied AI In Agriculture Market
The Business Research Company’s reports encompass a wide range of information, including:
1. Market Size (Historic and Forecast): Analysis of the market’s historical performance and projections for future growth.
2. Drivers: Examination of the key factors propelling market growth.
3. Trends: Identification of emerging trends and patterns shaping the market landscape.
4. Key Segments: Breakdown of the market into its primary segments and their respective performance.
5. Focus Regions and Geographies: Insight into the most critical regions and geographical areas influencing the market.
6. Macro Economic Factors: Assessment of broader economic elements impacting the market.
Applied AI In Agriculture Market Overview
Market Drivers –
The increasing crop productivity is expected to propel the growth of the applied AI in agriculture market going forward. Crop productivity refers to the measure of the output of crops, typically expressed in terms of yield per unit area of land. Crop productivity is rising due to advancements in agricultural technology and practices, such as improved crop varieties, efficient irrigation methods, and the use of precision farming techniques. Applied AI in agriculture enhances crop productivity by optimizing farming practices through data-driven insights, predictive analytics, and automation. For instance, in January 2024, according to the United States Department of Agriculture, a US-based federal executive department responsible for developing and executing federal laws, the average yield for all United States rice was estimated at 7,649 pounds per acre in 2023, up 264 pounds from the 2022 average yield of 7,385 pounds per acre. Therefore, increasing crop productivity is driving the applied AI in agriculture market.
Market Trends –
Major companies operating in the applied AI in agriculture market are increasing their focus on developing innovative solutions, such as AI-based tools, to sustain their position in the market. AI-based tools refer to software and technologies that utilize artificial intelligence to enhance various aspects of farming and agricultural practices. For instance, in July 2024, Google LLC, a US-based technology company, launched an innovative AI-based tool called Agricultural Landscape Understanding (ALU) to enhance agricultural practices in India, with a focus on drought preparedness and irrigation management. This tool uses high-resolution satellite imagery and machine learning to provide customized insights for individual farm fields, addressing the diverse needs of India’s agricultural landscape. By defining clear field boundaries, the ALU tool can analyze various factors such as crop type, field size, and proximity to water sources, which are crucial for effective irrigation and drought management strategies. The goal of this initiative is to empower farmers by improving crop yields, facilitating access to capital, and enhancing market access for agricultural products.
The applied ai in agriculture market covered in this report is segmented –
1) By Component: Hardware, Software, Service
2) By Technology: Machine Learning And Deep Learning, Predictive Analytics, Computer Vision
3) By Application: Precision Farming, Drone Analytics, Agriculture Robots, Livestock Monitoring, Other Applications
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Regional Insights –
North America was the largest region in the applied AI in agriculture market in 2023. The regions covered in the applied AI in agriculture market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
Key Companies –
Major companies operating in the applied AI in agriculture market are Microsoft Corporation, BASF SE, International Business Machines Corporation, Bayer AG, Deere & Company, SAP SE, CNH Industrial N.V., Kubota Corporation, Corteva Inc., AGCO Corporation, Trimble Inc., Raven Industries Inc., The Climate Corporation, AG Leader Technology, The BAE Systems Taranis, Farmers Edge Inc., PrecisionHawk, AgEagle Aerial Systems, Descartes Labs Inc., Prospera Technologies Ltd., Agribotix, Gamaya< /b>
Table of Contents
1. Executive Summary
2. Applied AI In Agriculture Market Report Structure
3. Applied AI In Agriculture Market Trends And Strategies
4. Applied AI In Agriculture Market – Macro Economic Scenario
5. Applied AI In Agriculture Market Size And Growth
…..
27. Applied AI In Agriculture Market Competitor Landscape And Company Profiles
28. Key Mergers And Acquisitions
29. Future Outlook and Potential Analysis
30. Appendix
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