Press release
Top Factor Driving Applied AI In Agriculture Market Growth in 2025: Increasing Crop Productivity As A Key Driver For The Growth Of Applied AI In Agriculture Market
Applied AI In Agriculture
The growth of the applied AI in agriculture market is anticipated to be driven by the rising crop productivity. Crop productivity, which refers to the quantity of crops produced per unit area, is seeing an upwards trend due to enhancements in agricultural technology and techniques such as advanced crop breeds, effective watering systems, and the usage of precision farming strategies. Applied AI in agriculture aids in boosting crop productivity by refining farming practices using data-driven findings, predictive analysis, and automation. For example, as per the data from the United States Department of Agriculture in January 2024, a US federal executive department that is in charge of developing and implementing federal regulations, the average yield for all United States rice witnessed an increment of 264 pounds per acre from the 2022 average yield of 7,385 pounds per acre, reaching 7,649 pounds per acre in 2023. As a result, the applied AI in agriculture market is escalating due to the increasing crop productivity.
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How will the growth rate of the applied ai in agriculture market shape industry trends by 2034?
The market size of applied AI in agriculture has witnessed a tremendous explosion in growth recently. The market, expected to escalate from $2.89 billion in 2024 to $3.75 billion in 2025, is set to experience a compound annual growth rate (CAGR) of 29.7%. The noticeable expansion during the historic period could largely be credited to factors such as increased data access and collection, advances in technology, labor deficits, the impact of climate change and sustainability, and environmental issues.
Expect to observe a substantial surge in the applied ai in agriculture market size in the forthcoming years, with projections to reach $10.5 billion in 2029, alongside a compound annual growth rate (CAGR) of 29.4%. This growth during the projection period can be linked to various factors like a rising inclination towards precision farming, government-led initiatives and subsidies, an escalating demand for food security, financial involvement in agritech startups, and data-led decision-making systems. Noteworthy trends in the prediction period consist of development in precision agriculture, AI-supported predictive analytics, mechanization and robotics, intelligent irrigation setups, and AI-improved crop breeding methods.
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What trends are poised to drive the future success of the applied ai in agriculture market?
Leading businesses in the applied AI in agriculture sector are focusing more on creating innovative offerings like AI-based tools, to maintain their standing in the market. The term AI-based tools involves software and technologies that leverage artificial intelligence to improve several aspects of farming and agricultural practices. As an example, in July 2024, Google LLC, an American technology company, launched a novel AI-based tool known as Agricultural Landscape Understanding (ALU) to improve agricultural practices in India, particularly around drought preparation and irrigation management. This tool, utilizing high-resolution satellite imagery and machine-learning, generates tailored insights for each individual farm field, catering to the unique requirements of India's diverse agricultural landscape. With defining precise field boundaries as one of its features, the ALU tool can assess various elements such as the type of crop, the size of the field, and its proximity to water sources - factors vital for effective irrigation and drought management plans. The aim of this project is to enable farmers by enhancing crop yields, simplifying access to capital, and broadening market reach for agricultural commodities.
Which primary segments of the applied ai in agriculture market are driving growth and industry transformations?
The applied ai in agriculturemarket 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
Subsegments:
1) By Hardware: Sensors (Soil, Climate, Crop Monitoring), Drones For Crop Surveillance, Automated Tractors And Harvesters, GPS & GIS Systems For Precision Farming, Imaging Systems, IoT Devices For Agricultural Monitoring, Robotics For Seeding, Planting, And Weeding
2) By Software: Farm Management Software, AI-Based Crop Prediction Software, Precision Agriculture Software, Irrigation Management Software, Pest And Disease Detection Software, Data Analytics And Visualization Tools, Supply Chain Optimization Software, Machine Learning Algorithms For Yield Prediction
3) By Service: AI Integration And Implementation Services, Data Analytics Services For Crop And Soil Analysis, Cloud-Based Agricultural Services, AI Model Training And Customization Services, Maintenance And Support Services, Consultation And Advisory Services For AI Adoption In Agriculture
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Which geographical regions are pioneering growth in the applied ai in agriculture market?
North America was the largest region in the applied AI in agriculture market in 2024. 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.
Who are the influential players reshaping the applied ai in agriculture market landscape?
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
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What Is Covered In The Applied AI In Agriculture Global Market Report?
•Market Size Forecast: Examine the applied ai in agriculture market size across key regions, countries, product categories, and applications.
•Segmentation Insights: Identify and classify subsegments within the applied ai in agriculture market for a structured understanding.
•Key Players Overview: Analyze major players in the applied ai in agriculture market, including their market value, share, and competitive positioning.
•Growth Trends Exploration: Assess individual growth patterns and future opportunities in the applied ai in agriculture market.
•Segment Contributions: Evaluate how different segments drive overall growth in the applied ai in agriculture market.
•Growth Factors: Highlight key drivers and opportunities influencing the expansion of the applied ai in agriculture market.
•Industry Challenges: Identify potential risks and obstacles affecting the applied ai in agriculture market.
•Competitive Landscape: Review strategic developments in the applied ai in agriculture market, including expansions, agreements, and new product launches.
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