Infinium Global Research analyzes the integration of AI technologies—including machine learning, computer vision, and predictive analytics—into agriculture to optimize planting, irrigation, and harvesting. The report projects market value rising from USD 852.15 million to USD 5,390.14 million by 2030, driven by sustainability demands, resource scarcity, and regional adoption trends in North America and Asia-Pacific.
Key points
Projects market growth from USD 852.15 million in 2022 to USD 5,390.14 million by 2030 at a 22.40% CAGR.
Highlights leading segments: AI-driven precision farming systems and drone analytics for resource optimization.
Details regional insights: North America leads with advanced tech ecosystems; Asia-Pacific shows fastest adoption rates.
Q&A
What is CAGR?
What does AI-as-a-Service entail?
How do drone analytics improve crop monitoring?
What is predictive analytics in agriculture?
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Academy
Precision Farming
Precision farming, also called precision agriculture, leverages data analytics and automated equipment to optimize crop production and resource use. By collecting and analyzing field data—such as soil moisture, nutrient levels, and plant health—farmers can make targeted decisions on when and where to plant, irrigate, fertilize, and harvest.
- Data collection: Sensors, satellites, and drones gather high-resolution information on soil properties, weather, and crop status.
- Decision support: Machine learning algorithms process data to generate actionable recommendations for irrigation scheduling, fertilizer application, and pest control.
- Actuation: Automated machinery and variable-rate technology adjust inputs in real time to match specific field conditions.
Precision farming improves yields, reduces input costs, conserves water and nutrients, and lowers environmental impact by applying resources only where needed.
Drone Analytics in Agriculture
Drone analytics combine unmanned aerial vehicles (UAVs) with advanced imaging and computer vision to monitor crops and field conditions. Equipped with multispectral, thermal, or RGB cameras, drones capture detailed aerial imagery that is processed to identify crop stress, disease, and growth patterns.
- Flight planning: Predefined waypoints ensure consistent coverage and optimal image overlap.
- Image capture: Sensors record spectral data that reflect plant health indicators, such as chlorophyll content and water stress.
- Image processing: Software stitches images into orthomosaic maps and applies machine learning models to highlight anomalies and performance metrics.
- Actionable insights: Visual maps and reports help farmers pinpoint areas needing targeted treatments, reducing waste and labor.
Drone analytics enable rapid, noninvasive crop assessment over large areas, supporting proactive management and enhancing yield prediction accuracy.
Benefits of integrating precision farming and drone analytics include:
- Enhanced resource efficiency by directing water and inputs only to areas that need them
- Early detection of crop issues, reducing yield losses and pesticide use
- Improved decision-making through real-time field intelligence and predictive modeling
Together, these digital technologies drive sustainable agricultural practices, boost productivity, and support global food security challenges.
