Research Approach
Our experimentation phase combines controlled field trials with real-world farm testing to validate AI recommendations.
Experimental plots are carefully designed to evaluate the impact of different irrigation strategies, nitrogen application rates, and monitoring frequencies on crop performance.
High-resolution drone imagery, combined with ground-based sensor networks, provides the data foundation for training and validating our machine learning models.
Experimentation Timeline
Key milestones in our field experimentation journey.
Field Preparation
Initial soil analysis and plot demarcation at Çukurova University, Turkey.
Sensor Deployment
Installation of IoT sensors for soil moisture, temperature, and nutrient monitoring.
First Drone Flights
Baseline multispectral imagery captured across all 54 experimental plots.
Growing Season Monitoring
5 drone flights with 10 multispectral bands, producing 2,700 images throughout the crop growth cycle.
Harvest Data Collection
Yield measurements and quality assessments across all experimental treatments.
Data Analysis & Model Training
Processing field data to train and validate AI models for crop prediction.
Experimental Variables
Key factors being tested across our experimental plots.
Irrigation Levels
Three irrigation treatments: 0%, 50%, and 100% of crop water requirements (100% being ideal)
Nitrogen Rates
Three nitrogen application rates tested: 0, 10, and 20 g/m² (20 being ideal)
Monitoring Frequency
Drone imagery captured at different intervals to optimize survey scheduling
Field Gallery
Images from our experimental sites and field work activities.
Challenges & Learnings
Key insights gained from our field experimentation activities.
Challenge: Weather Variability
Learning: Developed robust models that account for inter-annual climate variations
Challenge: Sensor Calibration
Learning: Established standardized protocols for consistent data quality across sites
Challenge: Data Integration
Learning: Created unified data pipeline for heterogeneous sensor and imagery data
Challenge: Scale Differences
Learning: Validated model transferability across different farm sizes and regions
Explore More
Learn about specific field studies and meet the partners conducting this research.