I am a Professional Electrical Engineer and researcher specializing in digital agriculture, high-throughput field phenotyping, and applied artificial intelligence for crop improvement. My work integrates advanced sensing systems, scalable data engineering pipelines, and machine learning models to quantify complex agronomic traits across large-scale field environments.
I contribute to multi-environment phenomics initiatives focused on improving cereal crop productivity, strengthening climate resilience, and enabling data-driven breeding decisions. My research emphasizes multimodal sensing using UAV and UGV platforms, LiDAR-based structural phenotyping, and predictive modelling of yield and phenological traits.
High-throughput phenotyping
Agricultural AI & deep learning
Crop stress physiology
Spatial-temporal data analytics
Precision agriculture systems
UAV / UGV sensing platforms
LiDAR & thermal imaging
Python, Django, Flutter
HPC & cloud computing
Data engineering pipelines
Electrical engineering & automation
Industrial control systems (PLC / DCS)
Embedded monitoring solutions
Experimental design & statistics
Scientific computing & modeling
His long-term vision is to advance scalable digital agriculture frameworks that enable breeders and agronomists to make timely, evidence-based decisions. By integrating sensing technologies with predictive analytics, his work aims to enhance genetic gain, improve crop resilience under climate variability, and contribute toward sustainable food production systems.