Sunoj Shajahan

Sunoj Shajahan is a research associate with the Nutrient Management Spear Program (NMSP). Sunoj joined in 2019 and has been involved in precision agriculture projects at NMSP. He is developing an alternative approach to estimate and predict yield (corn grain and silage) using a time-series drone and satellite imagery. Along with these imageries, he is also interested in understanding the interaction between other data layers such as weather, topography, and landforms, with the yield monitor data. His goal is to develop farm-specific yield models with the use of multi-year yield monitor data and remotely sensed data layers. He is exploring big data analytics using cloud computing to analyze and build such models.

Research

My research focuses on using advanced data analytics (machine learning and deep learning) on multi-source geospatial data and developing data-driven decision support tools for agriculture in near real-time. I have been fascinated and involved in using free and open-source software (FOSS) platforms for geospatial data analysis and tool development.