Kamila Dilmurat

Kamila Dilmurat is a Postdoctoral Scholar at the School of Engineering, University of California Merced. Her research focuses on integrating remote sensing technologies, machine learning, and geospatial analytics to advance precision agriculture and environmental monitoring.

Her work emphasizes the use of Unmanned Aerial Vehicles (UAVs), satellite data, and deep learning algorithms for crop disease detection, yield prediction, and seed composition analysis. Key applications include wheat Fusarium head blight monitoring, sorghum irrigation treatment differentiation, and soybean yield estimation using multispectral and hyperspectral imagery.

Notable contributions include developing multitask learning frameworks for simultaneous biomass and nutrient estimation, assimilating UAV data into crop models like DSSAT, and creating high-throughput plant phenotyping systems using LiDAR and photogrammetry. Her research bridges environmental science, computer science, and agricultural engineering to enhance sustainable farming practices.

While no formal awards are listed, her publications reflect significant innovation in geospatial AI (GeoAI) and precision agriculture methodologies. She has collaborated on projects spanning crop water status monitoring, carbon capture process assessments, and multiscale remote sensing analysis.