The research focuses on developing a decision support tool for optimal irrigation management in winter wheat by integrating remote sensing, UAV and satellite imagery, soil measurements, and mechanistic crop models. The project will develop machine learning and regression models for monitoring LAI, biomass, chlorophyll, and Crop Water Stress Index (CWSI) using thermal, multispectral, RGB, and LiDAR sensing. The study combines controlled irrigation experiments and large-scale commercial field trials across Israel using Sentinel-2, Google Earth Engine, and yield maps to advance smart irrigation, yield prediction, in wheat.
Requirements:
For M.Sc. graduates with a B.Sc. or M.Sc. degree in Agriculture, Geography, Computer Science, Industrial Engineering and Management, or related fields, with a strong passion for research, good interpersonal skills, ready for field work, and the ability for independent learning.
The thesis will be part of a large interdisciplinary research initiative on irrigation management in wheat for increasing productivity, led by Dr. Roey Ben-David and involving researchers in soil and water sciences, plant physiology, remote sensing, and modeling. The student will be jointly supervised by Dr. Itay Herman from the Faculty of Agriculture and Dr. Yafit Cohen. Students in the GeoAg Research Lab will gain expertise in remote sensing, precision agriculture, and agricultural modeling for crop monitoring and irrigation management, including hands-on experience with UAVs, multispectral and thermal imaging, LiDAR, satellite imagery (Sentinel-2), Google Earth Engine, GIS, and Machine Learning–based analysis. The research combines fieldwork, advanced data analysis, and the development of decision-support tools for smart agriculture and water management.
See yourself fit? send your CV to Dr. Yafit Cohen via yafitush@volcani.agri.gov.il