About the Author¶
Abdelrahman Saleh¶
Abdelrahman Saleh is an interdisciplinary PhD researcher at the University of Manitoba, affiliated with the Digital Agroecosystem Lab in the Department of Soil Science. His work brings together software engineering, machine learning, remote sensing, and crop modelling to develop practical digital solutions for modern agriculture.
Abdelrahman holds a Bachelor’s degree in Agricultural Engineering and a Master’s degree in Biosystems Engineering, where his research focused on applying deep learning techniques to sustainable water management. His academic and research journey has equipped him with strong expertise in data-driven modelling, agricultural decision-support systems, and computational tools for environmental and climate-related applications.
His work spans a wide range of areas, including crop yield forecasting, irrigation optimization, climate data analysis, geospatial analytics, and AI-driven agricultural modelling. He has contributed to international research initiatives such as CY-Bench, a benchmark dataset for subnational crop yield forecasting, and has led research on using deep reinforcement learning to optimize irrigation scheduling for tomatoes under real climate conditions.
Abdelrahman is the creator and developer of EarthStat, an open-source Python geospatial toolkit designed to process large-scale raster, remote-sensing, and climate datasets efficiently. EarthStat helps overcome major bottlenecks in geospatial analytics by combining automated preprocessing, compatibility checking, and parallel computation, making it easier to extract high-quality data for machine learning and deep learning applications.
Abdelrahman has also worked with Planet Labs PBC through Mitacs, where he contributed to building educational and technical resources for Tanager Mission. This work included developing two resources: TanagerSpec, a software package designed to support hyperspectral data analysis workflows, as well as creating course materials to help learners understand and analyze hyperspectral data.
Through his teaching, research, and open-source work, Abdelrahman aims to make advanced computational methods more accessible and useful for researchers, students, and practitioners working in digital agriculture, environmental modelling, and remote sensing.