Ayman M. El mesalami

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About

Ayman M. El Mesalami is a researcher specializing in agricultural robotics and computer vision, with a focus on automating plant phenotyping and greenhouse management. His key research areas include image processing, machine learning, and real-time detection systems for precision agriculture. His most notable contribution is the development of an automatic algorithm for detecting the main vine and branches of tomato plants grown in greenhouses, published in 2018. This work employs the Distance Regularized Level Set Evolution (DRLSE) algorithm to track plant structures in real time, using automatically located seeding points on the main vine. Although this paper has garnered 2 citations to date, it represents a foundational step toward non-invasive, automated monitoring of crop growth, which is critical for optimizing yield and resource use in controlled environments. El Mesalami’s research bridges the gap between computer vision and agricultural engineering, offering practical solutions for smart farming. His work is particularly relevant for students and researchers interested in applying deep learning and segmentation techniques to biological systems, and it holds promise for scaling to other crop types and field conditions.