Sammar Haggag

Papers

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About

Sammar Haggag is a researcher at the forefront of agricultural robotics and computer vision, with a focus on enabling automated harvesting systems. Her work addresses the critical challenge of developing robust machine vision for object detection in complex, real-world environments like tomato greenhouses. In her highly cited 2024 study, "Object Detection in Tomato Greenhouses: A Study on Model Generalization," she investigates how deep learning models can generalize across varying greenhouse conditions, a key hurdle for cost-effective harvesting robots. This work, which has already garnered 8 citations, demonstrates her ability to tackle practical, industry-relevant problems—specifically, the labour shortages and rising costs plaguing modern agriculture. By advancing model robustness and reducing the need for expensive, site-specific training data, Haggag’s contributions help bridge the gap between lab-based AI and field-ready automation. Her research not only pushes the boundaries of agricultural technology but also offers scalable solutions that could make robotic harvesting more accessible to growers worldwide.