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About

Ahmad Ostovar is a robotics researcher whose work sits at the intersection of agricultural and forestry automation, with a primary focus on computer vision and object detection for autonomous field robots. His most impactful contribution, part of the H2020 SWEEPER project, is an adaptive image thresholding method for detecting yellow peppers, enabling visual servoing for a sweet pepper harvesting robot (24 citations). This work addresses the critical challenge of accurate and stable fruit detection in greenhouse environments. Earlier in his career, Ostovar developed a quality-guided image segmentation approach for tree detection (6 citations), essential for collision avoidance and autonomous navigation in forestry. He also explored the integration of Kinect depth data with stochastic classification frameworks to distinguish between bushes, trees, stones, and humans in forest environments (3 citations). His master’s thesis further advanced forestry object detection by enhancing feature extraction methods (2 citations). Across these projects, Ostovar demonstrates a consistent commitment to improving the perception capabilities of autonomous robots operating in unstructured, natural environments—from greenhouses to forests—laying groundwork for more reliable agricultural and forestry robotics.