Gergely Teschner
Papers
4
Total Citations
41
H-Index
4
About
Gergely Teschner is a leading researcher at the intersection of agricultural robotics, computer vision, and sustainable digital farming. His work focuses on developing intelligent systems that enhance precision agriculture, from automated weed detection to yield estimation. Teschner’s most cited paper, "Weed Detection and Classification with Computer Vision Using a Limited Image Dataset" (2024, 17 citations), addresses the critical challenge of deploying CNN-based deep learning in resource-constrained agricultural environments, enabling more efficient weeding and harvesting robots. He further advanced field robotics with his study on "Field-grown tomato yield estimation using point cloud segmentation with 3D shaping and RGB pictures" (2024, 11 citations), introducing a novel method that combines robot-captured images and 3D scanning for accurate yield prediction. Teschner also explores broader systemic innovations, such as drone-based protection systems for agricultural areas and the integration of IoT for ecocentric sustainable development. His work not only pushes the boundaries of computer vision in agriculture but also emphasizes the ethical and environmental dimensions of technology, making him a key voice in the transition toward data-driven, regenerative farming practices.
Research Focus
Key Achievements
Top Papers
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- 3Digital Twin of Drone-based Protection of Agricultural Areas8 citations · 2022
- 4