Papers
12
Total Citations
302
H-Index
8
About
Dawood Ahmed is a leading researcher in agricultural robotics and computer vision, whose work is transforming how orchards are managed through automation. His primary research areas include instance segmentation for complex orchard environments, precision pruning, and machine vision for crop-load estimation. Ahmed’s most impactful contribution is his 2024 study comparing YOLOv8 and Mask R-CNN for instance segmentation, which has garnered 172 citations and provides a foundational framework for automating tasks like selective harvesting and precision pruning. He has also developed a semiautonomous precision pruning system for upright fruiting offshoot orchards, integrating robust perception and manipulation technologies. His work on early-stage apple flower detection using machine vision supports precision thinning and pollination, addressing critical labor shortages in fruit production. Additionally, Ahmed has explored robotic pollination as a sustainable alternative to traditional methods, highlighting its potential amid climate change. With over 290 total citations across his top papers, Ahmed’s research is pivotal in advancing agricultural robotics, offering scalable solutions to enhance efficiency and sustainability in modern farming.
Research Focus
Key Achievements
Top Papers
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- 5Machine Vision-Based Crop-Load Estimation Using YOLOv817 citations · 2023
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- 7An autonomous robot for pruning modern, planar fruit trees12 citations · 2022
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- 10Robotics for crop pollination: recent advances and future direction3 citations · 2024