Papers
15
Total Citations
1,041
H-Index
10
About
Yaqoob Majeed is a prominent researcher specializing in agricultural robotics, computer vision, and precision horticulture, with a particular focus on automating fruit detection, harvesting, and orchard management. His work sits at the intersection of deep learning and agricultural engineering, developing intelligent systems that address critical labor challenges in modern farming. Majeed's most influential contributions center on applying convolutional neural networks—particularly Faster R-CNN architectures—to detect and localize fruits in complex field environments. His 2020 paper on multi-class apple detection in SNAP systems has garnered over 326 citations, while companion work integrating RGB and depth features for robotic apple harvesting has accumulated 217 citations, reflecting the field's appetite for practical, deployable solutions. His early kiwifruit detection system (2018, 133 citations) demonstrated remarkable robustness across varying lighting conditions, establishing a foundation for subsequent orchard automation research. Beyond fruit detection, Majeed has made significant contributions to automated tree training, vine management, and hydroponic crop monitoring, showcasing the breadth of his expertise. His more recent investigations into stereo vision-based fruit localization and biomechanical fruit detachment forces reflect a maturing research agenda that bridges perception systems with physical robotic harvesting requirements—work increasingly essential as agriculture confronts intensifying labor shortages worldwide.
Research Focus
Key Achievements
Top Papers
- 1Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN326 citations · 2020
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- 4Kiwifruit detection in field images using Faster R-CNN with ZFNet133 citations · 2018
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