Papers
1
Total Citations
3
H-Index
1
About
Ayesha Zeb is a rising researcher at the forefront of applying computer vision to smart agriculture, with a particular focus on automated fruit recognition and object detection. Her most-cited work, "Performance Evaluation of Modern Object Detection Models for Automated Fruit Recognition in Smart Agriculture" (2025), systematically benchmarks five state-of-the-art frameworks on a merged dataset of ten common fruit classes, addressing the critical challenge of data scarcity in agricultural AI. This study provides a practical roadmap for deploying accurate, real-time detection systems essential for yield mapping and robotic harvesting. While her citation count is still growing—reflecting the recency of her contributions—her work is already shaping how modern object detection models are evaluated for real-world agricultural tasks. Zeb’s research bridges the gap between cutting-edge computer vision and the pressing needs of precision farming, offering scalable solutions that could transform crop monitoring and autonomous harvesting. As her publication record expands, she is poised to become a key voice in the intersection of deep learning and sustainable agriculture.
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Top Papers
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