Sarmad Ahmad
Papers
1
Total Citations
3
H-Index
1
About
Dr. Sarmad Ahmad is a leading researcher at the intersection of computer vision and smart agriculture, with a primary focus on developing robust, real-time object detection systems for automated fruit recognition and yield estimation. His most influential work, "Performance Evaluation of Modern Object Detection Models for Automated Fruit Recognition in Smart Agriculture" (2025, 3 citations), provides a critical benchmark of five state-of-the-art detection frameworks—including YOLO and Faster R-CNN—on a merged dataset of ten common fruit classes. By systematically addressing the challenge of data scarcity and model generalization in agricultural settings, Dr. Ahmad’s research directly supports advancements in robotic harvesting and precision yield mapping. His contributions are foundational for deploying AI-driven solutions that improve efficiency and accuracy in modern farming practices. With a growing citation record and a focus on bridging the gap between cutting-edge deep learning and practical agricultural challenges, Dr. Ahmad is establishing himself as a key voice in the field of smart agriculture and automated visual inspection.
Research Focus
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Top Papers
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