Papers
19
Total Citations
332
H-Index
9
About
Muhammad Tahir Khan is a researcher whose work spans precision agriculture, robotics, and artificial intelligence, with a particular focus on applying deep learning and computer vision to real-world autonomous systems. He has made significant contributions to the field of agricultural robotics, pioneering vision-based weed and crop identification systems that enable selective agrochemical spraying — a breakthrough approach that reduces chemical waste, protects the environment, and improves farming economics. His most influential work, "Deep Learning-Based Identification System of Weeds and Crops in Strawberry and Pea Fields" (2021), has accumulated over 160 citations, establishing him as a leading voice in precision agriculture automation. Beyond agriculture, Khan has built a strong foundation in mobile robotics, contributing notable research on fault detection and isolation using multi-sensor fusion, visual servo control, and multi-robot cooperation and task allocation. His early work on market-based multi-robot systems and autonomous manipulation laid the groundwork for his later applied research. With a cumulative body of work exceeding 299 citations, Khan's research bridges theoretical robotics with practical agricultural challenges, making his contributions especially relevant for researchers working at the intersection of AI, autonomous systems, and sustainable farming.
Research Focus
Key Achievements
Top Papers
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- 4Fault detection in mobile robots using sensor fusion20 citations · 2015
- 5Application of Visual Servo Control in Autonomous Mobile Rescue Robots12 citations · 2016
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- 8Developments in Visual Servoing for Mobile Manipulation10 citations · 2013
- 9Autonomous market-based multi-robot cooperation9 citations · 2010
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