Zohaib Khan is a dedicated researcher specializing in machine learning, object detection, and control science engineering, with a strong focus on precision agriculture and AI-driven automation. Currently pursuing a PhD at Jiangsu University, China, he has made significant contributions to deep learning-based agricultural robotics, publishing multiple first-author papers in high-impact SCI Q1, Q2, and EI journals. His work emphasizes real-time detection, optimization algorithms, and AI-driven sustainability solutions. With extensive mentoring experience (50+ Bachelor’s and 10 Master’s students), he has played a key role in academic development. Zohaib has received numerous national and international awards, including first prizes in elite research and innovation competitions. His technical expertise spans Python, MATLAB, LaTeX, and AI-driven modeling, complementing his ability to lead interdisciplinary research. With a passion for advancing AI applications in agriculture, he continues to drive innovation in sustainable and automated farming solutions.
Professional Profile
Google Scholar
Scopus Profile
ORCID Profile
Education
Zohaib Khan is currently pursuing a PhD in Control Science Engineering at Jiangsu University, China (2022–2026), specializing in machine learning and object detection. He previously earned an MSc in Electrical Engineering (2019–2022) from the same institution, focusing on power systems and renewable energy. His Bachelor’s degree in Electrical Power Engineering (2013–2017) from Swedish College of Engineering and Technology, Pakistan, laid the foundation for his technical expertise. His early academic years were marked by excellence, having completed Pre-Engineering at Fazaia Degree College (2011–2013) and his Secondary School Certificate (2009–2011) at Agricultural University Public School. Zohaib’s academic journey is distinguished by his strong analytical skills and passion for integrating AI and automation in engineering solutions. His education reflects a deep commitment to advanced research, innovation, and interdisciplinary problem-solving, positioning him as a future leader in AI-driven technologies and precision agriculture.
Professional Experience
Zohaib Khan has gained substantial experience in both academic research and engineering practice. As an intern at WAPDA, Pakistan, he developed hands-on expertise in power distribution and transmission lines, strengthening his understanding of grid operations and maintenance. Later, as an Electrical Engineer at LIMAK (JV) ZKB – CPEC Project (2017–2018), he contributed to electrical system design, installation, and maintenance, gaining valuable project management experience. His role involved troubleshooting, safety compliance, and interdisciplinary collaboration, enhancing his problem-solving capabilities. In academia, Zohaib has mentored over 50 Bachelor’s and 10 Master’s students, guiding them through research projects in machine learning, object detection, and automation. His strong writing, teaching, and IT skills have been instrumental in fostering innovation. His diverse experience, spanning applied research and engineering implementation, makes him a well-rounded professional capable of driving breakthroughs in AI-powered automation and precision agriculture.
Research Interest
Zohaib Khan’s research focuses on machine learning, deep learning, object detection, and AI-driven automation, with applications in precision agriculture and robotics. His studies revolve around real-time detection, optimization algorithms, and advanced control systems for agricultural sustainability and industrial automation. He has pioneered AI-driven precision farming techniques, developing deep learning-enhanced YOLOv7 and YOLOv8 algorithms for real-time crop health assessment and robotic spraying systems. Additionally, his work explores autonomous navigation in unstructured farmlands, energy-efficient control systems, and reinforcement learning for AI-based decision-making. His research extends to risk assessment in renewable energy systems, contributing to more efficient and resilient smart grids. Through interdisciplinary collaborations, Zohaib continues to push the boundaries of AI in sustainable agriculture, robotics, and industrial automation, aiming to develop intelligent, scalable, and high-impact solutions for modern technological challenges.
Awards and Honors
Zohaib Khan has received multiple prestigious awards recognizing his contributions to research, innovation, and academic excellence. He has won First Prizes in National Competitions, including the China University Business Elite Challenge (2024) and the Brand Planning Competition (2024). His research excellence was acknowledged with the Excellent Paper Award at the Sino-award (2021) and special recognition in Jiangsu Province Graduate Energy-saving and Low-Carbon Research Competition (2023). Additionally, he was honored as an Outstanding Student in the 17th “Yale School of Jiangsu University” program and received a Certificate of Excellence for Teaching Assistance. His leadership and public speaking skills earned him first place in an English debate at Jiangsu University. These accolades reflect his dedication to research, leadership in innovation, and commitment to advancing AI applications in engineering and agriculture, solidifying his reputation as a promising researcher in his field.
Conclusion
Zohaib Khan’s academic, professional, and research journey showcases his exceptional talent in AI-driven automation, machine learning, and precision agriculture. His extensive experience in research, mentoring, and engineering practice positions him as a leading scholar in intelligent agricultural robotics and sustainable AI applications. With a strong publication record in high-impact journals (SCI Q1, Q2, and EI) and multiple national and international awards, he has demonstrated his ability to drive innovation and solve real-world problems. His work in deep learning-based automation and AI-driven optimization techniques continues to push the boundaries of technology for sustainability and efficiency. As he progresses in his career, Zohaib remains committed to advancing cutting-edge research, fostering academic collaborations, and contributing transformative solutions in AI, robotics, and smart energy systems. His dedication and achievements make him a strong candidate for prestigious research awards and a key contributor to the future of AI in engineering and agriculture.
Publications Top Noted
-
Title: A Composite Sliding Mode Controller with Extended Disturbance Observer for 4WSS Agricultural Robots in Unstructured Farmlands
Authors: Yafei Zhang, Yue Shen, Hui Liu, Siwei He, Zohaib Khan
Year: 2025
Source: Computers and Electronics in Agriculture
DOI: 10.1016/j.compag.2025.110069
-
Title: Optimizing Precision Agriculture: A Real-Time Detection Approach for Grape Vineyard Unhealthy Leaves Using Deep Learning Improved YOLOv7 with Feature Extraction Capabilities
Authors: Zohaib Khan, Hui Liu, Yue Shen, Zhaofeng Yang, Lanke Zhang, Feng Yang
Year: 2025
Source: Computers and Electronics in Agriculture
DOI: 10.1016/j.compag.2025.109969
-
Title: A Single-Stage Navigation Path Extraction Network for Agricultural Robots in Orchards
Authors: Hui Liu, Xiao Zeng, Yue Shen, Jie Xu, Zohaib Khan
Year: 2025
Source: Computers and Electronics in Agriculture
DOI: 10.1016/j.compag.2024.109687
-
Title: Optimization of Improved YOLOv8 for Precision Tomato Leaf Disease Detection in Sustainable Agriculture
Authors: Yue Shen, Zhaofeng Yang, Zohaib Khan, Hui Liu, Wenhua Chen, Shuyang Duan
Year: 2025
Source: Sensors
DOI: 10.3390/s25051398
-
Title: Deep Learning Improved YOLOv8 Algorithm: Real-Time Precise Instance Segmentation of Crown Region Orchard Canopies in Natural Environment
Authors: Zohaib Khan, Hui Liu, Yue Shen, Xiao Zeng
Year: 2024
Source: Computers and Electronics in Agriculture
DOI: 10.1016/j.compag.2024.109168
Citations: 7
-
Title: Effects of Optimization on User-Based Charging/Discharging Control Strategy
Authors: Zohaib Khan, Y. Wang
Year: 2022
Source: Recent Advances in Electrical and Electronic Engineering
DOI: 10.2174/2215083808666220324144603
Citations: 2