Sundas Naqeeb Khan
Papers
1
Total Citations
12
H-Index
1
About
Sundas Naqeeb Khan is a researcher at the forefront of applying deep learning to precision agriculture, with a primary focus on developing intelligent, real-time systems for crop and weed classification. Her most impactful work, "Real-Time Wheat Classification System for Selective Herbicides Using Broad Wheat Estimation in Deep Neural Network" (2019), addresses a critical bottleneck in sustainable farming: the labor-intensive and time-consuming process of manual seed identification. By engineering a deep neural network capable of broad wheat estimation, Khan’s system enables automated, real-time discrimination between wheat and weeds, paving the way for selective herbicide application. This contribution directly supports the goals of reducing chemical overuse and promoting environmentally friendly agricultural practices. With 12 citations, this foundational paper demonstrates her ability to translate complex computational models into practical, economically viable tools for the agricultural industry. Khan’s research sits at the intersection of big data analytics, computer vision, and agronomy, showcasing a commitment to solving real-world problems through innovative, data-driven solutions. Her work is particularly valuable for students and researchers exploring the deployment of deep learning in resource-constrained, field-based environments.
Research Focus
Key Achievements
Top Papers
- 1