Sana Shabbir

Papers

Total Citations

106

H-Index

About

Sana Shabbir is a leading researcher in agricultural automation and computer vision, with a primary focus on developing intelligent systems for fruit detection and harvesting. Her most cited work, "Mature Tomato Fruit Detection Algorithm Based on improved HSV and Watershed Algorithm" (2018, 106 citations), addresses the critical challenge of automating tomato picking—a task traditionally reliant on labor-intensive manual methods. By enhancing the HSV color space and integrating the Watershed algorithm, Shabbir's approach enables precise, real-time identification of mature tomatoes, significantly improving the efficiency and accuracy of robotic harvesting systems. This contribution directly supports the agricultural industry's shift toward automation, reducing labor costs and time while ensuring optimal fruit quality. Her research has garnered widespread attention, with over 100 citations reflecting its practical impact on precision agriculture and food production. Shabbir's work exemplifies how computer vision can transform traditional farming practices, offering scalable solutions for global food security. Her innovative algorithm remains a foundational reference for researchers developing autonomous harvesting technologies, cementing her role as a key contributor to the intersection of machine vision and sustainable agriculture.