Naseeb Singh
Papers
5
Total Citations
114
H-Index
5
About
Naseeb Singh is a leading researcher in agricultural robotics, specializing in the development of intelligent systems for precision cotton harvesting. His work centers on applying deep learning and computer vision to address critical challenges in manual and mechanical cotton picking. Singh’s major contributions include pioneering semantic segmentation of in-field cotton bolls using deep convolutional neural networks, achieving high-accuracy detection under natural lighting conditions. His most-cited paper (32 citations) demonstrates how drone-based imaging and AI can enable selective, robotic harvesting—preserving fiber quality while reducing labor costs and harvest losses. He further advanced the field by designing lightweight CNN models for real-time boll segmentation and optimizing robotic manipulators through evolutionary algorithms and neural networks for inverse kinematics. His 2024 study on in-field performance evaluation of a robotic arm marks a key step toward practical deployment. With over 100 total citations across his core publications, Singh’s work directly addresses the looming shortage of farm labor in developing nations, offering a scalable, cost-effective path to automated cotton harvesting that combines the benefits of manual selectivity with mechanical efficiency.
Research Focus
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