Arshleen Kaur
Papers
1
Total Citations
4
H-Index
1
About
Arshleen Kaur is a rising researcher in applied deep learning, with a primary focus on agricultural technology and computer vision. Her most-cited work, "An Effective Pistachio Classification by Ensembling Fine-tuned ResNet20 and DenseNet Models" (2024), tackles the critical challenge of automating agricultural sorting processes. By developing an ensemble model that combines fine-tuned ResNet20 and DenseNet architectures, Kaur addresses the inherent subjectivity and inconsistency of manual classification—a problem that directly impacts productivity and precision in the food industry. This contribution demonstrates her ability to bridge state-of-the-art neural network techniques with real-world agricultural needs, offering a scalable solution to reduce human error in quality control. While her citation count is currently modest at 4, the work’s timeliness and practical relevance position it as a foundational step in precision agriculture. Kaur’s research exemplifies how deep learning can transform traditional manual tasks, and her focus on ensemble methods highlights a commitment to robust, high-accuracy models. As her career progresses, she is poised to make further strides in applying AI to optimize food sorting and classification systems.
Research Focus
Key Achievements
Top Papers
- 1