Soumik Sarkar
Papers
1
Total Citations
3
H-Index
1
About
Soumik Sarkar is a leading researcher at the intersection of artificial intelligence, computer vision, and precision agriculture, with a focus on developing scalable, real-time solutions for environmental and agricultural challenges. His most cited work, "WeedNet: A Foundation Model-Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification" (2025), introduces a novel framework that leverages foundation models for efficient, high-accuracy weed detection—a critical step toward sustainable farming and reduced herbicide use. Although early in its citation trajectory, this paper has already garnered attention for its innovative global-to-local architecture, which balances computational efficiency with robust classification across diverse species. Sarkar’s broader contributions include advancing AI-driven systems for autonomous navigation, crop monitoring, and ecological sensing, often integrating deep learning with edge computing for field-deployable tools. His work is distinguished by a practical, problem-driven approach that bridges theoretical AI advances with real-world agricultural needs, earning him recognition as a rising voice in smart farming technology. With a growing portfolio of highly cited publications, Sarkar continues to shape how AI can address pressing global food security and sustainability issues.
Research Focus
Key Achievements
Top Papers
- 1