Meetha V. Shenoy
Papers
2
Total Citations
18
H-Index
2
About
Dr. Meetha V. Shenoy is a pioneering researcher at the intersection of agricultural automation and privacy-preserving artificial intelligence. Her work spans two transformative domains: precision agriculture using computer vision and secure federated learning for IoT systems. In agricultural technology, Dr. Shenoy developed a novel hybrid consensus and recovery block-based detection system using RGB-D sensors for identifying ripe coffee cherry bunches. This breakthrough addresses the critical challenge of selective harvesting for small, clustered fruits, achieving 14 citations for its practical impact on automated harvesting systems. More recently, Dr. Shenoy introduced HAFedL, a Hessian-aware adaptive privacy-preserving horizontal federated learning scheme designed specifically for IoT applications. This 2024 work, already garnering 4 citations, tackles the dual challenges of communication efficiency and data privacy in distributed machine learning environments. By incorporating second-order optimization insights, HAFedL represents a significant advancement in making federated learning both more secure and computationally practical for resource-constrained IoT devices. Dr. Shenoy’s research demonstrates a rare ability to bridge physical sensing with algorithmic privacy, creating solutions that are both theoretically rigorous and deployment-ready.
Research Focus
Key Achievements
Top Papers
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