Fiyona Shah
Papers
1
Total Citations
2
H-Index
1
About
Fiyona Shah is a rising researcher at the intersection of precision agriculture and artificial intelligence, with a core focus on leveraging IoT and machine learning to solve critical challenges in crop management. Her most-cited work, "A Study of Weed Management in Agricultural Plants using IoT and Machine Learning" (2024), introduces an innovative framework for automated weed detection and classification. By distinguishing between harmful and beneficial weed species, Shah’s approach directly addresses a major pain point for farmers: the need to reduce yield loss and production costs without resorting to blanket herbicide application. Though early in her career, her research has already garnered attention (2 citations), signaling its relevance to the growing field of smart farming. Shah’s contribution lies in bridging sensor-driven data collection with intelligent algorithms, offering a scalable, real-time solution for sustainable agriculture. Her work stands out for its practical orientation—aiming to empower farmers with precise, data-driven decisions. As the agricultural sector increasingly turns to digital tools, Fiyona Shah is poised to make a lasting impact on how we manage weeds, reduce waste, and boost crop productivity.
Research Focus
Key Achievements
Top Papers
- 1