Roop Raj
Papers
1
Total Citations
5
H-Index
1
About
Roop Raj is a researcher at the forefront of applying deep learning to address critical challenges in smart agriculture and climate-resilient farming. His work centers on developing advanced computational models that integrate climatic conditions with agricultural data to enhance crop yield forecasting. In his most-cited study, "Climatic Condition–Based Comparative Study of Deep Learning Models for Yield Forecasting in Smart Agriculture" (2025), Raj systematically evaluates various deep learning architectures—such as LSTMs and CNNs—against traditional methods, demonstrating how climate variables can significantly improve prediction accuracy. This contribution is vital for farmers and policymakers seeking to adapt to changing weather patterns and optimize food production. With 5 citations already, his research is gaining traction among scholars in precision agriculture and environmental computing. Raj’s work stands out for its practical focus on bridging the gap between complex AI models and real-world agricultural decision-making, offering a data-driven pathway to more sustainable and efficient farming systems. His efforts highlight the transformative potential of machine learning in ensuring global food security under climate uncertainty.
Research Focus
Key Achievements
Top Papers
- 1