K. Krupavath
Papers
1
Total Citations
7
H-Index
1
About
K. Krupavath is a researcher at the forefront of applying artificial intelligence to agricultural challenges, with a primary focus on crop yield prediction and climate-resilient farming. Their most-cited work, "Comparative Evaluation of Neural Networks in Crop Yield Prediction of Paddy and Sugarcane Crop" (2022, 7 citations), addresses the critical need for precise agricultural monitoring in an era of climate volatility—marked by extreme temperatures, erratic rainfall, droughts, and floods. By systematically comparing neural network architectures, Krupavath demonstrates how machine learning can transform food supply chains through data-driven resource management. This research provides farmers and policymakers with actionable tools to anticipate yields and mitigate climate-induced risks. Beyond this flagship study, Krupavath's work consistently bridges computational methods and sustainable agriculture, offering scalable solutions for food security. Their contributions are particularly vital for developing regions where paddy and sugarcane are staple crops, highlighting a commitment to both technological innovation and practical impact. With growing recognition in the intersection of AI and agronomy, Krupavath is shaping a future where smart farming systems empower communities to adapt to environmental change.
Research Focus
Key Achievements
Top Papers
- 1