N. Renuka
Papers
1
Total Citations
3
H-Index
1
About
N. Renuka is a researcher specializing in computational agriculture and multimodal data analysis, with a focus on leveraging machine learning for food quality assessment. Her most notable contribution is the development of an efficient framework to bifurcate healthy and diseased vegetables and fruits using a multimodal approach, published in 2021. This work integrates diverse data sources—such as visual, spectral, or textural features—to enhance the accuracy and speed of detecting spoilage or disease in produce, addressing critical challenges in food safety and supply chain management. While her citation count is currently modest (3 citations for this key paper), the framework represents a foundational step toward scalable, non-invasive screening technologies. Renuka’s research sits at the intersection of computer vision, agricultural engineering, and public health, offering practical solutions for reducing post-harvest losses and ensuring consumer safety. Her work is particularly relevant for students and researchers exploring applied AI in agriculture, as it demonstrates how multimodal fusion can overcome limitations of single-sensor systems. As the field of precision agriculture grows, Renuka’s contributions are poised to gain further recognition for their real-world impact on food quality control.
Research Focus
Key Achievements
Top Papers
- 1