Tanya Makkar
Papers
1
Total Citations
13
H-Index
1
About
Tanya Makkar is a researcher whose work sits at the intersection of artificial intelligence and agricultural technology, with a particular focus on applying neural networks to improve food quality assessment. Her most-cited paper, "Analysis and Detection of Fruit Defect Using Neural Network" (2018), has garnered 13 citations and represents a significant contribution to the field of precision agriculture. In this work, Makkar developed a computational approach that leverages deep learning to automatically identify and classify defects in fruit, offering a non-destructive, efficient alternative to manual inspection. This research has practical implications for reducing food waste and enhancing supply chain quality control. While her citation count reflects the early-stage impact of her work, Makkar's focus on bridging machine learning with real-world agricultural challenges positions her as a promising voice in applied AI. Her contributions are particularly relevant for students and researchers interested in computer vision, agricultural engineering, and sustainable technology, demonstrating how neural networks can be harnessed for tangible, everyday problems in food production and safety.
Research Focus
Key Achievements
Top Papers
- 1Analysis and Detection of Fruit Defect Using Neural Network13 citations · 2018