Papers
1
Total Citations
36
H-Index
1
About
Paarth Bir is a researcher at the intersection of deep learning and agricultural technology, with a primary focus on applying computer vision to crop disease detection. His most influential work, "Transfer Learning based Tomato Leaf Disease Detection for mobile applications" (2020), addresses a critical challenge in Indian agriculture—where an estimated 15–25% of potential crop production is lost to pests and diseases. By leveraging transfer learning with convolutional neural networks, Bir demonstrated how mobile-optimized models can enable early, accurate disease identification, offering a scalable solution for food security. This paper has garnered 36 citations, reflecting its practical relevance in precision agriculture. Bir’s contributions stand out for bridging state-of-the-art AI with real-world deployment constraints, making advanced diagnostics accessible to farmers. His work is a notable step toward democratizing agricultural technology, and it continues to inspire further research in lightweight, transferable models for resource-limited settings.
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Top Papers
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