Praful Hambarde
Papers
2
Total Citations
247
H-Index
2
About
Praful Hambarde is a researcher at the forefront of applying deep learning to solve real-world challenges in underwater robotics and sustainable agriculture. His work is defined by a dual focus on visual perception and precision agriculture. Hambarde’s most impactful contribution is the development of UW-GAN, a generative adversarial network that simultaneously performs single-image depth estimation and image enhancement for underwater scenes. This pioneering work, cited over 190 times, directly addresses the ill-posed problem of depth prediction in murky, low-visibility environments, offering a critical tool for marine engineering and autonomous underwater vehicles. More recently, he has advanced the field of agricultural technology with a lightweight deep learning approach for real-time plant disease detection, specifically targeting pigeon pea crops. By creating a novel dataset and an efficient model, his 2024 work (with 55 citations) enables rapid, on-field diagnosis, a vital step for sustainable crop production. Hambarde’s research bridges the gap between complex computer vision theory and practical, deployable solutions, making him a notable figure in both marine and agricultural AI.
Research Focus
Key Achievements
Top Papers
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- 2