Papers
2
Total Citations
11
H-Index
2
About
Abhijit is a researcher at the forefront of applying computer vision to agricultural automation, with a particular focus on precision classification systems for specialty crops. His work centers on developing deep learning frameworks that bridge the gap between advanced object detection models and practical farming needs. Abhijit’s major contribution lies in the adaptation of the YOLO V5 architecture for real-time, high-accuracy classification of bird eye chili (kantahri mulaku), a task that presents unique challenges due to the small size and visual variability of the produce. His most cited paper, "Computer Vision Assisted Real-Time Bird Eye Chili Classification Using YOLO V5 Framework" (2023), has garnered 9 citations, demonstrating its relevance to researchers in agricultural informatics. A subsequent paper on the same framework (2023, 2 citations) further refines the methodology. By enabling automated sorting and quality assessment, Abhijit’s work directly supports the goal of reducing post-harvest losses and increasing efficiency in the spice supply chain. His research exemplifies how cutting-edge computer vision can be deployed in resource-constrained agricultural settings, making him a notable contributor to the growing field of AI-driven agritech.
Research Focus
Key Achievements
Top Papers
- 1
- 2