Amith Khandakar
Papers
2
Total Citations
15
H-Index
2
About
Amith Khandakar is a rising researcher at the forefront of applied deep learning and computer vision, with a sharp focus on agricultural automation and sustainable robotics. His work bridges the gap between high-performance AI models and real-world, resource-constrained deployment, demonstrating a clear commitment to practical, scalable solutions. Khandakar’s major contributions include the development of a YOLOv8-based system for the real-time detection and classification of tomato ripeness stages, moving beyond simple binary classification to enable nuanced, multi-stage ripeness assessment on a Raspberry Pi platform. This work, already garnering 10 citations, directly addresses critical needs in precision crop management and automated harvesting. Further extending his impact into environmental sustainability, Khandakar introduced RTDRNet-Lite, a lightweight detection framework designed for robotic waste sorting. This framework, with 5 citations, exemplifies his ability to optimize complex neural architectures for efficient, real-time operation in edge computing environments. By consistently prioritizing both accuracy and computational efficiency, Amith Khandakar is establishing himself as a key innovator in deploying intelligent vision systems for the agricultural and recycling industries.
Research Focus
Key Achievements
Top Papers
- 1
- 2