A ResNet-CNN for accurate quantification of grapevine leaf hair
Research Internship @ Julius Kühn-Institut, Germany.
May 2021 - August 2021
- Worked on a project titled ”A ResNet-CNN for accurate quantification of grapevine leaf hair, Research presented at XIII International Symposium on Grapevine Breeding and Genetics 2022
- Developed and trained a convolutional neural network using the Residual Networks (ResNet) architecture, trained to identify leaf hair on slices of a leaf disk with 95.41% accuracy
Development of an Enterprise Management System (PMPLERP)
Capstone Project, Full Stack Development Internship @ Prithvi Metals Pvt. Ltd.
November 2020 - May 2021
- Developed an Enterprise Management System using the MEAN stack and Microservices Architecture
- Deployed and Administered an On-Premise Kubernetes Cluster for running EMS and other business applications reliably while surviving internet outages & sudden loss of power