Experience

Acclivis Technologies Pvt Ltd

• India
• IT Services and IT Consulting
• 1 - 100 Employee

• Software Developer

  • Sep 2023 - Present



Thiagarajar College of Engineering

• Education Administration Programs
• 500 - 600 Employee

• PHD Scholar

  • Jul 2019 - Jun 2023

  "Comprehensive deep learning and machine learning frameworks for tree species identification from UAV and Sentinel-2A images" is a project focused on utilizing advanced artificial intelligence techniques to accurately classify tree species using imagery from unmanned aerial vehicles (UAVs) and the Sentinel-2A satellite. The project involves the application of deep learning and machine learning algorithms to analyze visual features of tree species, leveraging high-resolution images provided by UAVs and Sentinel-2A. Multispectral data, beyond what can be perceived by the human eye, may be included in the images. The project comprises several key steps. Firstly, a diverse dataset of UAV and Sentinel-2A images is collected, serving as the basis for training and evaluating machine learning models. Data preprocessing tasks include cropping, resizing, normalization, and potentially extracting spectral bands from Sentinel-2A images. Data augmentation techniques may also be employed. Next, meaningful features are extracted from the images using deep learning techniques, such as convolutional neural networks (CNNs). CNNs can learn hierarchical representations from image data. The models are then trained on the prepared dataset, optimizing parameters to minimize classification error. The trained models are evaluated using metrics like accuracy, precision, recall, or F1-score. Finally, the models are deployed into a framework or application for automatic tree species identification. Users can upload images and receive predicted species as output. This comprehensive framework automates tree species identification, benefiting applications like forestry management, environmental monitoring, and biodiversity conservation. By leveraging deep learning and machine learning, the project enhances efficiency and accuracy in tree species identification, facilitating informed decision-making in relevant domains. Show less



Sparrow Softtech

• India
• IT Services and IT Consulting
• 1 - 100 Employee

• Research And Development Engineer

  • Sep 2017 - Jul 2019

  Contribution in Projects: Project Contribution: Project 1: Develop a robust and efficient method for detecting circles on diamond surfaces. Tool Used: OpenCV, C++ Project 2: Develop an efficient and accurate method for detecting symbols on raw diamond surfaces using the Fast R-CNN Tool used: Python, OpenCV, TensorFlow, Keras Project 3: Develop an algorithm to differentiate between Natural and Lab-Grown Diamonds Tool used: Python, C++, Raspberry pi, OpenCV, pillow, Linux Project 4: Develop an efficient and accurate method for detecting manufacturing defects in industrial products using image processing techniques Tool Used: Python, OpenCV, Sklearn, Scikit-Image Project 5: Automated Analysis of Diamond Color (D H N Z) Grading Tools used: Python, OpenCV, Scikit-Image, sklearn, Raspberry pi Show less



Bhaskaracharya Institute For Space Applications and Geo-Informatics

• India
• Defense and Space Manufacturing
• 200 - 300 Employee

• BiSAG

  • Jul 2015 - Aug 2016

  For internship For internship