Assistant. Professor
Computer Science & Artifical Intelligence
Sri Satya Sai University of Technology and Medical Sciences
10
Internet of Things, Deep Learning, Artifical Intelligence
Internet of Things
Mail: r.prashanth@sru.edu.in
Ph.D in Computer Science & Engineering from Sri Satya Sai University of Technology and Medical Sciences
M.Tech in Computer Science & Engineering from Jawaharlal Nehru Technological University Hyderabad
B.Tech in Information Technology from Jawaharlal Nehru Technological University Hyderabad
Assistant. Professor at MARRI LAXMAN REDDY INSTITUTE OF TECHNOLOGY AND MANAGEMENT, from 2022-03-05 to 2025-05-04.
Assistant. Professor at ST.MARTIN’S ENGINEERING COLLEGE, from 2021-04-07 to 2022-02-21.
Assistant. Professor at SREYAS INSTITUTE OF ENGINEERING AND TECHNOLOGY, from 2019-08-31 to 2021-03-31.
Assistant. Professor at SVS GROUP OF INSITUTIONS, from 2015-04-13 to 2019-01-12.
Ph.D
PG
UG
D.Panduranga, R.Prashanth Kumar, More Swami Das, G.Narender, M. Sreelaxmi, *N.Rajeswaran, Sustainable Aquaculture Management through IoT and Deep Learning-Driven Remote Monitoring, Proceedings of the 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA 2024)
Mr. Ravula Prashanth Kumar, Dr. Harsh Lohia, Dr. A. Ramaswami Reddy, A Better Software Framework for Increasing Qos in the Internet of Things TELEMATIQUE Volume 22 Issue 1, 2023, ISSN: 1856-4194 230 – 241
Mr. Ravula Prashanth Kumar, Dr. Harsh Lohia, Dr. A. Ramaswami Reddy, A Survey On Iot Environment Qos Architecture And Implementations (Mukt Shabd Journal ISSN NO: 2347-3150)
Dr. Harsh Lohia, Dr. A. Ramaswami Reddy, Mr. Ravula Prashanth Kumar, A CROSS-LAYER PARADIGM WITH QOS AWARENESS FOR IOT APPLICATIONS IN URBAN DEVELOPMENT, Industrial Engineering Journal ISSN: 0970-2555, Volume : 52, Issue 7, July : 2023
The present invention provides an intelligent soil health analytics-based system and method for crop-specific fertilizer recommendation and optimization of Nitrogen (N), Phosphorus (P), and Potassium (K) nutrients. The invention is 5 designed to analyze soil nutrient conditions and generate precise fertilizer recommendations tailored to specific crops and field conditions, thereby improving agricultural productivity and promoting sustainable nutrient management practices.
The present invention discloses an Internet of Things (IoT) based smart irrigation system and method for optimized water and nutrient delivery in intensive mango horticulture. The invention integrates soil sensing devices, environmental monitoring units, wireless communication modules, and an automated irrigation control mechanism to ensure efficient water utilization and balanced nutrient supply for mango orchards.
The present invention provides an intelligent system and method for automated detection and classification of diseases affecting Brinjal leaves using advanced deep learning techniques. The invention utilizes a Deep Belief Network based architecture to analyze leaf images, identify disease patterns, and classify the detected diseases with improved accuracy and reliability.
The present invention discloses a system and method for explainable artificial intelligence-based nutrient stress characterization and paddy yield prediction across diverse agro-climatic zones. The invention integrates multi-source agricultural data including soil nutrient measurements, climatic 5 parameters, crop imagery, and historical yield records to identify nutrient stress conditions and forecast crop productivity with improved accuracy and transparency.
