About
Dr. Revanasiddappa Bandi is an accomplished Computer Science and Engineering academician, researcher, educator, and mentor with more than 15 years of experience in teaching, research, student mentoring, and academic administration. He currently serves as an Associate Professor in the School of Computer Science & Engineering at RV University, Bengaluru, Karnataka, India.
He completed his Ph.D. in IoT and Machine Learning from Visvesvaraya Technological University (VTU), Belagavi, in 2024. His academic and research interests lie at the intersection of Artificial Intelligence, Machine Learning, Internet of Things, Smart Agriculture, Computer Vision, Cloud Computing, and Edge Computing. His work focuses on applying emerging technologies to address real-world challenges, particularly in agriculture, intelligent monitoring, automation, and data-driven decision-making.
Dr. Bandi has contributed to research publications, book chapters, conference proceedings, and technology-oriented projects. His research includes areas such as IoT-enabled smart agriculture, crop monitoring and yield prediction, explainable artificial intelligence, computer vision, cattle detection and counting, Kubernetes security, and edge computing. His scholarly contributions include publications in international journals and book chapters published by established academic publishers.
He is passionate about student-centric education and experiential learning, encouraging students to connect theoretical concepts with practical implementation, experimentation, analysis, and project development. His teaching interests include Artificial Intelligence, Machine Learning, Data Science, IoT, Cloud Computing, Edge Computing, DevOps, Kubernetes, Serverless Computing, Algorithms, and Programming.
Dr. Bandi has also received academic recognition, including a Best Paper Award at EASCT 2023, and has served as a Session Chair at INOACC 2025.
His professional philosophy is centered on continuous learning, meaningful research, innovation, and mentoring the next generation of technology professionals. He strives to create an academic environment where students learn with curiosity, experiment confidently, and develop solutions that create meaningful societal impact.
A Security-Based Trust-Aware Heterogeneous Scheduling Framework for Internet of Vehicles Workflows
This research presents HETRUST, a security- and trust-aware heterogeneous scheduling framework for Internet of Vehicles (IoV) workflows. The framework integrates CPU–GPU heterogeneous computing, edge–cloud collaboration, energy-aware scheduling, cost optimization, and trust-based security mechanisms for efficient workflow execution. Experimental evaluation using Montage and Cybershake workflows demonstrates improvements in makespan, energy consumption, resource utilization, cost, and attack detection compared with baseline approaches. The work contributes to secure and efficient workflow management for large-scale IoV and edge–cloud environments.
Authors: Manjunatha S., Deepak D. J., Revanasiddappa Bandi, R. Vadivel, Padmini M. S., Asha Rani M., Prabhu Vijay S. S.
Link: https://doi.org/10.19101/IJATEE.2022.10100136?utm_source=chatgpt.com
Leaf Disease Severity Classification with Explainable Artificial Intelligence using Transformer Networks
This research explores Explainable Artificial Intelligence (XAI) for plant disease severity classification using transformer-based deep learning techniques. The work aims to improve the reliability and interpretability of AI-assisted agricultural diagnosis by not only classifying plant diseases but also providing insights into the decision-making process of the model. Explainability is particularly important in agricultural applications because farmers and agricultural experts need confidence in AI-generated recommendations. The study contributes to the intersection of deep learning, computer vision, plant disease diagnosis, and explainable AI, supporting the development of intelligent systems for precision agriculture and early disease management.
Link: https://doi.org/10.19101/IJATEE.2022.10100136?utm_source=chatgpt.com
Real-time Early Detection of Weed Plants in Pulse Crop Field using Drone with IoT
This research focuses on the application of IoT, drones, computer vision, and deep learning for real-time detection of weeds in pulse crop fields. The study investigates the use of low-altitude aerial imagery to identify weed plants and support precision agriculture. A semantic segmentation approach using LinkNet with ResNet-34 was explored for detecting weed regions from drone-acquired images. The research demonstrates how integrating aerial sensing, IoT technologies, and AI-based image analysis can support timely agricultural interventions and reduce dependence on manual field inspection. The work contributes to the development of technology-driven precision agriculture systems capable of improving crop management and resource utilization.
Link: 10.13140/RG.2.2.27656.03845
IoT-Enabled Silkworm Egg Counting: A Visual Processing Approach
This work presents an IoT and computer vision-based approach for automated silkworm egg counting. The research investigates visual processing techniques to reduce the dependency on manual counting and improve efficiency in sericulture-related applications. By combining image-based analysis with IoT technologies, the system demonstrates how intelligent automation can support agricultural and allied activities. The work reflects my broader research interest in applying computer vision and machine intelligence to real-world agricultural problems where automated monitoring, accurate counting, and data-driven decision-making can improve productivity and operational efficiency. The work was presented at the International Conference on Emerging Research in Computing, Information, Communication, Artificial Intelligence and Machine Learning (ERCICAM 2024) and subsequently contributed to a scholarly book chapter
Link: 10.1007/978-981-96-0165-3_30
Animal Detection and Cattle Counting using YoLoV5 Model
This research applies deep learning and computer vision to automated animal detection and cattle counting. The objective is to develop an intelligent system capable of detecting and counting cattle in large-scale farming environments, addressing challenges associated with manual livestock monitoring. The approach leverages the YOLOv5 object-detection architecture to identify animals from visual data. Such systems can support smart livestock management by enabling automated population monitoring, reducing manual effort, and providing timely information for farm operations. The work represents an extension of my research from crop-focused smart agriculture toward AI-enabled livestock monitoring and intelligent farm management.
Link: https://www.researchgate.net/publication/394478964_Animal_Detection_and_Cattle_Counting_using_YoLoV5_Model
AI & Machine Learning
IoT & Smart Agriculture
Edge & Cloud Computing
Computer Vision & Deep Learning
Explainable AI
Internet of Vehicles & Intelligent Transportation
Cybersecurity & Trust-Aware Computing
Embedded Systems & IoT Automation
AI-Driven Sustainable Agriculture
Research Interests
Artificial Intelligence and Machine Learning
Internet of Things (IoT)
Smart Agriculture and Precision Farming
Edge Computing and Cloud Computing
Computer Vision and Deep Learning
Multimodal and Multilingual AI
Best Paper Award – EASCT 2023
Received the Best Paper Award at the International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT-2023), organized by RVITM, Bengaluru, October 2023.
Indian Patent – IoT-Based Smart Agriculture
Filed Indian Patent Application No. 202341074029 titled “IoT-Based Smart Agriculture to Optimize Crop Yield,” published on 15 December 2023.
Ph.D. in Computer Science & Engineering
Awarded Ph.D. in Computer Science and Engineering from Visvesvaraya Technological University in May 2024, specializing in IoT and Machine Learning.
Session Chair – International Conference
Served as Session Chair for an international conference, contributing to technical discussions, research evaluation, and knowledge exchange among researchers and academic professionals.
Research in Smart Agriculture
Developed research contributions integrating IoT, Machine Learning, Edge Computing, and Computer Vision for crop monitoring, disease detection, and intelligent agricultural decision-making.
Research Publication – Q1 Elsevier Journal
Co-authored a 2026 research article in *Computers and Electrical Engineering*, a Q1 Elsevier journal, focusing on security-based trust-aware scheduling for Internet of Vehicles workflows.
Funded Research Project – Smart Agriculture
Successfully developed a funded research proposal on IoT-based agriculture yield prediction and crop monitoring, integrating sensors, edge computing, machine learning, and cloud technologies.
Associate Professor
School of Computer Science and Engineering
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Bengaluru
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