Publications
Embedded Files
Cooperative Localization of UAVs in Multi-Robot Systems Using Deep Learning-Based Detection
Published at AIAA SCITECH 2025 Forum [paper]
This paper introduces a novel cooperative localization framework designed to enhance localization accuracy in multi-robot systems comprising UAVs and Unmanned Ground Vehicles (UGVs). The proposed method leverages deep learning-based detection, specifically utilizing the YOLOv8 convolutional neural network, to enable real-time object detection and localization. By integrating perception with Kalman Filtering (KF), the approach achieves improved localization accuracy, even in challenging environments, thus advancing the state-of-the-art in cooperative multi-robot systems.
Digital soil mapping of available phosphorus using a smartphone-integrated RGB imaging device and ascorbic acid extraction method
Published at Smart Agricultural Technology [paper]
To take quick remedial actions, it is critical to map the non-germinated mulch cells at a high throughput capacity which is aimed in this study. We compared the performance of different object detection models to find the missing index of mulch planting.
Estimated the maximum resolution of testing images and calculated the maximum height for the drone flight. Targetting Computers and Electronics in Agriculture Journal.
A Two-stage Deep-learning Model for Detection and Occlusion-based Classification of Kashmiri Orchard Apples for Robotic Harvesting.
Published at Journal of Biosystems Engineering [paper]
Proposed a novel two-stage deep-learning-based approach that can detect the apples using YOLOv7 and utilized EfficientNet that can classify the apple's occlusion condition.
From Goals, Waypoints Paths To Long Term Human Trajectory Forecasting
Published at ReScience C Journal [paper] [code]
Undertaken as part of the Machine Learning Reproducibility Challenge 2021 , we reviewed the above-accepted ICCV 2021 publication for its reproducibility and verification of its empirical claims. Our major contributions include implementing new sampling methods, creating new visualizations, achieving better results than the original paper, and discovering the generalizing power of the model over other datasets.
Conferences
Virtual Farm Environments and Sim-to-Real Transfer in Agricultural Robotics Using NVIDIA Omniverse
Selected at American Society of Agricultural and Biological Engineers Annual International Meet 2025
• Developed virtual farm environments in the NVIDIA Omniverse Isaac Sim to replicate real-world conditions like cornfields.
• Trained models using visual, force, and 3D data in simulation, the researchers aim to predict stalk lodging risk.
Robotic Manipulation for Plant Interaction Using Reinforcement Learning
Selected at Workshop on Machine Learning for Cyber-Agricultural Systema 2022 [GitHub]
An agent was trained for bending the corn plant built in the "Chrono” simulator which calculates the finite element analysis. I did this project at Iowa State University.
Fine-tuning based approach for generalizing YOLOv4 network for Soybean detection in UAS images
Selected at American Society of Agricultural and Biological Engineers Annual International Meet 2022
• Explored two methods for achieving generalization of the YOLOv4 model using images collected from different fields with various real-life confusing differences like the presence of weeds in the field.
• Implemented Active Learning to reduce the burden of manually annotating the imagery dataset by 75 %.
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