BASE: Bridging AI, Systems, and Environment
Advancing AI frontiers through innovative research in agriculture and beyond
Our Research Focus
Computer Vision
Developing advanced techniques for visual data interpretation, including object detection, recognition, and image processing, to enhance automated visual understanding in diverse applications.
Deep Learning
Designing and optimizing state-of-the-art neural network architectures and algorithms to address complex problems across various domains, with a focus on improving performance and scalability.
AI for Agriculture
Applying artificial intelligence to agricultural challenges such as precision livestock management, crop health monitoring, and yield prediction, aiming to improve efficiency and sustainability in agriculture.
Federated Learning
Creating robust federated learning frameworks to handle non-iid data and resource constraints, ensuring effective and secure distributed learning across heterogeneous environments.
Generative Models
Advancing generative model techniques for data augmentation and realistic image synthesis, with a focus on improving the quality and diversity of synthetic data for various applications.
Real-time Systems
Implementing real-time AI solutions to enable immediate and accurate decision-making in critical applications, ensuring timely responses and actions in dynamic and high-stakes environments.
Lab Highlights
69+
Publications
5+
Research Projects
8
Team Members
19+
Years of Experience
Latest Works and Activities
Conference Presentation at ICMLA 2024
PhD student Taminul Islam presented our research on weed growth stage classification and detection at the Deep Learning and Applications special session during the 23rd International Conference on Machine Learning and Applications (ICMLA).
Learn MorePoster Presentation at IIN Sustainability Research Conference
Taminul Islam presented a poster on our research on weed growth stage classification and detection at the Illinois Innovation Network’s Sustainability Research Conference, held at Illinois State University.
Learn MorePoster Presentation at 5th Annual Hemp Cannabis Symposium 2024
Our team presented research on detecting and classifying cannabis seeds using deep learning techniques.
Learn MoreBASE Lab at the Graduate School Fair 2024
PhD students Toqi and Taminul, along with Dr. Khaled, represented the BASE Lab and the School of Computing at the Graduate School Fair 2024 at Southern Illinois University Carbondale.
Learn MorePhD students with supervisor at agricultural field
Our team is using drones to collect research samples from the field.
Learn MoreCollecting data using drone
Our team is using drones to collect research samples from the agricultural field.
Learn MoreConference Presentation at CVPR 2024
Our team presented research on segmenting in vitro methane emissions in cattle using optical gas imaging and deep learning.
Learn MoreOur Sponsors and Collaborators
Recent Publications
WeedVision: Multi-Stage Growth and Classification of Weeds using DETR and RetinaNet for Precision Agriculture
Taminul Islam, Toqi Tahamid Sarker, Khaled R. Ahmed, Cristiana Bernardi Rankrape, Karla Gage
Detection and Classification of Cannabis Seeds Using RetinaNet and Faster R-CNN
Taminul Islam, Toqi Tahamid Sarker, Khaled R. Ahmed, Naoufal Lakhssassi
Porosity Prediction of 3D Printed Components Using U-Net and Its Variants
Aluri Manoj, Khaled R. Ahmed, Ghada Omar
Gasformer: A Transformer-based Architecture for Segmenting Methane Emissions from Livestock in Optical Gas Imaging
Toqi Tahamid Sarker, Mohamed G Embaby, Khaled R. Ahmed, Amer AbuGhazaleh
Latest News and Achievements
Paper presentation at ICMLA, Miami, FL
December 2024
Taminul Islam, a PhD student and a Research Assistant from the BASE Lab, presented our research titled "WeedVision: Multi-Stage Growth and Classification of Weeds Using DETR and RetinaNet for Precision Agriculture" at the Deep Learning and Applications special session during the 23rd International Conference on Machine Learning and Applications (ICMLA).
Poster Presentation at IIN Sustainability Research Conference, Normal, IL
November 2024
Taminul Islam, a PhD student and Research Assistant from the BASE Lab, presented a poster titled "WeedVision: Multi-Stage Growth and Classification of Weeds Using DETR and RetinaNet for Precision Agriculture" at the Illinois Innovation Network’s Sustainability Research Conference, held at Illinois State University.
Poster Presentation at 5th Annual Hemp Cannabis Symposium, SIUC, Carbondale, IL
October, 2024
Taminul Islam and Toqi Tahamid Sarker, PhD students and Research Assistants from the BASE Lab, presented a poster titled "Detection and Classification of Cannabis Seeds Using RetinaNet and Faster R-CNN" at Southern Illinois University’s 5th Annual Hemp Cannabis Symposium 2024.
New Illinois Soybean Center Grant Awarded
August 2024
Awarded a one-year research grant from Illinois Soybean Center for "Smart Farming Soybean: The use of AI to detect and estimate early insect infections in Soybean"
Join Our Lab
Interested in pushing the boundaries of AI? We are always looking for talented individuals to join our team.
PhD Students
Conduct cutting-edge research in AI and Computer Vision.
Masters Students
Gain hands-on experience in advanced AI projects.