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).
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Poster 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.
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Poster Presentation at 5th Annual Hemp Cannabis Symposium 2024
Our team presented research on detecting and classifying cannabis seeds using deep learning techniques.
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BASE 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.
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PhD students with supervisor at agricultural field
Our team is using drones to collect research samples from the field.
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Collecting data using drone
Our team is using drones to collect research samples from the agricultural field.
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Conference Presentation at CVPR 2024
Our team presented research on segmenting in vitro methane emissions in cattle using optical gas imaging and deep learning.
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Recent Publications
CarboNeXT and CarboFormer: Dual Semantic Segmentation Architectures for Detecting and Quantifying Carbon Dioxide Emissions Using Optical Gas Imaging
Taminul Islam, Toqi Tahamid Sarker, Mohamed G. Embaby, Khaled R. Ahmed, Amer AbuGhazaleh
Carbon Emission Quantification of Machine Learning: A Review
Syed Mhamudul Hasan, Taminul Islam, Munshi Saifuzzaman, Khaled R. Ahmed, Chun-Hsi Huang, Abdur R. Shahid
SoyStageNet: Balancing Accuracy and Efficiency for Real-Time Soybean Growth Stage Detection
Abdellah Lakhssassi, Toqi Tahamid Sarker, Khaled Ahmed, Naoufal Lakhssassi, Khalid Meksem
Optical gas imaging and deep learning for quantifying enteric methane emissions from rumen fermentation in vitro
Mohamed G. Embaby, Toqi Tahamid Sarker, Amer AbuGhazaleh, Khaled R. Ahmed
Latest News and Achievements
Journal Article Accepted: WeedSwin
June 2025
We are thrilled to announce that our journal article, 'WeedSwin Hierarchical Vision Transformer with SAM-2 for Multi-Stage Weed Detection and Classification' has been acepted by the Scientific Reports (Nature). Congratulations to the team!
Review Article Published: Carbon Emission Quantification of Machine Learning
June 2025
We are excited to announce that our review article, 'Carbon Emission Quantification of Machine Learning: A Review' has been published in IEEE Transactions on Sustainable Computing. This collaborative work with SHIELD Lab, SIU represents an important contribution to sustainable computing research. Congratulations to the entire team!
IIN Sustaining Illinois Funding for AI-Driven Sustainable Materials Research
March 2025
We are pleased to announce that our research proposal, 'AI-Driven Discovery of Sustainable Ionic Liquids: A Multimodal Deep Learning Approach for Enhanced Lignin and Plastic Deconstruction,' has been awarded funding by the IIN Sustaining Illinois program. The period of performance is May 16, 2025, to May 15, 2026
New Publication: Deep Learning Meets Agricultural Sustainability
January 2025
Our lab's latest research "Optical gas imaging and deep learning for quantifying enteric methane emissions from rumen fermentation in vitro" published in IET Image Processing explores a potential new approach to greenhouse gas monitoring in the livestock industry. By combining Optical Gas Imaging (OGI) with deep learning, we've investigated a promising alternative to traditional methane quantification methods. Our interdisciplinary collaboration between Animal Science and Computer Science shows how we can make environmental sustainability more achievable for farmers and researchers alike. While further research is needed, this initial work represents an important step in exploring more accessible approaches to livestock greenhouse gas monitoring.
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.