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

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

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

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

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

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

Collecting data using drone

Our team is using drones to collect research samples from the agricultural field.

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Research Sample at Greenhouse

Research Sample at Greenhouse

Our team prepared reseach sample in a greenhouse.

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Conference Presentation at CVPR 2024

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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Our Sponsors and Collaborators

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Recent Publications

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

IET Image ProcessingView Publication

Latest News and Achievements

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.

Publication

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).

Academic

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.

Academic

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.

Academic

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.