Frequently Asked Questions - Toqi Tahamid Sarker

Question 1

What is your research focus?

Accepted Answer

My research focuses on computer vision and deep learning for precision agriculture and environmental monitoring. I specialize in developing efficient multi-task learning frameworks for semantic segmentation, object detection, and tracking, with applications in livestock methane emission monitoring and precision agriculture.

Question 2

How do you measure livestock methane emissions using computer vision?

Accepted Answer

We use optical gas imaging (OGI) cameras combined with deep learning models to detect and quantify methane plumes from livestock. Our AI systems can segment methane emissions in real-time and classify different dietary treatments, providing a low-cost alternative to traditional measurement methods.

Question 3

What programming languages and frameworks do you use?

Accepted Answer

I primarily work with Python for deep learning research, using PyTorch as my main framework. I also have experience with JavaScript, TypeScript, and Next.js for web development. For computer vision projects, I utilize MMDetection, MMSegmentation, and other OpenMMLab tools.

Question 4

Where do you publish your research?

Accepted Answer

I publish in top-tier computer vision conferences and journals including CVPR, ICCV, ICMLA, IET Image Processing, and Scientific Reports. My work has been presented at both main conferences and specialized workshops focusing on agricultural applications of AI.

Question 5

Are you available for research collaboration?

Accepted Answer

Yes, I'm open to research collaborations in computer vision, deep learning, and precision agriculture. I'm particularly interested in projects involving multi-task learning, efficient architectures, and real-world applications of AI in agriculture and environmental monitoring.

Question 6

Do you accept graduate students or interns?

Accepted Answer

As a PhD student, I don't directly supervise students, but I collaborate with undergraduate researchers through the BASE Lab at SIU Carbondale. For graduate opportunities, please contact my advisor Dr. Khaled Ahmed. I'm also open to Summer 2026 internship opportunities in AI/ML and computer vision.

Question 7

What datasets do you work with?

Accepted Answer

I work with both public datasets and custom datasets we've created for agricultural applications. This includes our methane emission datasets using OGI cameras, weed detection datasets with multi-stage growth annotations, and soybean growth stage datasets. I also utilize standard computer vision benchmarks for comparative evaluation.

Question 8

How can I cite your work?

Accepted Answer

You can find all my publications with proper citations on my Google Scholar profile or the publications page of this website. Each paper includes BibTeX citations for easy reference. My ORCID is 0000-0003-2482-8059 for academic identification.

Question 9

What is precision agriculture and how does AI help?

Accepted Answer

Precision agriculture uses technology to optimize crop yields and reduce environmental impact. AI and computer vision enable automated monitoring of crop health, pest detection, growth stage classification, and resource optimization. Our research specifically focuses on livestock emission monitoring and sustainable farming practices.

Question 10

How accurate are your computer vision models?

Accepted Answer

Our models achieve state-of-the-art performance: GasTwinFormer reaches 74.47% mIoU for methane segmentation, WeedSense achieves 89.78% mIoU for weed segmentation, and SoyStageNet attains 83.2% AP for growth stage detection. We prioritize both accuracy and efficiency for real-world deployment.

WeedRepFormer: Reparameterizable Vision Transformers for Real-Time Waterhemp Segmentation and Gender Classification

FUME: Fused Unified Multi-Gas Emission Network for Livestock Rumen Acidosis Detection

GasTwinFormer: A Hybrid Vision Transformer for Livestock Methane Emission Segmentation and Dietary Classification in Optical Gas Imaging

WeedSense: Multi-Task Learning for Weed Segmentation, Height Estimation, and Growth Stage Classification

CarboFormer: A Lightweight Semantic Segmentation Architecture for Efficient Carbon Dioxide Detection Using Optical Gas Imaging

SoyStageNet: Balancing Accuracy and Efficiency for Real-Time Soybean Growth Stage Detection

WeedSwin hierarchical vision transformer with SAM-2 for multi-stage weed detection and classification

Optical gas imaging and deep learning for quantifying enteric methane emissions from rumen fermentation in vitro

WeedVision: Multi-Stage Growth and Classification of Weeds using DETR and RetinaNet for Precision Agriculture

Gasformer: A Transformer-based Architecture for Segmenting Methane Emissions from Livestock in Optical Gas Imaging

Detection and Classification of Cannabis Seeds Using RetinaNet and Faster R-CNN

Cannabis Seed Variant Detection Using Faster R-CNN

Solar Event Tracking with Deep Regression Networks: A Proof of Concept Evaluation