Curriculum Vitae

Md. Nafiul Islam, Ph.D.

Postdoctoral Research Associate
Texas A&M AgriLife Research, College Station, TX

Research Interests

Artificial Intelligence in Agriculture; Precision Livestock Farming; Computer Vision; Deep Learning; Multimodal Data Integration; Geospatial Analytics; Animal Health and Welfare Monitoring

Education

Ph.D. in Biosystems Engineering

University of Tennessee, Knoxville, USA — 2025

• Research Areas: Agricultural IoT, Computer Vision, Deep Learning, Machine Learning, Animal Health and Welfare
• Dissertation: Smart Cattle Monitoring: Using Computer Vision to Detect Physiological, Behavioral, and Physical Characteristics
• Major Professor: Hao Gan, Ph.D.

M.S. in Agricultural Machinery Engineering

Chungnam National University, South Korea — 2021

• Research Areas: Wireless Sensor Networks, Precision Agriculture Sensing and Control, Agricultural Machinery Design, Structural Analysis
• Thesis: Structural Analysis of a Clamp-Type Picking Mechanism for a 2.7 kW Automatic Pepper Transplanter
• Major Professor: Sun-Ok Chung, Ph.D.

B.S. in Agricultural Engineering

Hajee Mohammad Danesh Science and Technology University, Bangladesh — 2017

• Research Areas: Sensors, Microcontrollers, Storage Systems
• Thesis: Development of a Low-Cost Evaporative Cooling System for Fresh Tomato
• Major Professor: Md. Shaha Nur Kabir, Ph.D.

Professional Experience

Postdoctoral Research Associate

Texas A&M AgriLife Research, College Station, TX
Group of AI in Agriculture (GAIA)

• Develop AI-driven systems for livestock monitoring using computer vision and sensor data
• Design multimodal data fusion pipelines integrating vision, GPS, and environmental data
• Collaborate with interdisciplinary teams across engineering, animal science, and data science

Graduate Research Assistant

University of Tennessee, Knoxville, TN

• Developed computer vision models for livestock behavior and health monitoring
• Designed experimental systems for large-scale data collection
• Applied deep learning and machine learning techniques for real-world agricultural problems

Research Assistant

Chungnam National University, South Korea

• Conducted research in precision agriculture systems and machinery design
• Performed structural and mechanical analysis of agricultural equipment

Research Expertise

• Artificial Intelligence in Agriculture
• Computer Vision for Livestock Monitoring
• Precision Livestock Farming
• Machine Learning and Deep Learning
• Geospatial Data Analysis (GPS, NDVI, Remote Sensing)
• Multimodal Data Integration

Technical Skills

• Programming: Python, MATLAB
• AI/ML Frameworks: PyTorch, TensorFlow, Scikit-learn
• Computer Vision: OpenCV, YOLO, Image Processing
• Geospatial Analysis: QGIS, ArcGIS, Raster and GPS Data Processing
• Data Analysis & Visualization: Pandas, NumPy, Matplotlib

Research Projects

AI-Based Livestock Health and Welfare Detection

• Developed the computer vision pipeline using YOLO and segmentation models
• Extracted anatomical features (spine, tail-head angle, fat deposition)
• Applied machine learning models (Random Forest, SVM) for classification
• Achieved high agreement with expert scoring
• Developed non-contact methods for detecting drinking and respiration behavior

Multimodal Grazing Behavior Analysis

• Integrated GPS tracking data with forage and NDVI datasets
• Developed spatial analysis workflows (Voronoi zones, grid analysis)
• Quantified animal movement, grazing patterns, and forage utilization

Crop Yield Prediction Using Machine Learning and Multimodal Data

• Developed predictive models for crop yield using genotype, phenotype, and environmental data
• Integrated temporal features such as planting date, heading date, and harvest duration
• Applied machine learning and deep learning approaches for yield estimation
• Performed feature engineering, variance filtering, and data normalization for high-dimensional datasets
• Evaluated model performance using statistical and predictive accuracy metrics

Awards & Honors

• Outstanding Graduate Student Award
  International Conference on Precision Agriculture (ICPA), 2024
  
  Conference Details

Grants & Funding

• Research Equipment Grant (Submitted), Office of the Vice Chancellor for Research, The Texas A&M University System (A&M System), April 2026

Teaching & Mentoring

• Mentored undergraduate and graduate students in AI and agricultural engineering research
• Provided guidance on data analysis, machine learning, and experimental design

Professional Activities & Service

• Reviewer for peer-reviewed journals in agricultural engineering and artificial intelligence
• Member of professional societies in precision agriculture and biosystems engineering