Faculty Mentors: Marufuzzaman, Street, Wang

Opportunities: Students will gain hands-on experience developing a cost-effective, computationally sound, industry-scale image-based (contact-free) tool to rapidly assess woodchip and sawdust characteristics (e.g., ash, moisture content, particle size distribution) in real-time. The tool will offer an alternative solution to rapidly assess feedstock quality, an ongoing problem for industries that rely on woodchips and sawdust as primary feedstock. The students will learn how to store a large volume of collected images (~10,000 RGB) and feed them into a convolutional neural network-based deep learning (DL) architecture.

Faculty Mentors: Wang, Fortuin, Street

Opportunities: This study examines the effectiveness of advanced sensing technologies for detecting and monitoring forest conditions. It integrates data from multiple sensor types deployed through both aerial and ground platforms to capture detailed information on vegetation health, soil moisture, canopy structure, and signs of environmental stress. Through the use of high-resolution imagery combined with machine learning and geospatial analytics, the study evaluates how each sensing tool supports early identification of issues such as disease outbreaks, drought stress, storm damage, and invasive species. The research also compares cost, coverage, data quality, and operational feasibility across sensing systems to provide a comprehensive view of their strengths and limitations. The findings help advance data-driven forest management by enabling more precise, timely, and scalable monitoring approaches for conservation, resource planning, and climate resilience.

Faculty Mentors: Street, Wang, Ragon

Opportunities: The desired moisture of timbers before being treated is approximately 25% to allow for the treatment chemical to easily penetrate the wood cells. However, if the timbers are too wet or too dry, the efficiency of introducing the chemicals into the wood using a treatment cylinder is reduced. Students involved in this project will collect data (e.g., taking images of the timbers and gathering soundwave data), assist with developing and testing deep learning architecture to determine the moisture content of the timbers, create a mobile application, and prepare the extension report. This technology would alleviate the tedious process of physically boring wood to obtain a sample, which is then used to determine the moisture content.

Faculty Mentors: Ragon, Fortuin, Marufuzzaman

Opportunities: Automatically determining and packaging fenceposts with a similar diameter in a realistic, fast-paced industrial environment is an important research problem. The posts must all be cut to an equivalent length but are not peeled to equal diameters. With ongoing manual efforts, industries lose both valuable production and labor time. Students involved in this project will be responsible for gathering data (taking post images, measuring the actual diameter of the posts, etc.) and assisting with writing the code for the deep learning model to predict the classification of the posts.

Faculty Mentors: Fortuin, Tian, Wang

Opportunities: Early detection and quick identification of damaging forest insects can be critical for protecting timber resources and wood quality.  Students will gain hands-on experience in developing AI tools for identifying key forest insects from images.  Students involved in this research will gather image data from online taxonomic databases as well as utilizing collections at the Mississippi Entomological Museum and the Forest Disturbance, Insect and Conservation Ecology lab to train an AI model in forest insect recognition and identification.  Students will develop skills in insect identification and have the opportunity to create a mobile application for forest insect ID for end users to assist in.

Faculty Mentors: Wang, Ragon, Tian

Opportunities: Students will gain experience in using state-of-the-art Generative AI models to create synthetic images and data representations of wood product samples. This will enhance wood quality assessment, defect detection, and new material simulation for the forest products industry. The student will learn how to use AI-based models to augment real-world wood product datasets for training machine learning algorithms, how to improve defect detection and wood classification by generating diverse and realistic wood texture patterns, and how to simulate aging, weathering, and quality variations in wood products for predictive analytics.