Automated Root Tracing Using Deep Learning

Roots play a crucial role in plant development by anchoring plants, absorbing nutrients, and maintaining soil structure. Understanding root structures and dynamics is vital for ecological research and assessing soil health. However, tracing roots from photos obtained with Minirhizotron is a time-consuming task, and applying deep learning techniques can facilitate this process. This thesis applies the DeepLabV3+ model with a confidence weighted approach to segment root structures in soil images. The methodology involves classifying images based on root visibility, cropping images to focus on root regions, and training the DeepLabV3+ model, which employs atrous convolutions and an Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale contextual information. The confidence method modulates the loss function based on pixel confidence scores to handle ambiguous boundaries and low-resolution images. The confidence function decreases with distance from root boundaries and adapts to varying scales. This method was tested on multiple datasets from natural environments with varying soil types, including Mepibdeath, Ban Harol, Champenoux, and Hesse, which allowed for an assessment of the robustness and generalization ability of the tested models. Evaluated using metrics such as Cohen’s kappa and R2 for surface and length, the results show that the confidence-weighted approach improves segmentation quality by reducing false positives but may miss weakly expressed roots. Future work should focus on enhancing model robustness and improving training data quality to handle complex root structures and environmental noise better.

Level thesis:degree_level

Masters

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Lu, Cen

Advisor dc:contributor.advisor

• Pradalier, Cédric

Committee members dc:contributor.committeemember

• Xu, Danfei
• Aravecchia, Stephanie

• Computer Vision
• Root
• Deep Learning

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1853/76906

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/76906