Theses and Dissertations
ORCID
https://orcid.org/0000-0002-3250-0929
Advisor
Wang, Haifeng
Committee Member
Marufuzzaman, Mohammad
Committee Member
Lu, Yuzhen
Date of Degree
8-13-2024
Original embargo terms
Immediate Worldwide Access
Document Type
Graduate Thesis - Open Access
Major
Industrial & Systems Engineering (Data Analytics)
Degree Name
Master of Science (M.S.)
College
James Worth Bagley College of Engineering
Department
Department of Industrial and Systems Engineering
Abstract
Effective weed control is crucial, especially for herbicide-resistant species. Machine vision technology, through weed detection and localization, can facilitate precise, species-specific treatments. Despite the challenges posed by unstructured field conditions and weed variability, deep learning (DL) algorithms show promise. This study evaluated thirteen DL-based weed detection models, including YOLOv5, RetinaNet, EfficientDet, Fast RCNN, and Faster RCNN, using pre-trained object detectors. RetinaNet (R101-FPN) achieved the highest accuracy with a mean average precision (mAP@0.50) of 79.98%, though it had longer inference times. YOLOv5n, with the fastest inference (17 ms on Google Colab) and only 1.8 million parameters, achieved a comparable 76.58% mAP@0.50, making it suitable for real-time use in resource-limited devices. A prototype using YOLOv5 was tested on two datasets, showing good real-time accuracy on In-season data and comparable results on Cross-season data, despite some accuracy challenges due to dataset distribution shifts.
Recommended Citation
Rahman, Abdur, "Performance evaluation of deep learning object detectors for weed detection and real time deployment in cotton fields" (2024). Theses and Dissertations. 6212.
https://scholarsjunction.msstate.edu/td/6212