Theses and Dissertations
ORCID
https://orcid.org/0009-0003-4465-2377
Advisor
Lowe, J.
Committee Member
Chesser, G.
Committee Member
Pieralisi, Brian
Date of Degree
5-15-2026
Original embargo terms
Immediate Worldwide Access
Document Type
Graduate Thesis - Open Access
Major
Agriculture (Engineering Technology)
Degree Name
Master of Science (M.S.)
College
College of Agriculture and Life Sciences
Department
Department of Agricultural and Biological Engineering
Abstract
Controlling in-field weeds in row-crop production systems is vital to the success of the operation. To control weeds, most producers use combinations of weed control strategies, including tillage and herbicide applications. Cotton production operations across the country have been trending toward conservation tillage practices in an effort to minimize soil disruption and maintain crop residues on the soil surface. This practice, in addition to the adoption of herbicide-tolerant cotton varieties, has encouraged more use of chemical herbicides. The overuse of these herbicides has resulted in an increase in herbicide-resistant weeds, making weed control difficult for farmers. This research aimed to address excessive tillage and herbicide applications by creating a precision tillage implement that could identify in-field weeds and mechanically remove those weeds in real-time. The platform utilized HSV color space thresholding and model training to identify unwanted vegetation and trigger mechanical removal. Findings will lay a foundational framework for the platform to become a producer-friendly, open-source weed removal tool.
Sponsorship (Optional)
Cotton Incorporated
Recommended Citation
Cobb, Clara Anne, "The development and evaluation of an automated tactical tillage tool to identify and control weeds in row-crop production systems using vision-based machine learning." (2026). Theses and Dissertations. 6880.
https://scholarsjunction.msstate.edu/td/6880
