Date of Award
2021
Publisher
North Dakota State University
Document Type
Thesis
Degree Awarded
Master of Science (MS)
Program
Agricultural and Biosystems Engineering
Department
Agricultural and Biosystems Engineering
College
Agriculture, Food Systems and Natural Resources
Faculty Advisor
Cannayen, Igathinathane
Description
Evaluating plant stand count or classifying weeds by manual scouting is time-consuming, laborious, and subject to human errors. Proximal remote sensed imagery used in conjunction with machine vision algorithms can be used for these purposes. Despite its great potential, the rate of using these technologies is still slow due to their subscription cost and data privacy issues. Therefore, in this research, open-source image processing software, ImageJ and Python that support in-house processing, was used to develop algorithms to evaluate stand count, develop spatial distribution maps, and classify the four common weeds of North Dakota. A novel sliding and shifting region of interest method was developed for plant stand count. Handcrafted simple image processing and machine learning approaches with shape features were successfully employed for weed species classification. Such tools and methodologies using open-source platforms can be extended to other scenarios and are expected to be impactful and helpful to stakeholders.
Recommended Citation
Pathak, Harsh, "Machine Vision Methods for Evaluating Plant Stand Count and Weed Classification Using Open-Source Platforms" (2021). Agribusiness and Biosystems Engineering. 3.
https://digitalcommons.ndsu.edu/agribusiness-biosystems-engi/3
Rights
NDSU Policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Author ORCID Identifier
0000-0002-6739-3473
Handle Identifier
https://hdl.handle.net/10365/32706