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dc.contributor.authorPathak, Harsh
dc.description.abstractEvaluating 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.en_US
dc.publisherNorth Dakota State Universityen_US
dc.rightsNDSU Policy 190.6.2
dc.titleMachine Vision Methods for Evaluating Plant Stand Count and Weed Classification Using Open-Source Platformsen_US
dc.typeThesisen_US
dc.date.accessioned2022-06-07T19:28:05Z
dc.date.available2022-06-07T19:28:05Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/10365/32706
dc.subjectimage processingen_US
dc.subjectmachine learningen_US
dc.subjectmachine visionen_US
dc.subjectopen-sourceen_US
dc.subjectprecision agricultureen_US
dc.subjectremote sensingen_US
dc.identifier.orcid0000-0002-6739-3473
dc.rights.urihttps://www.ndsu.edu/fileadmin/policy/190.pdf
ndsu.degreeMaster of Science (MS)en_US
ndsu.collegeAgriculture, Food Systems and Natural Resourcesen_US
ndsu.departmentAgricultural and Biosystems Engineeringen_US
ndsu.programAgricultural and Biosystems Engineeringen_US
ndsu.advisorCannayen, Igathinathane


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