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Machine Vision Methods for Evaluating Plant Stand Count and Weed Classification Using Open-Source Platforms.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Vision Methods for Evaluating Plant Stand Count and Weed Classification Using Open-Source Platforms./
作者:	Pathak, Harsh.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	161 p.
附註:	Source: Masters Abstracts International, Volume: 83-03.
Contained By:	Masters Abstracts International83-03.
標題:	Agricultural engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28714624
ISBN:	9798538110506

Machine Vision Methods for Evaluating Plant Stand Count and Weed Classification Using Open-Source Platforms.
Pathak, Harsh.

Machine Vision Methods for Evaluating Plant Stand Count and Weed Classification Using Open-Source Platforms. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 161 p.

Source: Masters Abstracts International, Volume: 83-03.

Thesis (M.S.)--North Dakota State University, 2021.

This item must not be sold to any third party vendors.

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.

ISBN: 9798538110506Subjects--Topical Terms:

3168406
Agricultural engineering.
Subjects--Index Terms:

Image Processing

Machine Vision Methods for Evaluating Plant Stand Count and Weed Classification Using Open-Source Platforms.
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