國立虎尾科技大學 |

Soybean Leaf Chlorophyll Estimation and Iron Deficiency Field Rating Determination at Plot and Field Scales Through Image Processing and Machine Learning.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Soybean Leaf Chlorophyll Estimation and Iron Deficiency Field Rating Determination at Plot and Field Scales Through Image Processing and Machine Learning./
作者:	Hassanijalilian, Oveis.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	206 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
Contained By:	Dissertations Abstracts International82-02B.
標題:	Agricultural engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28026395
ISBN:	9798664730401

Soybean Leaf Chlorophyll Estimation and Iron Deficiency Field Rating Determination at Plot and Field Scales Through Image Processing and Machine Learning.
Hassanijalilian, Oveis.

Soybean Leaf Chlorophyll Estimation and Iron Deficiency Field Rating Determination at Plot and Field Scales Through Image Processing and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 206 p.

Source: Dissertations Abstracts International, Volume: 82-02, Section: B.

Thesis (Ph.D.)--North Dakota State University, 2020.

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

Iron deficiency chlorosis (IDC) is the most common reason for chlorosis in soybean (Glycine max (L.) Merrill) and causes an average yield loss of 120 million dollars per year across 1.8 million ha in the North Central US alone. As the most effective way to avoid IDC is the use of tolerant cultivars, they are visually rated for IDC by experts; however, this method is subjective and not feasible for a larger scale. An alternate more objective image processing method can be implemented in various platforms and fields. This approach relies on a color vegetation index (CVI) that can quantify chlorophyll, as chlorophyll content is a good IDC indicator. Therefore, this research is aimed at developing image processing methods at leaf, plot, and field scales with machine learning methods for chlorophyll and IDC measurement. This study also reviewed and synthesized the IDC measurement and management methods. Smartphone digital images with machine learning models successfully estimated the chlorophyll content of soybean leaves infield. Dark green color index (DGCI) was the best-correlated CVI with chlorophyll. The pixel count of four different ranges of DGCI (RPC) was used as input features for different models, and the support vector machine produced the highest performance. Handheld camera images of soybean plots extracted DGCI, which mimicked visual rating, and canopy size that were used as inputs to decision-tree based models for IDC classification. The AdaBoost model was the best model in classifying IDC severity. Unmanned aerial vehicle soybean IDC cultivar trial fields images extracted DGCI, canopy size, and their product (CDP) for IDC severity monitoring and yield prediction. The area under the curve (AUC) was employed to aggregate the data into a single value through time, and the correlation between all the features and yield was good. Although CDP at latest growth stage had the highest correlation with yield, AUC of CDP was the most consistent index for soybean yield prediction. This research demonstrated that digital image processing along with the machine learning methods can be successfully applied to the soybean IDC measurement and the various soybean related stakeholders can benefit from this research.

ISBN: 9798664730401Subjects--Topical Terms:

1148660
Agricultural engineering.
Subjects--Index Terms:

Chlorophyll

Soybean Leaf Chlorophyll Estimation and Iron Deficiency Field Rating Determination at Plot and Field Scales Through Image Processing and Machine Learning.
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520 $a Iron deficiency chlorosis (IDC) is the most common reason for chlorosis in soybean (Glycine max (L.) Merrill) and causes an average yield loss of 120 million dollars per year across 1.8 million ha in the North Central US alone. As the most effective way to avoid IDC is the use of tolerant cultivars, they are visually rated for IDC by experts; however, this method is subjective and not feasible for a larger scale. An alternate more objective image processing method can be implemented in various platforms and fields. This approach relies on a color vegetation index (CVI) that can quantify chlorophyll, as chlorophyll content is a good IDC indicator. Therefore, this research is aimed at developing image processing methods at leaf, plot, and field scales with machine learning methods for chlorophyll and IDC measurement. This study also reviewed and synthesized the IDC measurement and management methods. Smartphone digital images with machine learning models successfully estimated the chlorophyll content of soybean leaves infield. Dark green color index (DGCI) was the best-correlated CVI with chlorophyll. The pixel count of four different ranges of DGCI (RPC) was used as input features for different models, and the support vector machine produced the highest performance. Handheld camera images of soybean plots extracted DGCI, which mimicked visual rating, and canopy size that were used as inputs to decision-tree based models for IDC classification. The AdaBoost model was the best model in classifying IDC severity. Unmanned aerial vehicle soybean IDC cultivar trial fields images extracted DGCI, canopy size, and their product (CDP) for IDC severity monitoring and yield prediction. The area under the curve (AUC) was employed to aggregate the data into a single value through time, and the correlation between all the features and yield was good. Although CDP at latest growth stage had the highest correlation with yield, AUC of CDP was the most consistent index for soybean yield prediction. This research demonstrated that digital image processing along with the machine learning methods can be successfully applied to the soybean IDC measurement and the various soybean related stakeholders can benefit from this research.
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653 $a Soybean
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