國立虎尾科技大學 |

Assessing Roots Distribution of Tart Cherry Tree Using Ground Penetrating Radar (GPR) and Artificial Intelligence.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Assessing Roots Distribution of Tart Cherry Tree Using Ground Penetrating Radar (GPR) and Artificial Intelligence./
作者:	Salako, John Oludemilade.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	109 p.
附註:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
標題:	Agriculture. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30426868
ISBN:	9798379448134

Assessing Roots Distribution of Tart Cherry Tree Using Ground Penetrating Radar (GPR) and Artificial Intelligence.
Salako, John Oludemilade.

Assessing Roots Distribution of Tart Cherry Tree Using Ground Penetrating Radar (GPR) and Artificial Intelligence. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 109 p.

Source: Masters Abstracts International, Volume: 84-11.

Thesis (M.S.)--Michigan State University, 2023.

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

The importance of tree cultivation and management is necessary for the 21st century, given the need to sequestrate carbon and secure adequate food and raw materials productivity, which are part of the ecosystem services trees provide. This study improves upon previous studies and bridges the gap in assessing the roots of trees using non-invasive approaches. This study assessed the root distribution of Tart cherry trees using ground-penetrating radar (GPR) and artificial intelligence. Grid and cylindrical data collection and processing methodology were employed using the 800 MHz antenna frequency. Three mature trees were sampled from two Tart cherry fields in Michigan State (Clarksville and Traverse City). The reconstruction results revealed that the roots extend 30-45 cm deep in the soil. Furthermore, an Unmanned Aerial Vehicle (Matrix 100 drone) was used to obtain RGB aerial images from both fields.The findings of this study show that Tart cherry tree roots extend farther than the canopy size, as discussed extensively in this Thesis. A controlled experiment was developed to serve as ground truth in assessing the GPR's accuracy. The reconstructed result showed that the GPR accurately reconstructed and measured the depth the proxies were buried and the length of the root proxies. The biomass weight model estimator was another novel idea developed in this study. The model was developed using 115 root proxies, where the measured biomass length, width, and circumference were used as independent variables in predicting the weight of the biomass. Four regressor algorithms were used in developing the weight model. 5-fold cross-validation showed that the model performed optimally with an error of about 6% in the weight prediction. This study highlights the potential of GPR and artificial intelligence in assessing root distribution in Tart cherry trees, offering valuable insights for optimizing tree management and growth.

ISBN: 9798379448134Subjects--Topical Terms:

660421
Agriculture.
Subjects--Index Terms:

3D roots reconstruction

Assessing Roots Distribution of Tart Cherry Tree Using Ground Penetrating Radar (GPR) and Artificial Intelligence.
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100 1 $a Salako, John Oludemilade. $3 1437726
245 1 0 $a Assessing Roots Distribution of Tart Cherry Tree Using Ground Penetrating Radar (GPR) and Artificial Intelligence.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 109 p.
500 $a Source: Masters Abstracts International, Volume: 84-11.
500 $a Advisor: Basso, Bruno.
502 $a Thesis (M.S.)--Michigan State University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a The importance of tree cultivation and management is necessary for the 21st century, given the need to sequestrate carbon and secure adequate food and raw materials productivity, which are part of the ecosystem services trees provide. This study improves upon previous studies and bridges the gap in assessing the roots of trees using non-invasive approaches. This study assessed the root distribution of Tart cherry trees using ground-penetrating radar (GPR) and artificial intelligence. Grid and cylindrical data collection and processing methodology were employed using the 800 MHz antenna frequency. Three mature trees were sampled from two Tart cherry fields in Michigan State (Clarksville and Traverse City). The reconstruction results revealed that the roots extend 30-45 cm deep in the soil. Furthermore, an Unmanned Aerial Vehicle (Matrix 100 drone) was used to obtain RGB aerial images from both fields.The findings of this study show that Tart cherry tree roots extend farther than the canopy size, as discussed extensively in this Thesis. A controlled experiment was developed to serve as ground truth in assessing the GPR's accuracy. The reconstructed result showed that the GPR accurately reconstructed and measured the depth the proxies were buried and the length of the root proxies. The biomass weight model estimator was another novel idea developed in this study. The model was developed using 115 root proxies, where the measured biomass length, width, and circumference were used as independent variables in predicting the weight of the biomass. Four regressor algorithms were used in developing the weight model. 5-fold cross-validation showed that the model performed optimally with an error of about 6% in the weight prediction. This study highlights the potential of GPR and artificial intelligence in assessing root distribution in Tart cherry trees, offering valuable insights for optimizing tree management and growth.
590 $a School code: 0128.
650 4 $a Agriculture. $3 660421
650 4 $a Remote sensing. $3 557272
650 4 $a Geology. $3 670379
653 $a 3D roots reconstruction
653 $a Artificial intelligence
653 $a Ground penetrating radar
653 $a Tart cherry tree
653 $a Weight model
690 $a 0372
690 $a 0799
690 $a 0473
710 2 $a Michigan State University. $b Geological Sciences - Master of Science. $3 1437727
773 0 $t Masters Abstracts International $g 84-11.
790 $a 0128
791 $a M.S.
792 $a 2023
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30426868