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Machine Vision System for the Automa...

Majeed, Yaqoob.

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Machine Vision System for the Automated Green Shoot Thinning in Vineyards.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Vision System for the Automated Green Shoot Thinning in Vineyards./
Author:	Majeed, Yaqoob.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	196 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
Contained By:	Dissertations Abstracts International82-04B.
Subject:	Robotics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27830466
ISBN:	9798678105196

Machine Vision System for the Automated Green Shoot Thinning in Vineyards.
Majeed, Yaqoob.

Machine Vision System for the Automated Green Shoot Thinning in Vineyards. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 196 p.

Source: Dissertations Abstracts International, Volume: 82-04, Section: B.

Thesis (Ph.D.)--Washington State University, 2020.

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

Green shoot thinning (removing unnecessary shoots to redirect vines energy to most significant shoots) in vineyards is used to reduce the crop load to achieve higher flavor concentration in wine grapes. Mechanical green shoot thinning reduces the labor usage by ~25 times compared to manual green shoot thinning. However, due to difficulty in precise placement of the thinning end-effector to the trajectories of cordons, cluster removal efficiencies of green shoots vary between 10-85%. Automating the mechanical thinning operation could help to substantially increase its efficiency and performance. For the automated thinning operation, first step is to develop a machine vision system that can determine cordon trajectories during thinning season in real-field conditions. However, during thinning season growth of shoots/leaves occlude significant portion of cordons making it challenging to accurately determine their trajectories. The focus of this research is on the study and evaluation of a machine vision system and integrated prototype for automated green shoot thinning in vineyards. A deep learning-based machine vision system was designed to estimate the cordon trajectories from different growth stages of green shoots under real vineyard environment. The proposed approach considers the location information of visible segments of trunk, cordon and density of shoots/leaves to accurately estimate the cordon trajectories in full foliage canopies during varying growth stages of green shoots. This deep learning-based approach helped to estimate cordon trajectories with high correlation coefficient of 0.997, 0.996, and 0.991 from different growth stages of green shoots (week 2 through week 4). The integrated automated green shoot thinning prototype with a low cost RGB-D (red, green, blue, and depth) camera can precisely position the thinning end-effector to the estimated cordon trajectories with an Root Mean Squared Error (RMSE) of 1.47 cm at forward speed of 6.6 cm.s-1(0.24 km.h-1) with averaged initial processing time of 8 s for a single cordon. The results from this study showed the potential of machine vision-based automated green shoot thinning operation in vineyards.

ISBN: 9798678105196Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

Artificial intelligence in agriculture

Machine Vision System for the Automated Green Shoot Thinning in Vineyards.
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300 $a 196 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
500 $a Advisor: Zhang, Qin;Karkee, Manoj.
502 $a Thesis (Ph.D.)--Washington State University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Green shoot thinning (removing unnecessary shoots to redirect vines energy to most significant shoots) in vineyards is used to reduce the crop load to achieve higher flavor concentration in wine grapes. Mechanical green shoot thinning reduces the labor usage by ~25 times compared to manual green shoot thinning. However, due to difficulty in precise placement of the thinning end-effector to the trajectories of cordons, cluster removal efficiencies of green shoots vary between 10-85%. Automating the mechanical thinning operation could help to substantially increase its efficiency and performance. For the automated thinning operation, first step is to develop a machine vision system that can determine cordon trajectories during thinning season in real-field conditions. However, during thinning season growth of shoots/leaves occlude significant portion of cordons making it challenging to accurately determine their trajectories. The focus of this research is on the study and evaluation of a machine vision system and integrated prototype for automated green shoot thinning in vineyards. A deep learning-based machine vision system was designed to estimate the cordon trajectories from different growth stages of green shoots under real vineyard environment. The proposed approach considers the location information of visible segments of trunk, cordon and density of shoots/leaves to accurately estimate the cordon trajectories in full foliage canopies during varying growth stages of green shoots. This deep learning-based approach helped to estimate cordon trajectories with high correlation coefficient of 0.997, 0.996, and 0.991 from different growth stages of green shoots (week 2 through week 4). The integrated automated green shoot thinning prototype with a low cost RGB-D (red, green, blue, and depth) camera can precisely position the thinning end-effector to the estimated cordon trajectories with an Root Mean Squared Error (RMSE) of 1.47 cm at forward speed of 6.6 cm.s-1(0.24 km.h-1) with averaged initial processing time of 8 s for a single cordon. The results from this study showed the potential of machine vision-based automated green shoot thinning operation in vineyards.
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653 $a Deep learning
653 $a Grapevine canopy detection
653 $a Machine vision in agriculture
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