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Color-Ratio Based Strawberry Plant Localization and Nutrition Deficiency Detection

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Date Issued:
2019
Abstract/Description:
In recent years, precision agriculture has become popular anticipating to partially meet the needs of an ever-growing population with limited resources. Plant localization and nutrient de?ciency detection are two important tasks in precision agriculture. In this dissertation, these two tasks are studied by using a new color-ratio(C-R) index technique. Firstly, a low cost and light scene invariant approach is proposed to detect green and yellow leaves based on the color-ratio (C-R) indices. A plant localization approach is then developed using the relative pixel relationships of adjacent plants. Secondly, the Sobel operator and morphology techniques are applied to segment the target strawberry leaf from a ?eld image. The characterized color for a speci?c nutrient de?ciency is detected by the C-R indices. The pattern of the detected color on the leaf is then examined to determine the speci?c nutrient de?ciency. The proposed approaches are validated in a commercial strawberry farm.
Title: Color-Ratio Based Strawberry Plant Localization and Nutrition Deficiency Detection.
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Name(s): Kong, Xiangling, Author
Xu, Yunjun, Committee Chair
Elgohary, Tarek, Committee Member
Fu, Qiushi, Committee Member
Wu, Dazhong, Committee Member
Wang, Liqiang, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2019
Publisher: University of Central Florida
Language(s): English
Abstract/Description: In recent years, precision agriculture has become popular anticipating to partially meet the needs of an ever-growing population with limited resources. Plant localization and nutrient de?ciency detection are two important tasks in precision agriculture. In this dissertation, these two tasks are studied by using a new color-ratio(C-R) index technique. Firstly, a low cost and light scene invariant approach is proposed to detect green and yellow leaves based on the color-ratio (C-R) indices. A plant localization approach is then developed using the relative pixel relationships of adjacent plants. Secondly, the Sobel operator and morphology techniques are applied to segment the target strawberry leaf from a ?eld image. The characterized color for a speci?c nutrient de?ciency is detected by the C-R indices. The pattern of the detected color on the leaf is then examined to determine the speci?c nutrient de?ciency. The proposed approaches are validated in a commercial strawberry farm.
Identifier: CFE0007666 (IID), ucf:52482 (fedora)
Note(s): 2019-08-01
Ph.D.
Engineering and Computer Science, Mechanical and Aerospace Engineering
Doctoral
This record was generated from author submitted information.
Subject(s): Agriculture Ground Robot -- Color-Ratio Index -- Color Detection -- Crop Localization -- Crop Nutrient Deficiency Detection
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0007666
Restrictions on Access: campus 2022-08-15
Host Institution: UCF

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