Abstract

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 field image. The characterized color for a specific nutrient deficiency is detected by the C-R indices. The pattern of the detected color on the leaf is then examined to determine the specific nutrient deficiency. The proposed approaches are validated in a commercial strawberry farm.

Notes

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Graduation Date

2019

Semester

Summer

Advisor

Xu, Yunjun

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Mechanical and Aerospace Engineering

Degree Program

Mechanical Engineering

Format

application/pdf

Identifier

CFE0007666

URL

http://purl.fcla.edu/fcla/etd/CFE0007666

Language

English

Release Date

August 2022

Length of Campus-only Access

3 years

Access Status

Doctoral Dissertation (Campus-only Access)

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