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.
If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu.
Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Mechanical and Aerospace Engineering
Length of Campus-only Access
Doctoral Dissertation (Campus-only Access)
Kong, Xiangling, "Color-Ratio Based Strawberry Plant Localization and Nutrition Deficiency Detection" (2019). Electronic Theses and Dissertations. 6518.