Classification of tomato (Solanum lycopersicum) leaf diseases using color texture features and Artificial Neural Networks (ANN) .
Date
10-2011
Degree
Bachelor of Science in Applied Physics
College
College of Arts and Sciences (CAS)
Adviser/Committee Chair
Alexandra B. Santos
Co-adviser
Darwin B. Putungan
Abstract
A method for identifying healthy and diseased regions of tomato leaves was developed. This was implemented by performing texture feature analysis on the Gray-Level Co-Occurrence Matrices (GLCM) images of the leaves. Different regions were classified using a trained two-layer feedforward backpropagation artificial neural network. Early Blight regions were detected with up to 90.9% accuracy; Phoma blight regions, 86.7%; and healthy regions, 100%. It was also found that the Hue color layer is better used when differentiating between Early and Phoma Blight, and the Intensity color layer when comparing diseased regions to healthy ones. Keywords: Image Processing (07.05.Pj); Neural networks (07.05.Mh); Matrix Theory (02.10.Yn)
Language
English
Location
UPLB Main Library Special Collections Section (USCS)
Call Number
Thesis
Recommended Citation
Natividad, Darryl Kim C., "Classification of tomato (Solanum lycopersicum) leaf diseases using color texture features and Artificial Neural Networks (ANN) ." (2011). Undergraduate Theses. 10465.
https://www.ukdr.uplb.edu.ph/etd-undergrad/10465
Document Type
Thesis