Degree Type

Thesis

Date of Award

2019

Degree Name

Master of Science

Department

Mechanical Engineering

Major

Mechanical Engineering

First Advisor

Pranav Shrotriya

Abstract

Early detection and quantification of diseases in food plants are critical to agriculture industry and national food security. However, limitation in technology and cost has limited the success of applying Computer Vision in Plant Science. This research builds on the recent advance of Machine Learning, GPU and smartphones to tackle the problem of fast and low cost diagnosis of plant disease. In particular, we choose soybean as the subject for applying automatic disease detection. The reason is because soybean is an important crop for the state of Iowa and an important source of food for America. The plant is however, highly vulnerable to several type of diseases. This thesis consists of two sub-analyses of soybean diseases, which are: First, detection of a single disease in soybean, particularly Sudden Death Syndrome (SDS) with high detail (including location and severity). Second, detection of multiple diseases in soybean, using mobile phones which are resource- constrained

Copyright Owner

Xuan Truong Tran

Language

en

File Format

application/pdf

File Size

73 pages

Share

COinS