Design and implementation of a tomato leaf blight disease detection system using machine learning and image processing
Date
2021-05
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Abstract
Tomato production in Sub-Saharan Africa can be plagued by a fungal disease known as leaf
blight. The tomato blight disease can have devastating effects on smallholder farmers if
measures are not taken early. Currently, the most common method of leaf disease diagnosis is
an estimation by human judgment. This method is very error-prone and can be quite tedious
given the volume of plants on a typical tomato farm. This research investigates the creation of a
faster disease detection method using image processing and machine learning. Similar solutions
in the past that employ these technologies typically have to connect to an offsite server to
perform all processing and diagnosis. This project explores the deployment of compressed,
quantized models on small edge devices which can produce accurate results at a significantly
low cost. The tomato blight disease is characterized by brown leaf spots. The system could
classify a diseased leaf with brown spots from a healthy leaf with an accuracy of 90% ± 12.32.
Description
Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, May 2021
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Capstone project
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Keywords
robotics, agricultural surveillance devices