Strawberry Detection Under Various Harvestation Stages

<p>This paper analyzes three techniques attempting to detect strawberries at various stages in its growth cycle. Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Convolutional Neural Networks (CNN) were implemented on a limited custom-built dataset. The methodologies were compared in terms of accuracy and computational efficiency. Computational efficiency is defined in terms of image resolution as testing on a smaller dimensional image is much quicker than larger dimensions. The CNN based implementation obtained the best results with an 88% accuracy at the highest level of efficiency as well (600x800). LBP generated moderate results with a 74% detection accuracy at an inefficient rate (5000x4000). Finally, HOG’s results were inconclusive as it performed poorly early on, generating too many misclassifications. </p>

Name thesis:degree_name

MS in Electrical Engineering

Discipline thesis:degree_discipline

Electrical Engineering

Year dc:date.available

2019

Author dc:creator

• Fitter, Yavisht

Contributors dc:contributor

• Jane Zhang

• Computer Vision
• Deep Learning
• Electrical and Computer Engineering

Identifier

10.15368/theses.2019.5

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-3369