Detecting strawberries using different Convolutional Neural Networks

Bachelor thesis (2022)

Authors

J.W.J. Bechtold Electrical Engineering, Mathematics and Computer Science

Contributors

T.E.P.M.F. Abeel Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
J. Wen Algorithmics - EEMCS (supervisor 1)
A. Nadeem Cyber Security - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Jeroen Bechtold
Deep learning Convolutional neural network Strawberry Segmentation
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Published Date

21-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This paper tries to combat the food waste of strawberries during the harvesting steps.
An automatic pipeline must be established to combat this food waste.
One of the steps needed in this pipeline is detecting strawberries in images.
Therefore, this paper aims to find out which Convolutional Neural Network (CNN) can be best used to detect strawberries.
Faster r-cnn, Mask r-cnn and RetinaNet are compared against each other using different setting.
Mask r-cnn achieved the highest average bounding box and segmentation mAP with 51.63 and 73.20 respectively.