Using pre-trained convolutional neural networks to predict maturity levels of strawberries

Bachelor thesis (2022)

Authors

J.R. Buitenweg Electrical Engineering, Mathematics and Computer Science

Contributors

J. Wen Algorithmics - EEMCS (supervisor 1)
T.E.P.M.F. Abeel Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
A. Nadeem Cyber Security - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Jurriaan Buitenweg
Convolutional Neural Network Classification Object detection
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Published Date

21-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

To reduce food waste, the strawberry harvesting process should be optimized. In the modern era, computer vision can provide huge amounts of help. This paper focuses on optimizing pre-trained convolutional neural networks (CNN) to determine the maturity level of strawberries on a 1-10 scale. Here, 1 means unripe and 10 means overripe. Maturity level 8 is marketable. Experiments are done with VGG19, Resnet50, InceptionV2, Alexnet, and EfficientNetB2 as classifiers on segments using ADAM and SGD as optimizers and cross-entropy as loss function. The same CNN's are applied as a backbone for FasterRCNN to see how they would behave within an object detection architecture. The biggest challenge during this research was the low amount of training data. The research showed that using convolutional neural networks as a maturity level predictor is possible, but a well made training set with an equal spread for each maturity level is necessary to possibly achieve high accuracy.