Print Email Facebook Twitter Using pre-trained convolutional neural networks to predict maturity levels of strawberries Title Using pre-trained convolutional neural networks to predict maturity levels of strawberries Author Buitenweg, Jurriaan (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Wen, J. (mentor) Abeel, T.E.P.M.F. (mentor) Nadeem, A. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2022-06-21 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. Subject Convolutional Neural NetworkClassificationObject detection To reference this document use: http://resolver.tudelft.nl/uuid:9f5701e3-8bcb-4fd5-ae79-cc55bff65141 Part of collection Student theses Document type bachelor thesis Rights © 2022 Jurriaan Buitenweg Files PDF Research_paper_Jurriaan_B ... tenweg.pdf 4.26 MB Close viewer /islandora/object/uuid:9f5701e3-8bcb-4fd5-ae79-cc55bff65141/datastream/OBJ/view