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Strawberries Maturity Level Detection Using Convolutional Neural Network (CNN) and Ensemble Method

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dc.contributor.authorDaşkın, Zeynep Dilan
dc.contributor.authorKhan, Muhammad Umer
dc.contributor.authorİrfanoğlu, Bülent
dc.contributor.authorAlam, Muhammad Shahab
dc.date.accessioned2023-12-26T08:03:55Z
dc.date.available2023-12-26T08:03:55Z
dc.date.issued2023-08-01
dc.descriptionPublished by Computer Vision and Machine Learning in Agriculture, Part of the Algorithms for Intelligent Systems book series (AIS) ISBN 978-981-99-3754-7, https://doi.org/10.1007/978-981-99-3754-7_10, Zeynep Dilan Daşkın & Muhammad Umer Khan, Department of Mechatronics Engineering, Atilim University, Ankara, 06830, Turkey, Bulent Irfanoglu, Department of Electrical and Electronics Engineering, Baskent University, Ankara, 06790, Turkey, Muhammad Shahab Alam, Defense Technologies Institute, Gebze Technical University, Kocaeli, 41400, Turkey.
dc.description.abstractHarvesting high-quality products at an affordable expense has been the prime incentive for the agriculture industry. Automation and intelligent software technology is playing a pivotal role in achieving both practical and effective solutions. In this study, we developed a robust deep learning-based vision framework to detect and classify strawberries according to their maturity levels. Due to the unavailability of the relevant dataset, we built up a novel dataset comprising 900 strawberry images to evaluate the performance of existing convolutional neural network (CNN) models under complex background conditions. The overall dataset is categorized into three classes: mature, semi-mature, and immature. The existing classifiers evaluated during this study are AlexNet, GoogleNet, SqueezeNet, DenseNet, and VGG-16. To further improve the overall prediction accuracy, two Ensemble methods are proposed based on SqueezeNet, GoogleNet, and VGG-16. Based on the considered performance matrices, SqueezeNet is recommended as the most effective model among all the classifiers and networks for detecting and classifying the maturity levels of strawberries.
dc.identifier.citationhttp://hdl.handle.net/20.500.14411/1930
dc.identifier.issn2524-7573
dc.identifier.urihttps://doi.org/10.1007/978-981-99-3754-7_10
dc.language.isoen
dc.publisherComputer Vision and Machine Learning in Agriculture
dc.relation.ispartofseries3
dc.subjectConvolutional neural networks, strawberry classification, ensemble, smart farming, precision agriculture, deep learning
dc.titleStrawberries Maturity Level Detection Using Convolutional Neural Network (CNN) and Ensemble Method
dc.typeArticle
dspace.entity.typeArticle

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