Diğer Yayınlar

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14411/27

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Browsing Diğer Yayınlar by Author "Alam, Muhammad Shahab"

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    Strawberries Maturity Level Detection Using Convolutional Neural Network (cnn) and Ensemble Method
    (Computer Vision and Machine Learning in Agriculture, 2023) Daşkın, Zeynep Dilan; Khan, Muhammad Umer; İrfanoğlu, Bülent; Alam, Muhammad Shahab; Mechatronics Engineering; Department of Mechatronics Engineering; 15. Graduate School of Natural and Applied Sciences; 06. School Of Engineering; 01. Atılım University
    Harvesting 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.