Daşkın, Zeynep DilanKhan, Muhammad Umerİrfanoğlu, BülentAlam, Muhammad ShahabMechatronics EngineeringDepartment of Mechatronics Engineering2024-07-082024-07-0820232524-7573https://hdl.handle.net/20.500.14411/6515Published 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.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.enConvolutional neural networks, strawberry classification, ensemble, smart farming, precision agriculture, deep learningArticle