2023-01-302023-01-302020-10-13Kurtulmuş, F. (2020). "Identification of sunflower seeds with deep convolutional neural networks". Journal of Food Measurement and Characterization, 15(2), 1024-1033.2193-41262193-4134https://doi.org/10.1007/s11694-020-00707-7https://link.springer.com/article/10.1007/s11694-020-00707-7http://hdl.handle.net/11452/30708In the food and agricultural industries, it is crucial to identify and to choose correct sunflower seeds that meet specific requirements. Deep learning and computer vision methods can help identify sunflower seeds. In this study, a computer vision system was proposed, trained, and tested to identify four varieties of sunflower seeds using deep learning methodology and a regular color camera. Image acquisition was carried out under controlled illumination conditions. An image segmentation procedure was employed to reduce the workload in obtaining training images required for training deep convolutional neural network models. Three deep learning architectures, namely AlexNet, GoogleNet, and ResNet, were investigated for identifying sunflower seeds in this study. Different solver types were also evaluated to determine the best deep learning model in terms of both accuracy and training time. About 4800 sunflower seeds were inspected individually for training and testing. The highest classification accuracy (95%) was succeeded with the GoogleNet algorithm.eninfo:eu-repo/semantics/closedAccessFood science & technologySunflowerSeed classificationDeep learningNeural networksComputer visionVisionImageMachineSystemAgricultural robotsAgricultureComputer visionConvolutionConvolutional neural networksDeep neural networksImage segmentationLearning systemsAgricultural industriesClassification accuracyComputer vision systemIllumination conditionsLearning architecturesLearning architecturesLearning modelsSegmentation procedureTraining and testingDeep learningArticle0005796799000032-s2.0-8509269186310241033152Food science & technologyCorn Ears; Seed; Computer Vision