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Identification of sunflower seeds with deep convolutional neural networks

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Abstract

In 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.

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Acknowledgements

The author is grateful to Prof. Dr. Mehmet Sincik, a researcher in the Dept. of Field Crops, Agriculture Faculty, Bursa Uludag University, for kindly providing the seeds of Helianthus annulus L.

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  1. Department of Biosystems Engineering, Faculty of Agriculture, Bursa Uludag University, 16059, Bursa, Turkey

    Ferhat Kurtulmuş

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  1. Ferhat Kurtulmuş
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Correspondence to Ferhat Kurtulmuş.

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Kurtulmuş, F. Identification of sunflower seeds with deep convolutional neural networks. Food Measure 15, 1024–1033 (2021). https://doi.org/10.1007/s11694-020-00707-7

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  • DOI: https://doi.org/10.1007/s11694-020-00707-7

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