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Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

Author

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  • Kadir Sabanci

    (Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Turkey)

Abstract

In the present study, a deep learning-based two-scenario method is proposed to distinguish tomato seed cultivars. First, images of seeds of four different tomato cultivars (Sacher F1, Green Zebra, Pineapple, and Ozarowski) were taken. Each seed was then cropped on the raw image and saved as a new image. The number of images in the dataset was increased using data augmentation techniques. In the first scenario, these seed images were classified with four different CNN (convolutional neural network) models (ResNet18, ResNet50, GoogleNet, and MobileNetv2). The highest classification accuracy of 93.44% was obtained with the MobileNetv2 model. In the second scenario, 1280 deep features obtained from MobileNetv2 fed the inputs of the Bidirectional Long Short-Term Memory (BiLSTM) network. In the classification made using the BiLSTM network, 96.09% accuracy was obtained. The results show that different tomato seed cultivars can be distinguished quickly and accurately by the proposed deep learning-based method. The performed study is a great novelty in distinguishing seed cultivars and the developed innovative approach involving deep learning in tomato seed image analysis, and can be used as a comprehensive procedure for practical tomato seed classification.

Suggested Citation

  • Kadir Sabanci, 2023. "Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars," Sustainability, MDPI, vol. 15(5), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4443-:d:1085340
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    References listed on IDEAS

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    1. Peng Xu & Qian Tan & Yunpeng Zhang & Xiantao Zha & Songmei Yang & Ranbing Yang, 2022. "Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning," Agriculture, MDPI, vol. 12(2), pages 1-16, February.
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    Cited by:

    1. Huan Lin & Xiaolei Deng & Jianping Yu & Xiaoliang Jiang & Dongsong Zhang, 2023. "A Study of Sustainable Product Design Evaluation Based on the Analytic Hierarchy Process and Deep Residual Networks," Sustainability, MDPI, vol. 15(19), pages 1-22, October.

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