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Deep Learning Based Classification System for Recognizing Local Spinach

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Advances in Deep Learning, Artificial Intelligence and Robotics


A deep learning model gives an incredible result for image processing by studying from the trained dataset. Spinach is a leaf vegetable that contains vitamins and nutrients. In our research, a Deep learning method has been used that can automatically identify spinach and this method has a dataset of a total of five species of spinach that contains 3785 images. Four Convolutional Neural Network (CNN) models were used to classify our spinach. These models give more accurate results for image classification. Before applying these models there is some preprocessing of the image data. For the preprocessing of data, some methods need to happen. Those are RGB conversion, filtering, resize and rescaling, and categorization. After applying these methods image data are preprocessed and ready to be used in the classifier algorithms. The accuracy of these classifiers is in between 98.68 and 99.79%. Among those models, VGG16 achieved the highest accuracy of 99.79%.

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  1. Roughani, Mehdi, M.S.: Spinach: an important green leafy vegetable and medicinal herb. In: 2nd International Conference on Medicinal Plants, Organic Farming, Natural and Pharmaceutical Ingredients (2019)

    Google Scholar 

  2. Islam, R., Ara, T.: Leafy vegetables in Bangladesh. UBN: 015-A94510112010, Photon ebooks (2015)

    Google Scholar 

  3. Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y., Chang, Y., Xiang, Q.: A leaf recognition algorithm for plant classification using probabilistic neural network. In: 2007 IEEE International Symposium on Signal Processing and Information Technology, vol. 2007, pp. 11–16. Giza (2007)

    Google Scholar 

  4. Wang, G., Sun, Y., Wang, J.: Automatic image-based plant disease severity estimation using deep learning. Comput. Intell. Neurosci. 2017, 1–8 (2017)

    Google Scholar 

  5. Sun, Y., Liu, Y., Wang, G., Zhang, H.: Deep learning for plant identification in natural environment. Comput. Intell. Neurosci. 2017, 1–6 (2017)

    Google Scholar 

  6. Zhu, S., Feng, L., Zhang, C., Bao, Y., He, Y.: Identifying freshness of spinach leaves stored at different temperatures using hyperspectral imaging. Foods 8, 356 (2019)

    Google Scholar 

  7. Yalcin, H., Razavi, S.: Plant classification using convolutional neural networks. In: 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pp. 1–5. Tianjin (2016)

    Google Scholar 

  8. Kaya, A., Keceli, A., Catal, C., Yalic, H., Temucin, H., Tekinerdogan, B.: Analysis of transfer learning for deep neural network based plant classification models. Comput. Electron. Agric. 158, 20–29 (2019)

    Article  Google Scholar 

  9. Priya, C.A.T., Balasaravanan, A., Thanamani, S.: An efficient leaf recognition algorithm for plant classification using support vector machine. In: International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012), pp. 428–432. Salem, Tamilnadu (2012)

    Google Scholar 

  10. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9. Boston, MA (2015)

    Google Scholar 

  11. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826. Las Vegas, NV (2016)

    Google Scholar 

  12. Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)

    Google Scholar 

  13. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ArXiv preprint arXiv:1409.1556 (2014)

  14. Dalwinder, S., Birmohan, S.: Investigating the impact of data normalization on classification performance. Appl. Soft Comput. 105524 ISSN: 1568–4946 (2019)

    Google Scholar 

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We gratefully acknowledge for providing GPU support from Computational Intelligence Lab for providing the necessary support. We thank, Dept. of CSE, Daffodil International University. Moreover, thanks to the anonymous reviewers for their valuable comments and feedback.

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Correspondence to Mirajul Islam .

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Islam, M., Ria, N.J., Ani, J.F., Masum, A.K.M., Abujar, S., Hossain, S.A. (2022). Deep Learning Based Classification System for Recognizing Local Spinach. In: Troiano, L., et al. Advances in Deep Learning, Artificial Intelligence and Robotics. Lecture Notes in Networks and Systems, vol 249. Springer, Cham.

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