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Flower Detection Using Advanced Deep Learning Techniques

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Innovations in Electronics and Communication Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 355))

Abstract

In nature, we have found different types of flower plants. It is difficult to identify and recognize which flower species it is. Since the recent growth of deep learning in computer vision, identification of objects is extended through various fields. In this paper we aim to detect the flowers on Oxford17 flower dataset. Due to the wide variety of flower species with varying colors, shapes, and sizes, as well as their surroundings with leaves, shrubs, and other objects, flower recognition is the most difficult task in the subject of object detection. We present a formal contract Yolo object detection model in this research for rapid and accurate detections. The proposed model is a novel single-step object detection method for differentiating flowers from a wide variety of species. This system performs both localization and object recognition in the image automatically. The flower region is automatically split to enable for the creation of the smallest bounding box feasible around it, and the items in the image are then marked. We use advanced measures throughout the training stage to improve classification stability, precision, and speed. We evaluated our method on Oxford17 dataset and Google images dataset. The experimental study results have shown better results and exceed 98% on the dataset which is effective than the others.

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References

  1. K.B. Prakash, M.D. Rangaswamy, A.R. Raman, ANN for multi-lingual regional web communication, in International Conference on Neural Information Processing (Springer, Berlin, Heidelberg, 2012), pp. 473–478

    Google Scholar 

  2. K.B. Prakash, M.D. Rangaswamy, A.R. Raman, Statistical interpretation for mining hybrid regional web documents, in International Conference on Information Processing (Springer, Berlin, Heidelberg, 2012), pp. 503–512

    Google Scholar 

  3. K.B. Prakash, Mining issues in traditional Indian web documents. Indian J. Sci. Technol. 8(32), 1–11 (2015)

    Article  Google Scholar 

  4. K.B. Prakash, Content extraction studies using total distance algorithm, in 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) (IEEE, 2016), pp. 673–679

    Google Scholar 

  5. K.B. Prakash, M.D. Rangaswamy, T.V. Ananthan, V.N. Rajavarman, Information extraction in unstructured multilingual web documents. Indian J. Sci. Technol. 8(16), 1–8 (2015)

    Article  Google Scholar 

  6. J. Ärje, D. Milioris, D. Tran, A. Iosifidis, J. Raitoharju, Automatic flower detection and classification system using a light-weight convolutional neural network, in EUSIPCO Workshop on“Signal Processing, Computer Vision and Deep Learning for Autonomous Systems (2019)

    Google Scholar 

  7. H. Almogdady, S. Manaseer, H. Hiary, A flower recognition system based on image processing and neural networks. Int. J. Sci. Technol. Res. 7(11), 166–173 (2018)

    Google Scholar 

  8. K.B. Prakash, A. Rajaraman, Mining of bilingual Indian Web documents. Proc. Comput. Sci. 89, 514–520 (2016)

    Article  Google Scholar 

  9. M. Ismail, K.B. Prakash, M.N. Rao, Collaborative filtering-based recommendation of online social voting. Int. J. Eng. Technol. (UAE) 7(3), 1504–1507 (2018)

    Article  Google Scholar 

  10. W. Pardee, P. Yusungnern, P. Sripian, Flower identification system by image processing, in 3rd International Conference on Creative Technology CRETECH, vol. 1, pp. 1–4 (2015).

    Google Scholar 

  11. W. Liu, Y. Rao, B. Fan, J. Song, Q. Wang, Flower classification using fusion descriptor and SVM, in 2017 International Smart Cities Conference (ISC2) (IEEE, 2017), pp. 1–4

    Google Scholar 

  12. Google. Google Image Search. Retrieved from https://images.google.com

  13. D. Babitha, M. Ismail, S. Chowdhury, R. Govindaraj, K.B. Prakash, Automated road safety surveillance system using hybrid cnn-lstm approach. Int. J. Adv. Trends Comput. Sci. Eng. 9(2), 1767–1773 (2020)

    Article  Google Scholar 

  14. J. Wu, Complexity and accuracy analysis of common artificial neural networks on pedestrian detection. MATEC Web Conf. 232, 01003 (2018)

    Google Scholar 

  15. Y. Liu, F. Tang, D. Zhou, Y. Meng, W. Dong, Flower classification via convolutional neural network, in 2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA) (IEEE, 2016), pp. 110–116

    Google Scholar 

  16. D. Babitha, T. Jayasankar, V.P. Sriram, S. Sudhakar, K.B. Prakash, Speech emotion recognition using state-of-art learning algorithms. Int. J. Adv. Trends Comput. Sci. Eng. 9(2), 1340–1345 (2020)

    Article  Google Scholar 

  17. J. Redmon, A. Farhadi, YOLO9000: better, faster, stronger, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 7263–7271

    Google Scholar 

  18. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: unified, real- time object detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779–788

    Google Scholar 

  19. J. Redmon, A. Farhadi, Yolov3: an incremental improvement (2018). arXiv:1804.02767

  20. Y.S.S. Bharadwaj, P. Rajaram, V.P. Sriram, S. Sudhakar, K. Prakash, B: Effective handwritten digit recognition using deep convolution neural network. Int. J. Adv. Trends Comput. Sci. Eng. 9(2), 1335–1339 (2020)

    Article  Google Scholar 

  21. K.B. Prakash, M.D. Rangaswamy, T.V. Ananthan, Feature extraction studies in a heterogeneous web world. Int. J. Appl. Eng. Res. 9(22), 16571–16579 (2014)

    Google Scholar 

  22. H. Hiary, H. Saadeh, M. Saadeh, M. Yaqub, Flower classification using deep convolutional neural networks. IET Comput. Vis. 12(6), 855–862 (2018)

    Google Scholar 

  23. N. FatihahSahidan, A.K. Juha, N. Mohammad, Z. Ibrahim, Flower and leaf recognition for plant identification using convolutional neural network. Indonesian J. Electr. Eng. Comput. Sci. 16(2), 737–743 (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

    Kolla Bhanu Prakash

  2. Department of Computer Science and Engineering, BV Raju Institute of Technology, Narsapur, Medak, Telangana, India

    Ch. Sreedevi, Pallavi Lanke & Pradeep Kumar Vadla

  3. Guru Nanak Institutions Technical Campus (Autonomous), Khanapur, Telangana, India

    S. V. Ranganayakulu

  4. School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India

    Suman Lata Tripathi

Authors
  1. Kolla Bhanu Prakash
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  2. Ch. Sreedevi
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  3. Pallavi Lanke
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  4. Pradeep Kumar Vadla
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  5. S. V. Ranganayakulu
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  6. Suman Lata Tripathi
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Corresponding author

Correspondence to Kolla Bhanu Prakash .

Editor information

Editors and Affiliations

  1. Managing Director, Guru Nanak Institutions, Ibrahimpatnam, Telangana, India

    H. S. Saini

  2. Professor and Associate Director, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana, India

    R. K. Singh

  3. Department of Electronics and Communication Engineering, Jamia Millia Islamia, New Delhi, Delhi, India

    Mirza Tariq Beg

  4. Department of Electrical Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India

    Ravibabu Mulaveesala

  5. Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana, India

    Md Rashid Mahmood

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Prakash, K.B., Sreedevi, C., Lanke, P., Vadla, P.K., Ranganayakulu, S.V., Tripathi, S.L. (2022). Flower Detection Using Advanced Deep Learning Techniques. In: Saini, H.S., Singh, R.K., Tariq Beg, M., Mulaveesala, R., Mahmood, M.R. (eds) Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol 355. Springer, Singapore. https://doi.org/10.1007/978-981-16-8512-5_23

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  • DOI: https://doi.org/10.1007/978-981-16-8512-5_23

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8511-8

  • Online ISBN: 978-981-16-8512-5

  • eBook Packages: EngineeringEngineering (R0)

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