Skip to main content

Progressive Transfer Learning Approach for Identifying the Leaf Type by Optimizing Network Parameters

Neural Processing Letters volume 53, pages 3653–3676 (2021)Cite this article

Abstract

There are many plant species with medicinal properties and hence, it becomes very crucial to recognize its relevance. With a range of plant species available for medicinal use, it becomes vital to classify them accurately for their efficient use in medicine. The precise and unerring classification of the plant species is beyond the reach of a common person since it requires thorough knowledge of the subject and manual recognition is tedious and inaccurate due to human error. The advocate solution engages the ReNet50 architecture for automated classification of medicinal leaf prior to the fine-tuning. The initial layers of the pre-trained ResNet50 model are freezed during the first phase of the training while the newly added layers are trained using a differential learning rate obtained by the one-cycle policy. The fine-tuned model from phase I is loaded and trained by unfreezing in the second phase. This process is repeated in such a way that the size of the image is made to increase progressively from 80, 128, 150 to 180 pixels in these two stages of learning. The proposed architecture is validated on four-leaf datasets, which also includes a self-created dataset of leaf images collected from the internet. The publicly available benchmark datasets of leaf images considered for carrying out experiments are Flavia leaf dataset, LeafSnap dataset (lab and field), and MalayaKew (MK) Leaf Dataset (D1 and D2). Several preprocessing techniques; such as identifying mislabeled and irrelevant images from the dataset, data preprocessing, and data balancing are applied before fine-tuning of the model in case of self-collected dataset from the internet. The top-1 accuracy of the Flavia dataset is 100%, whereas MK-D1 and MK-D2 datasets achieved top-1 accuracy of 99.05%, and 99.89% respectively. The Accuracy of the LeafSnap dataset is 97.95% and 99.43% for the field and lab images respectively.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price includes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

References

  1. Pandey MM, Rastogi S, Rawat AKS (2013) Indian traditional ayurvedic system of medicine and nutritional supplementation. Evid Based Complementary Altern Med 2013:376327. https://doi.org/10.1155/2013/376327

  2. Dileep MR, Pournami PN (2019) Ayurleaf: a deep learning approach for classification of medicinal plants

  3. Smith LN (2018) A disciplined approach to neural network hyper-parameters Part 1 – learning rate, batch size, momentum, and weight decay. March, pp 464–72, http://arxiv.org/abs/1803.09820 [cs, stat]

  4. Hu J, Chen Z, Yang M, Zhang R, Cui Y (2018) A multiscale fusion convolutional neural network for plant leaf recognition. IEEE Signal Process Lett 25(6):853–857

    Article  Google Scholar 

  5. Wu SG, Bao FS, Xu EY, Wang YX, Chang YF, Xiang QL (2007) A Leaf Recognition Algorithm for Plant Classification using Probabilistic Neural Network. In: 7th IEEE international symposium on signal processing and information technology, Giza, Egypt, pp 11–16

  6. Hossain J, Amin MA (2010) Leaf Shape Identification Based Plant Biometrics. In: 13th international conference on computer and information technology, Dhaka, Bangladesh, pp 458–463

  7. Zeng W, Li M (2020) Crop leaf disease recognition based on Self-Attention convolutional neural network. Comput Electron Agric 172:105341

    Article  Google Scholar 

  8. Le TL, Tran DT, Hoang VN (2014) Fully Automatic leaf-based plant identification, application for Vietnamese medicinal plant search. In: Fifth symposium on information and communication technology, Hanoi, Vietnam, pp 146–154

  9. Du JX, Zhai CM, Wang QP (2013) Recognition of plant leaf image based on fractal dimension features. Neurocomputing 116:150–156

    Article  Google Scholar 

  10. Lee SH, Chan CS, Wilkin P, Remagnino P (2015) Deep-plant: Plant identification with convolutional neural networks. In: Image Processing (ICIP), 2015 IEEE International Conference on 2015 Sep 27, pp 452–456

  11. Lee SH, Chan CS, Mayo SJ, Remagnino P (2017) How deep learning extracts and learns leaf features for plant classification. Pattern Recogn 71:1–13

    Article  Google Scholar 

  12. Aakif A, Khan MF (2015) Automatic classification of plants based on their leaves. Biosyst Eng 139:66–75

    Article  Google Scholar 

  13. Du JX, Wang XF, Zhang GJ (2007) Leaf shape based plant species recognition. Appl Math Comput 185:883–893

    MATH  Google Scholar 

  14. Herdiyeni Y, Wahyuni NKS (2012) Mobile Application for Indonesian Medicinal Plants Identification using Fuzzy Local Binary Pattern and Fuzzy Color Histogram. In: international conference on advanced computer science and information systems (ICACSIS), West Java, Indonesia, pp 301–306

  15. Hernandez-Serna A, Jiménez-Segura LF (2014) Automatic Identification of species with neural networks. PeerJ 2:e563. https://doi.org/10.7717/peerj.563

    Article  Google Scholar 

  16. Siravenha ACQ, Carvalho SR (2015) Exploring the use of Leaf Shape Frequencies for Plant Classification. In: 28th SIBGRAPI conference on graphics, patterns and images, Salvador, Brazil, pp 297–304

  17. Chaki J, Parekh R, Bhattacharya S (2015) Plant leaf recognition using texture and shape features with neural classifiers. Pattern Recogn Lett 58:61–68

    Article  Google Scholar 

  18. Tan JW, Chang S, Abdul Kareem SB, Yap HJ, Yong K (2018) Deep learning for plant species classification using leaf vein morphometric. IEEE/ACM Trans Comput Biol Bioinform 17(1):82–90

  19. Janani R, Gopal A (2013) Identification of selected medicinal plant leaves using image features and ANN. In: 2013 international conference on advanced electronic systems (ICAES), September, pp 238–242

  20. Venkataraman D, Mangayarkarasi N (2016) Computer vision-based feature extraction of leaves for identification of medicinal values of plants. In: 2016 IEEE international conference on computational intelligence and computing research (ICCIC), December, pp 1–5

  21. Grinblat GL, Uzal LC, Larese MG, Granitto PM (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric 127:418–424

    Article  Google Scholar 

  22. Ghazi MM, Yanikoglu B, Aptoula E (2017) Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235:228–235

    Article  Google Scholar 

  23. Sabzi S, Pourdarbani R, Arribas JI (2020) A computer vision system for the automatic classification of five varieties of tree leaf images. Computers 9(1):6

    Article  Google Scholar 

  24. Kalyoncu C, Toygar Ö (2015) Geometric leaf classification. Comput Vis Image Underst 133:102–109

    Article  Google Scholar 

  25. Arribas JI, Sánchez-Ferrero GV, Ruiz-Ruiz G, Gómez-Gil J (2011) Leaf classification in sunflower crops by computer vision and neural networks. Comput Electron Agric 78(1):9–18

    Article  Google Scholar 

  26. Batvia V., Patel D., Dr. Vasant AR (2017) A Survey on Ayurvedic Medicine Classification using Tensor flow. International Journal of Computer Trends and Technology (IJCTT), Published by Seventh Sense Research Group, 2017, November, ISSN: 2231–2803, www.ijcttjoumal.org, V53(2): 68–70,

  27. Kumar PM, Surya CM, Gopi VP (2017) Identification of ayurvedic medicinal plants by image processing of leaf samples. In: Third international conference on research in computational intelligence and communication networks (ICRCICN), November, pp 231–238

  28. Priya CA, Balasaravanan T, Thanamani AS (2012) An efficient leaf recognition algorithm for plant classification using support vector machine. In: Proceedings of international conference on pattern recognition, informatics and medical engineering (PRIME-2012), pp 428–432

  29. Amlekar MM, Gaikwad AT (2018) Plant classification using image processing and neural network. Data Manag Anal Innov 839:375–384

    Article  Google Scholar 

  30. Saleem G, Akhtar M, Ahmed N, Qureshi WS (2019) Automated analysis of visual leaf shape features for plant classification. Comput Electron Agric 157:270–280

    Article  Google Scholar 

  31. Shah MP, Singha S, Awate SP (2017) Leaf classification using marginalized shape context and shape+texture dual-path deep convolutional neural network. In: 2017 IEEE international conference on image processing (ICIP), September, pp 860–864

  32. Bodhwani V, Acharjya DP, Bodhwani U (2019) Deep residual networks for plant identification. Procedia Comput Sci 152:186–194

    Article  Google Scholar 

  33. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision, September, pp 818–833

  34. Agarap AF (2018) Deep learning using rectified linear units (ReLU). March, arXiv preprint http://arxiv.org/abs/1803.08375

  35. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  36. Buslaev A, Iglovikov VI, Khvedchenya E, Parinov A, Druzhinin M, Kalinin AA (2020) Albumentations: fast and flexible image augmentations. Information 11(2):125

    Article  Google Scholar 

  37. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. February, arXiv preprint http://arxiv.org/abs/1502.03167

  38. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  39. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    MathSciNet  Article  Google Scholar 

  40. Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  41. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034

  42. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, March, pp 249–256

  43. Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. January, arXiv preprint http://arxiv.org/abs/1801.06146

  44. Smith LN (2017) Cyclical learning rates for training neural networks. In: IEEE winter conference on applications of computer vision (WACV), March, pp 464–472

  45. Loshchilov I, Hutter F Sgdr (2016) Stochastic gradient descent with warm restarts, August, arXiv preprint http://arxiv.org/abs/1608.03983

  46. Loshchilov I, Hutter F (2017) Fixing weight decay regularization in adam. 2017, November, http://arxiv.org/abs/1711.05101 [cs, math]

  47. Joshi D, Singh TP (2020) A survey of fracture detection techniques in bone X-ray images. Artif Intell Rev 53(6):4475–4517

    Article  Google Scholar 

  48. Eid HF, Abraham A (2017) Plant species identification using leaf biometrics and swarm optimization: a hybrid PSO, GWO, SVM model. Int J Hybrid Intell Syst 14(3):155–165

    Google Scholar 

  49. Wang B, Brown D, Gao Y, La Salle J (2015) Multiscale-arch-height description for mobile retrieval of leaf images. Inf Sci 302:132–148

    Article  Google Scholar 

  50. Pawara P, Okafor E, Surinta O, Schomaker L, Wiering M (2017) comparing local descriptors and bags of visual words to deep convolutional neural networks for plant recognition. ICPRAM 479:486

    Google Scholar 

  51. Barré P, Stöver BC, Müller KF, Steinhage V (2017) LeafNet: a computer vision system for automatic plant species identification. Ecol Inform 40:50–56

    Article  Google Scholar 

  52. Kumar N, Belhumeur PN, Biswas A, Jacobs DW, Kress WJ, Lopez IC, Soares JV (2012) Leafsnap: A computer vision system for automatic plant species identification. In: European conference on computer vision, Springer, Berlin, Heidelberg, 2012, October, pp 502–516.

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies (UPES), Energy Acres, Bidholi, Dehradun, 248007, Uttarakhand, India

    Deepa Joshi, Vidyanand Mishra & Honey Srivastav

  2. School of Computer Science, The University of Adelaide, Adelaide, Australia

    Diksha Goel

Authors
  1. Deepa Joshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vidyanand Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Honey Srivastav
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Diksha Goel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deepa Joshi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Joshi, D., Mishra, V., Srivastav, H. et al. Progressive Transfer Learning Approach for Identifying the Leaf Type by Optimizing Network Parameters. Neural Process Lett 53, 3653–3676 (2021). https://doi.org/10.1007/s11063-021-10521-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-021-10521-x