Skip to main content

Advertisement

SpringerLink
Log in

Plant classification in the wild: Energy evaluation for deep learning models

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Having a system that can take an image of a natural scene and accurately classify the plants in it is of undeniable importance. However, the complexities of dealing with natural scene images and the vast diversity of plants in the wild make designing such a classifier a challenging task. Deep Learning (DL) lends itself as viable solution to tackle such complex problem. However, advanced in DL architectures and software (including DL frameworks) come with a high cost in terms of energy consumption especially when employing Graphics Processing Units (GPU). As data expands rapidly, the need to create energy-aware models increases in order to reduce energy consumption and move towards “Greener AI”. Since the problem of designing energy-aware architectures for plant classification has not been studied significantly in the literature, our work comes to start bridging this gap by focusing not only on the models’ performance, but also on their energy usage on both CPU and GPU platforms. We consider different state-of-the-art Convolutional Neural Networks (CNN) architectures and train them on two famous challenging plants datasets: iNaturalist and Herbarium. Our experiments are meant to highlight the trade-off between accuracy and energy consumption. For examples, the results show that while GPU-bound models can be about 40% faster in terms of training time than simple models running on CPU, the latter’s energy consumption is only two thirds of the former. We hope that such findings will encourage the community to reduce its reliance on accuracy measures to compare different architectures and start taking other factors into account such as power consumption, simplicity, etc.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Notes

  1. https://sites.google.com/view/fgvc6/competitions/inaturalist-2019

  2. https://sites.google.com/view/fgvc7/competitions/herbarium2020

  3. https://github.com/RaffiLouyAlQurran/Plant-Classification

  4. https://github.com/sosy-lab/cpu-energy-meter

  5. https://developer.nvidia.com/nvidia-management-library-nvml

References

  1. Al-Qurran R, Al-Ayyoub M, Shatnawi A (2018) Plant classification in the wild: a transfer learning approach. In: 2018 International arab conference on information technology (ACIT), IEEE, pp 1–5

  2. Angelova A, Zhu S (2013) Efficient object detection and segmentation for fine-grained recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 811– 818

  3. Carneiro T, Da Nóbrega RVM, Nepomuceno T, Bian GB, De Albuquerque VHC, Reboucas Filho PP (2018) Performance analysis of google colaboratory as a tool for accelerating deep learning applications. IEEE Access 6:61677–61685

    Article  Google Scholar 

  4. Chai Y, Lempitsky V, Zisserman A (2011) Bicos: a bi-level co-segmentation method for image classification. In: 2011 International conference on computer vision, IEEE, pp 2579–2586

  5. Chen B, Medini T, Farwell J, Gobriel S, Tai C, Shrivastava A (2019) Slide: In defense of smart algorithms over hardware acceleration for large-scale deep learning systems. arXiv preprint arXiv:1903.03129

  6. El Massi I, Es-Saady Y, El Yassa M, Mammass D, Benazoun A (2016) Automatic recognition of the damages and symptoms on plant leaves using parallel combination of two classifiers. In: 2016 13Th international conference on computer graphics, imaging and visualization (CGiv), IEEE, pp 131– 136

  7. El Massi I, Es-saady Y, El Yassa M, Mammass D, Benazoun A (2016) A hybrid combination of multiple svm classifiers for automatic recognition of the damages and symptoms on plant leaves. In: International conference on image and signal processing, Springer, pp 40–50

  8. Es-saady Y, El Massi I, El Yassa M, Mammass D, Benazoun A (2016) Automatic recognition of plant leaves diseases based on serial combination of two svm classifiers. In: 2016 International conference on electrical and information technologies (ICEIT), IEEE, pp 561–566

  9. Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol 1. MIT Press, Cambridge

    MATH  Google Scholar 

  10. 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 

  11. 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

  12. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  13. Kanan C, Cottrell G (2010) Robust classification of objects, faces, and flowers using natural image statistics. In: 2010 IEEE Computer society conference on computer vision and pattern recognition, IEEE, pp 2472–2479

  14. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  15. Larese MG, Baya AE, Craviotto RM, Arango MR, Gallo C, Granitto PM (2014) Multiscale recognition of legume varieties based on leaf venation images. Expert Syst Appl 41(10):4638–4647

    Article  Google Scholar 

  16. Lee SH, Chan CS, Wilkin P, Remagnino P (2015) Deep-plant: Plant identification with convolutional neural networks. In: 2015 IEEE International conference on image processing (ICIP), IEEE, pp 452–456

  17. Liu Y, Tang F, Zhou D, Meng Y, Dong W (2016) Flower classification via convolutional neural network. In: 2016 IEEE International conference on functional-structural plant growth modeling, simulation, visualization and applications (FSPMA), IEEE, pp 110–116

  18. Nilsback ME, Zisserman A (2008) Automated flower classification over a large number of classes. In: 2008 Sixth indian conference on computer vision, graphics & image processing, IEEE, pp 722–729

  19. Nilsback ME, Zisserman A (2009) An automatic visual flora-segmentation and classification of flower images. Ph.D. thesis Oxford University Oxford

  20. Ouhami M, Es-Saady Y, El Hajji M, Hafiane A, Canals R, El Yassa M (2020) Deep transfer learning models for tomato disease detection. In: International conference on image and signal processing, Springer, pp 65–73

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

    Article  Google Scholar 

  22. Qingfeng W, Kunhui L, Changle Z, et al. (2007) Feature extraction and automatic recognition of plant leaf using artificial neural network {J}. Advances in Artificial Intelligence

  23. Russell S, Norvig P (2020) Artificial intelligence: A Modern Approach, 4 edn Pearson

  24. Schwartz R, Dodge J, Smith N, et al. (2019) Green ai (2019). arXiv preprint arXiv:1907.10597

  25. Sharif Razavian A, Azizpour H, Sullivan J, Carlsson S (2014) Cnn features off-the-shelf: An astounding baseline for recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 806–813

  26. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations

  27. Sünderhauf N., McCool C, Upcroft B, Perez T (2014) Fine-grained plant classification using convolutional neural networks for feature extraction. In: CLEF (Working notes), pp 756–762

  28. Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 31

  29. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  30. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  31. Toma A, Stefan LD, Ionescu B (2017) Upb hes so@ plantclef 2017: Automatic plant image identification using transfer learning via convolutional neural networks. In: CLEF (Working notes)

  32. Wittmann FM (2019) Timeline of transfer learning models. https://medium.com/analytics-vidhya/timeline-of-transfer-learning-models-db2a0be39b37. Accessed 18 Mar 2021

  33. Yalcin H, Razavi S (2016) Plant classification using convolutional neural networks

  34. Zhang C, Zhou P, Li C, Liu L (2015) A convolutional neural network for leaves recognition using data augmentation. In: 2015 IEEE International conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing, IEEE, pp 2143–2150

Download references

Acknowledgements

We gratefully acknowledge the support of the Deanship of Research at the Jordan University of Science and Technology for supporting this work via Grant #20180544.

Author information

Authors and Affiliations

  1. Jordan University of Science and Technology, Irbid, Jordan

    Raffi Al-Qurran, Mahmoud Al-Ayyoub & Ali Shatnawi

Authors
  1. Raffi Al-Qurran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahmoud Al-Ayyoub
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Shatnawi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahmoud Al-Ayyoub.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al-Qurran, R., Al-Ayyoub, M. & Shatnawi, A. Plant classification in the wild: Energy evaluation for deep learning models. Multimed Tools Appl 81, 30143–30167 (2022). https://doi.org/10.1007/s11042-022-12695-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12695-5

Keywords

Search

Navigation