Skip to main content
SpringerLink
Log in

The Analysis of Plants Image Classification Based on Machine Learning Approaches

  • Conference paper
  • First Online:
Emergent Converging Technologies and Biomedical Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 841))

Abstract

With the fast development in urbanization and populace, it has become a sincere errand to support and develop plants that are both significant in supporting the nature and the living creature’s needs. Moreover, there is a requirement for saving the plants having worldwide significance both financially and naturally. Finding such species from the backwoods or bushes having human contribution is a tedious and expensive undertaking to perform. Classification and identification of plants-leaf are useful for individuals to viably comprehend and ensure plants. The leaves of plants are the main acknowledgment organs. With the advancement of artificial intelligence and computer vision innovation, plant-leaf recognition dependent on plant-leaf image investigation is utilized to improve the information on plant classification and insurance. Deep learning is the condensing of deep neural network learning technique and has a place with neural organization structure. It can naturally take in highlights from huge information and utilize artificial neural network dependent on back propagation methods to prepare and order plant-leaf tests. There are numerous machine learning approaches for identification and classification of plant-leaf image. Some of the famous and effective approaches are Random Forest, Support Vector Machines, ResNet50, CNN, VGG16, VGG19, PNN, KNN, etc. In this paper, we are going to apply 9 machine learning techniques on Flavia plant-leaf image dataset. Flavia plant-leaf image dataset consists of 32 different species of plant. The images are first preprocessed and then their shape, color and texture-based features are extracted from the processed image. Initial these images are in the size of 256*256 pixels. These images were preprocessed and taken up to the size of 64*64 for fast processing. ResNet50 has given the best results with an accuracy of 98%. Though SVM and S-Inception have also provided a good accuracy.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cho S, Lee D, Jeong J (2002) Automation and emerging technologies: weedplant discrimination by machine vision and artificial neural network. Biosyst Eng 83(3):275280

    Google Scholar 

  2. Mallah C, Cope J, Orwell J (2013) Plant leaf classification using probabilistic integration of shape, texture and margin features. Signal Process Pattern Recogn Appl

    Google Scholar 

  3. Silva PFB, Marcal ARS, da Silva RMA (2013) Evaluation of features for leaf discrimination. Springer Lecture Notes in Computer Science, vol 7950, 197–204

    Google Scholar 

  4. Du JX, Wang XF, Zhang GJ (2007) Leaf shape based plant species recognition. Appl Math Comput 185(2):883–893

    MATH  Google Scholar 

  5. Jin T, Hou X, Li P et al (2015) A novel method of automatic plant species identification using sparse representation of leaf tooth features. PLoS One 10(10):e0139482

    Google Scholar 

  6. Wu SG, Bao FS, Xu EY et al (2007) A leaf recognition algorithm for plant classification using probabilistic neural network. In: IEEE International symposium signal processing and information technology. Cairo, Egypt, pp 11–16

    Google Scholar 

  7. Liu N, Kan JM (2016) Improved deep belief networks and multi-feature fusion for leaf identification. Neurocomputing 216:460–467

    Article  Google Scholar 

  8. Goodfellow I, Bengio Y, Courville A et al (2016) Deep learning, vol 1. MIT Press, Cambridge, pp 322–366

    MATH  Google Scholar 

  9. Simonyan K, Zisserman A (2014) Very deep convolutional networks for largescale image recognition. arXiv:1409.1556

  10. Szegedy C, Vanhoucke V, Ioffe S et al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference computer vision and pattern recognition, Las Vegas, USA, pp.2818–2826

    Google Scholar 

  11. Szegedy C, Ioffe S, Vanhoucke V et al (2017) Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Proceedings of the 31st AAAI Conference artificial intelligence, San Francisco, USA

    Google Scholar 

  12. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. 1610-02357

    Google Scholar 

  13. He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference computer vision and pattern recognition, Las Vegas, USA, pp 770–778

    Google Scholar 

  14. Huang G, Liu Z, Van Der Maaten L et al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference computer vision and pattern recognition, Honolulu, USA, pp 4700–4708

    Google Scholar 

  15. Howard AG, Zhu M, Chen B et al (2017) MobileNets: efficient convolutiona neural networks for mobile vision applications. arXiv:1704.04861

  16. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: NIPS'12 Proceedings of the 25th International conference on neural information processing systems, vol 1, Lake Tahoe, Nevada, pp 1097–1105

    Google Scholar 

  17. Wang Z, Sun X, Zhang Y et al (2016) Leaf recognition based on PCNN. Neural Comput Appl 27(4):899–908

    Article  Google Scholar 

  18. Tan JW, Chang SW, Kareem SBA et al (2018) Deep learning for plant species classification using leaf vein morphometric’, IEEE/ACM Trans Comput Biol Bioinf :1. https://ieeexplore.ieee.org/abstract/document/8388220

  19. 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 

  20. Sun Y, Liu Y, Wang G et al (2017) Deep learning for plant identification in natural environment. Comput Intell Neurosci 2017:6

    Google Scholar 

  21. Lee SH, Chan CS, Mayo SJ et al (2017) How deep learning extracts and learns leaf features for plant classification. Pattern Recognit 71:1–13

    Article  Google Scholar 

  22. Liu Z, Zhu L, Zhang XP et al (2015) Hybrid deep learning for plant leaves classification. In: International conference intelligent computing, Cham, August 2015, pp 115–123

    Google Scholar 

  23. Barré P, Stöver BC, Müller KF et al (2017) LeafNet: a computer vision system for automatic plant species identification. Ecol Inf 40:50–56

    Article  Google Scholar 

  24. Pawara P, Okafor E, Surinta O et al (2017) Comparing local descriptors and bags of visual words to deep convolutional neural networks for plant recognition. In: ICPRAM, Porto, Portugal, pp 479–486

    Google Scholar 

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

    Article  Google Scholar 

  26. Lalitha M, Kiruthiga M, Loganathan C (2013) A survey on image segmentation through clustering algorithm. Int J Sci Res 2(2):348–358

    Google Scholar 

  27. PatilBharti JK (2011) Advances in image processing for detection of plant diseases. J Adv Bioinforma Appl Res 2(2):135–141

    Google Scholar 

  28. Krig S (2014) Computer vision metrics: survey, taxonomy, and analysis. Apress:85–129

    Google Scholar 

  29. Forrest Sheng Bao EYX (2009) Flavia leaf database. http://flavia.sourceforge.net/

  30. Kumar N, Belhumeur P, Biswas A, Jacobs D, Kress W, Lopez I, Soares J (2012) Leafsnap: a computer vision system for automatic plant species identification, Computer Vision–ECCV. Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pp 502–516

    Google Scholar 

  31. Tan KC, Liu Y, Ambrose B, Tulig M, Belongie S (2019) The herbarium challenge 2019 dataset. Comput Vision Pattern Recogn

    Google Scholar 

  32. Mallah C, Cope J, Orwell J (2013) Plant leaf classification using probabilistic integration of shape, texture and margin features. Pattern Recognit Appl

    Google Scholar 

  33. Munisami T, Ramsurn M, Kishnah S, Pudaruth S (2015) Plant leaf recognition using shape features and color histogram with k-nearest neighbor classifiers. Proc Comput Sci (Elsevier) J 58:740–747

    Google Scholar 

  34. Imah EM, Rahayu YS, Wintarti A (2018) Plant leaf recognition using competitive based learning algorithm, In Proceedings of the 2nd annual applied science and engineering conference (AASEC 2017), IOP Confernce. Series: Materials Science and Engineering, 288

    Google Scholar 

  35. Salve P, Sardesai M, Manza R, Yannawar P (2016) Identification of the plants based on leaf shape descriptors. In: Satapathy S, Raju K, Mandal J, Bhateja V (Eds) Proceedings of the second international conference on computer and communication technologies. Advances in Intelligent Systems and Computing, vol 379. Springer, New Delhi

    Google Scholar 

  36. Kadir A, Nugroho L, Susanto A, Santosa PI (2014) Leaf classification using shape, color, and texture features. abs/1401.4447

    Google Scholar 

  37. Puja D, Saraswat M, Arya K (2013) Automatic agricultural leaves recognition system. In: Bansal J, Singh P, Deep K, Pant M, Nagar A (Eds) Proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 201. Springer, India

    Google Scholar 

  38. Aakif A, Khan MF (2015) Automatic classification of plants based on their leaves. Biosyst Eng

    Google Scholar 

  39. Lavania S, Matey PS (2014) Leaf recognition using contour based edge detection and SIFT algorithm. In: IEEE international conference on computational intelligence and computing research, IEEE ICCIC 2014

    Google Scholar 

  40. Fern BM, Sulong GB, Rahim MSM (2014) Leaf recognition based on leaf tip and leaf base using centroid contour gradient. Adv Sci Lett

    Google Scholar 

  41. Singh K, Gupta I, Gupta S (2010) SVM-BDT PNN and fourier moment technique for classification of leaf shape. Int J Signal Process Image Process Pattern Recogn 3:67–78

    Google Scholar 

  42. Gulhane VA, Kolekar MH (2014) Diagnosis of diseases on cotton leaves using principal component analysis classifier. In: Annual IEEE India conference (INDICON). Pune, India, pp 1–5

    Google Scholar 

  43. Pujari JD, Yakkundimath R, Byadgi AS (2014) Identification and classification of fungal disease affected on agriculture/horticulture crops using image processing techniques. In: 2014 IEEE international conference on computational intelligence and computing research, Coimbatore, India, pp 1–4

    Google Scholar 

  44. Hsiao J, Kang L, Chang C, Lin C (2014) Comparative study of leaf image recognition with a novel learning-based approach. In: Science and information conference. London, UK, pp 389–393

    Google Scholar 

  45. Conversion from RGB to HSV color space at https://math.stackexchange.com/questions/556341/rgb-to-hsv-colorconversion-algorithm

Download references

Author information

Authors and Affiliations

  1. Lovely Professional University, Phagwara, India

    Sukanta Ghosh & Amar Singh

Authors
  1. Sukanta Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra, Haryana, India

    N. Marriwala

  2. Dean Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India

    C. C. Tripathi

  3. Department of Electronic and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Shruti Jain

  4. Co-Founder of Dew Mobility, Santa Clara University, Santa Clara, CA, USA

    Shivakumar Mathapathi

Rights and permissions

Reprints and permissions

Copyright information

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

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ghosh, S., Singh, A. (2022). The Analysis of Plants Image Classification Based on Machine Learning Approaches. In: Marriwala, N., Tripathi, C.C., Jain, S., Mathapathi, S. (eds) Emergent Converging Technologies and Biomedical Systems . Lecture Notes in Electrical Engineering, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-16-8774-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-8774-7_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8773-0

  • Online ISBN: 978-981-16-8774-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation