Skip to main content
Springer Nature Link
Log in

Angiosperm Genus Classification by RBF-SVM

  • Conference paper
  • First Online:
Intelligent Data Engineering and Analytics (FICTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 371))

  • 294 Accesses

Abstract

Angiosperm genus classification performance has plateaued in the last few years. This paper proposed a novel method based on gray-level co-occurrence matrix and radial basis function kernel support vector machine for angiosperm genus classification. We collected a 300-image dataset, 100 are Hibiscus, 100 are Orchis, and the rest 100 are Prunus by digital camera. The results showed that our method achieved an accuracy of 84.73 ± 0.41%. In all, this method is promising in angiosperm genus classification.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
CHF34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
CHF 24.95
Price includes VAT (Switzerland)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
CHF 264.50
Price excludes VAT (Switzerland)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
CHF 330.50
Price excludes VAT (Switzerland)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
CHF 330.50
Price excludes VAT (Switzerland)
  • 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

Similar content being viewed by others

References

  1. Botirov, A., An, S., Arakawa, O., Zhang, S.: Application of a visible/near-infrared spectrometer in identifying flower and non-flower buds on ‘Fuji’ apple trees. Indian J. Agric. Res. 56(2), 214–219 (2022)

    Google Scholar 

  2. Teixeira-Costa, L., Heberling, J.M., Wilson, C.A., Davis, C.C.: Parasitic flowering plant collections embody the extended specimen. Methods Ecol. Evol. 14(2), 319–331 (2023)

    Article  Google Scholar 

  3. Veerendra, G., Swaroop, R., Dattu, D., Jyothi, C.A., Singh, M.K.: Detecting plant diseases, quantifying and classifying digital image processing techniques. Mater. Today Proc. 51, 837–841 (2022)

    Article  Google Scholar 

  4. Davidovic, L.M., Cumic, J., Dugalic, S., Vicentic, S., Sevarac, Z., et al.: Gray-level co-occurrence matrix analysis for the detection of discrete, ethanol-induced, structural changes in cell nuclei: an artificial intelligence approach. Microsc. Microanal. 28(1), 265–271 (2022)

    Article  Google Scholar 

  5. Saihood, A., Karshenas, H., Nilchi, A.R.N.: Deep fusion of gray level co-occurrence matrices for lung nodule classification. PLoS ONE 17(9), e0274516 (2022)

    Article  Google Scholar 

  6. Borman, R.I., Ahmad, I., Rahmanto, Y.: Klasifikasi Citra Tanaman Perdu Liar Berkhasiat Obat Menggunakan Jaringan Syaraf Tiruan radial basis function. Bull. Inform. Data Sci. 1(1), 6–13 (2022)

    Google Scholar 

  7. Su, H., Zhao, D., Yu, F., Heidari, A.A., Zhang, Y., et al.: Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images. Comput. Biol. Med. 142, 105181 (2022)

    Article  Google Scholar 

  8. Tanveer, M., Rajani, T., Rastogi, R., Shao, Y.-H., Ganaie, M.: Comprehensive review on twin support vector machines. Ann. Oper. Res. 1–46 (2022)

    Google Scholar 

  9. Sabanci, K., Aslan, M.F., Ropelewska, E., Unlersen, M.F.: A convolutional neural network-based comparative study for pepper seed classification: Analysis of selected deep features with support vector machine. J. Food Process Eng 45(6), e13955 (2022)

    Article  Google Scholar 

  10. Christaki, M., Vasilakos, C., Papadopoulou, E.-E., Tataris, G., Siarkos, I., et al.: Building change detection based on a gray-level co-occurrence matrix and artificial neural networks. Drones 6(12), 414 (2022)

    Article  Google Scholar 

  11. Pantic, I., Cumic, J., Dugalic, S., Petroianu, G.A., Corridon, P.R.: Gray level co-occurrence matrix and wavelet analyses reveal discrete changes in proximal tubule cell nuclei after mild acute kidney injury. Sci. Rep. 13(1), 4025 (2023)

    Article  Google Scholar 

  12. Wang, H., Li, S., Qiu, H., Lu, Z., Wei, Y., et al.: Development of a fast convergence gray-level co-occurrence matrix for sea surface wind direction extraction from marine radar images. Remote Sens. 15(8), 2078 (2023)

    Article  Google Scholar 

  13. Kisa, D.H., Ozdemir, M.A., Guren, O., Akan, A., IEEE.: Classification of hand gestures using sEMG signals and Hilbert-Huang transform. In: 30th European Signal Processing Conference (EUSIPCO). Belgrade, SERBIA (2022)

    Google Scholar 

  14. Zhang, Y.-D.: Secondary pulmonary tuberculosis recognition by 4-direction varying-distance GLCM and fuzzy SVM. Mob. Netw. Appl. (2022). https://doi.org/10.1007/s11036-021-01901-7

  15. Kaduhm, H.S., Abduljabbar, H.M.: Studying the classification of texture images by K-means of co-occurrence matrix and confusion matrix. Ibn AL-Haitham J. Pure Appl. Sci. 36(1), 113–122 (2023)

    Article  Google Scholar 

  16. Taye, M.M.: Theoretical understanding of convolutional neural network: concepts, architectures, applications, future directions. Computation 11(3), 52 (2023)

    Article  Google Scholar 

  17. Halder, S., Das, S., Basu, S.: Use of support vector machine and cellular automata methods to evaluate impact of irrigation project on LULC. Environ. Monit. Assess. 195(1), 3 (2023)

    Article  Google Scholar 

  18. Gordon, D., Norouzi, A., Blomeyer, G., Bedei, J., Aliramezani, M., et al.: Support vector machine based emissions modeling using particle swarm optimization for homogeneous charge compression ignition engine. Int. J. Engine Res. 24(2), 536–551 (2023)

    Article  Google Scholar 

  19. Alshikho, M., Jdid, M., Broumi, S.: A study of a support vector machine algorithm with an orthogonal Legendre kernel according to neutrosophic logic and inverse Lagrangian interpolation. J. Neutrosophic Fuzzy Syst. (JNFS) 5(01), 41–51 (2023)

    Article  Google Scholar 

  20. Tembhurne, J.V., Gajbhiye, S.M., Gannarpwar, V.R., Khandait, H.R., Goydani, P.R., et al.: Plant disease detection using deep learning based mobile application. Multimedia Tools Appl. 1–26 (2023)

    Google Scholar 

  21. Phillips, P.: Detection of Alzheimer’s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC. Biomed. Signal Process. Control 21, 58–73 (2015)

    Article  Google Scholar 

  22. Wang, S.: Detection of dendritic spines using wavelet packet entropy and fuzzy support vector machine. CNS & Neurol. Disorders Drug _targets 16(2), 116–121 (2017)

    Article  Google Scholar 

  23. Lu, H.M.: Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Access 4, 8375–8385 (2016)

    Article  Google Scholar 

  24. Gorriz, J.M., Ramírez, J.: Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning. Front. Comput. Neurosc. 10 (2016)

    Google Scholar 

  25. Tayari, E., Torkzadeh, L., Domiri Ganji, D., Nouri, K.: Investigation of hybrid nanofluid SWCNT–MWCNT with the collocation method based on radial basis functions. Euro. Phys. J. Plus 138(1), 3 (2023)

    Google Scholar 

  26. Rashidi, M., Alhuyi Nazari, M., Mahariq, I., Ali, N.: Modeling and sensitivity analysis of thermal conductivity of ethylene glycol-water based nanofluids with alumina nanoparticles. Experi. Techn. 47(1), 83–90 (2023)

    Google Scholar 

  27. Jalili, R., Neisy, A., Vahidi, A.: Multiquadratic-radial basis functions method for mortgage valuation under jump-diffusion model. Int. J. Fin. Manage. Account. 8(29), 211–219 (2023)

    Google Scholar 

  28. Noori, H.: Gradient-Controled Gaussian Kernel for image Inpainting. AUT J. Electr. Eng. 55(1), 2 (2023)

    Google Scholar 

  29. Gonzáleza, B., Negrına, E.: Operators with complex Gaussian kernels: asymptotic behaviours. Filomat 37(3), 833–838 (2023)

    Article  MathSciNet  Google Scholar 

  30. Zhang, Y.: Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. SIMULATION 92(9), 861–871 (2016)

    Article  Google Scholar 

  31. Wang, S.: Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine. PeerJ 4 (2016)

    Google Scholar 

  32. Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large margin DAGs for multiclass classification. In: 13th Annual Conference on Neural Information Processing Systems (NIPS). Co.

    Google Scholar 

  33. Wang, S.: Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection. Appl. Sci. 6(6) (2016)

    Google Scholar 

  34. Anupong, W., Jweeg, M.J., Alani, S., Al-Kharsan, I.H., Alviz-Meza, A., et al.: Comparison of wavelet artificial neural network, wavelet support vector machine, and adaptive neuro-fuzzy inference system methods in estimating total solar radiation in Iraq. Energies 16(2) (2023)

    Google Scholar 

  35. Zhang, Y.: Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine. Int. J. Imaging Syst. Technol. 25(4), 317–327 (2015)

    Article  Google Scholar 

  36. Shi, C.Y., Yin, X.X., Chen, R., Zhong, R.X., Sun, P., et al.: Prediction of end-point LF refining furnace based on wavelet transform based weighted optimized twin support vector machine algorithm. Metall. Res. Technol. 120(1) (2023)

    Google Scholar 

  37. Chen, J., Ye, H., Wang, J., Zhang, L.: Relationship between anthocyanin composition and floral color of Hibiscus syriacus. Horticulturae 9(1), 48 (2023)

    Article  Google Scholar 

  38. Kropf, M., Kriechbaum, M.: Monitoring of Dactylorhiza sambucina (L.) Soó (Orchidaceae)—Variation in flowering, flower colour morph frequencies, and erratic population census trends. Diversity 15(2), 179 (2023)

    Google Scholar 

  39. Wang, L., Song, J., Han, X., Yu, Y., Wu, Q., et al.: Functional divergence analysis of AGL6 genes in Prunus mume. Plants 12(1), 158 (2023)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing, 211200, China

    Shuwen Chen, Jiaji Wang, Yiyang Ni, Jiaqi Shao, Hui Qu & Ziyi Wang

  2. Jiangsu Province Engineering Research Center of Basic Education Big Data Application, Nanjing, 211200, China

    Shuwen Chen & Yiyang Ni

  3. State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, 210096, China

    Shuwen Chen

Authors
  1. Shuwen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiaji Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yiyang Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiaqi Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hui Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ziyi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuwen Chen .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Fiona Carroll

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    João Manuel R. S. Tavares

  4. Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Sandeep Singh Sengar

  5. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Chen, S., Wang, J., Ni, Y., Shao, J., Qu, H., Wang, Z. (2023). Angiosperm Genus Classification by RBF-SVM. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_12

Download citation

Publish with us

Policies and ethics

Search

Navigation

  NODES
innovation 2
INTERN 1
Project 1