Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Local triangular-ternary pattern: a novel feature descriptor for plant leaf disease detection

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

Abstract

Most of the plant diseases severely affect the plant leaves and thus can be identified from infected leaf images using modern computer vision techniques. During the last decade, various machine learning methods have been developed to detect and analyze plant leaf disease accurately. Hence, developing an effective machine learning prediction method for plant disease identification at an early stage arises enormous interest in computer vision research. Diagnosing plant leaf diseases is a challenging area of research, mainly because leaf images exhibit complexity arising from irregular shapes, uneven colors, a mixture of normal and abnormal regions, and are often accompanied by complex backgrounds. In this paper, a new feature descriptor titled as the local triangular-ternary pattern (LTriTP) for plant leaf disease detection is proposed. The proposed method extracts feature vectors using the triangular shape descriptor to enable efficient extraction and representation of features from the leaf images. To apply the ternary pattern, an absolute mean value based dynamic threshold is computed, which supports the sensitive analysis of plant leaf image texture information by producing highly relevant and diverse values. Since plant disease can appear at any orientation on a leaf image, therefore in each triangle, a histogram of the gradient is also computed in four directions (00, 450, 900, and 1350) to find the gradient change of infected regions in contrast to healthy areas. This is what we termed as the triangular histogram of gradient (T-HOG), which makes the proposed method orientation invariant. Fusion of T-HOG and LTriTP features has shown better disease detection performance. Multimodal classification is performed using 6 disease classes of tomato leaf images. The performance of the proposed method is compared with renowned methods like Local Binary Pattern, and Local Ternary Pattern using the publicly available PlantVillage dataset of tomato images. The classification accuracy varies from 94.50% to 97.80% with respect to different classifiers, where the error rate varies from 2.03% to 5.03%.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Data availability

Data sharing is not applicable to this article as no datasets were generated, dataset used in this research has been cited and can be downloaded from the respective source.

References

  1. Agarwal M et al (2020) ToLeD: Tomato leaf disease detection using convolution neural network. Proced Comput Scie 167:293–301. https://doi.org/10.1016/j.procs.2020.03.225

    Article  Google Scholar 

  2. Ahmad A, El Gamal A, Saraswat DJIA (2023) Toward generalization of deep learning-based plant disease identification under controlled and field conditions. IEEE Access 11:9042–9057. https://doi.org/10.1109/ACCESS.2023.3240100

    Article  Google Scholar 

  3. Ahmad W, Shah S, Irtaza A (2020) Plants disease phenotyping using quinary patterns as texture descriptor. KSII Trans Intern Inf Syst (TIIS) 14(8):3312–3327. https://doi.org/10.3837/tiis.2020.08.009

    Article  Google Scholar 

  4. Ahmed N, Asif HMS, Saleem G (2021) Leaf Image-based plant disease identification using color and texture features. arXiv preprint arXiv:2102.04515. 121(2):1139–1168. https://doi.org/10.1007/s11277-021-09054-2

  5. Aksoy A, kaymak HC (2021) Competition power of turkey’s tomato export and comparison with balkan countries. Bulg J Agric. 27(2):253–258

    Google Scholar 

  6. Al-gaashani MS et al (2022) Tomato leaf disease classification by exploiting transfer learning and feature concatenation. IET Image Proc 16(3):913–925. https://doi.org/10.1049/ipr2.12397

    Article  Google Scholar 

  7. Ali H et al (2017) Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Comput Electron Agric 138:92–104. https://doi.org/10.1016/j.compag.2017.04.008

    Article  Google Scholar 

  8. Amirkhani D, Bastanfard A (2021) An objective method to evaluate exemplar-based inpainted images quality using Jaccard index. Multimed Tools Appl 80(17):26199–26212. https://doi.org/10.1007/s11042-021-10883-3

    Article  Google Scholar 

  9. Armi L, Fekri-Ershad S (2019) Texture image classification based on improved local quinary patterns. Multimed Tools Appl 78(14):18995–19018. https://doi.org/10.1007/s11042-019-7207-2

    Article  Google Scholar 

  10. Bastanfard A et al (2022) Toward image super-resolution based on local regression and nonlocal means. Multimed Tools Appl 81(16):23473–23492. https://doi.org/10.1007/s11042-022-12584-x

    Article  Google Scholar 

  11. Bensaadi S, Louchene A (2023) Low-cost convolutional neural network for tomato plant diseases classifiation. 12(1):162

  12. Bhushanamu M, Rao MP, Samatha K (2020) Plant curl disease detection and classification using active contour and fourier descriptor. Eur J Mol Clin Med 7(5):1088–1105

    Google Scholar 

  13. Cristin R et al (2020) Deep neural network based Rider-Cuckoo Search Algorithm for plant disease detection. Artif Intell Rev 53(7):4993–5018. https://doi.org/10.1007/s10462-020-09813-w

    Article  Google Scholar 

  14. Department of Economic and Social Affairs 2022 U.N. World population prospects 2022. [cited 2023 Monday, May 1 ]; Available from: https://population.un.org/wpp/

  15. Fadaei S, Amirfattahi R, Ahmadzadeh MR (2017) Local derivative radial patterns: A new texture descriptor for content-based image retrieval. Signal Process 137:274–286. https://doi.org/10.1016/j.sigpro.2017.02.013

    Article  Google Scholar 

  16. Gadade H, Kirange D (2021) Machine learning based identification of tomato leaf diseases at various stages of development. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE. https://doi.org/10.1109/ICCMC51019.2021.9418263

  17. Giveki D, Soltanshahi MA, Montazer GA (2017) A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern. Optik 131:242–254. https://doi.org/10.1016/j.ijleo.2016.11.046

    Article  ADS  Google Scholar 

  18. Group TWB (2021) Agriculture and Food. 2021 [cited 2021 October 2, ]; Available from: https://www.worldbank.org/en/topic/agriculture/overview#1

  19. Guo Y et al (2020) Plant disease identification based on deep learning algorithm in smart farming. Discret Dyn Nat Soc 2020:1–11. https://doi.org/10.1155/2020/2479172

    Article  Google Scholar 

  20. Haridasan A et al (2023) Deep learning system for paddy plant disease detection and classification. Environ Monit Assess 195(1):120. https://doi.org/10.1007/s10661-022-10656-x

    Article  Google Scholar 

  21. Hong H, Lin J, Huang F (2020) Tomato disease detection and classification by deep learning. In: 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). IEEE. https://doi.org/10.1109/ICBAIE49996.2020.00012

  22. Huang X et al (2023) Tomato leaf disease detection system based on FC-SNDPN. Multimed Tools Appl 82(2):2121–2144. https://doi.org/10.1007/s11042-021-11790-3

    Article  Google Scholar 

  23. Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: A survey. Comput Electron Agric 147:70–90. https://doi.org/10.1016/j.compag.2018.02.016

    Article  Google Scholar 

  24. Kaur N et al (2021) Plant leaf disease detection using ensemble classification and feature extraction. 12(11):2339–2352

  25. Kaur N (2021) Plant leaf disease detection using ensemble classification and feature extraction. Turk J Comput Math Educ (TURCOMAT) 12(11):2339–2352

    Google Scholar 

  26. Khatoon S et al (2021) Image-based automatic diagnostic system for tomato plants using deep learning. Comput Mat Continua 67(1):595–612. https://doi.org/10.32604/cmc.2021.014580

    Article  MathSciNet  Google Scholar 

  27. Krishnamoorthy N et al (2021) Rice leaf diseases prediction using deep neural networks with transfer learning. Environ Res. 198:111275. https://doi.org/10.1016/j.envres.2021.111275

    Article  CAS  Google Scholar 

  28. Wan et al (2020) Plant disease classification using deep learning methods. In: Proceedings of the 4th International Conference on Machine Learning and Soft Computing. https://doi.org/10.1145/3380688.3380697

  29. Li M et al (2022) (2022) FWDGAN-based data augmentation for tomato leaf disease identification. Comput Electr Agric 194:106779. https://doi.org/10.1016/j.compag.2022.106779

    Article  Google Scholar 

  30. Li Y, Chao X (2020) ANN-based continual classification in agriculture. Agriculture 10(5):178. https://doi.org/10.3390/agriculture10050178

    Article  CAS  Google Scholar 

  31. Malathi V, Gopinath M (2021) Classification of pest detection in paddy crop based on transfer learning approach. Acta Agric Scandinav Section B–Soil & Plant Sci. 71(7):552–559. https://doi.org/10.1080/09064710.2021.1874045

    Article  Google Scholar 

  32. Mathew A et al (2022) Plant disease detection using GLCM feature extractor and voting classification approach. Mat Today Proc 58:407–415. https://doi.org/10.1016/j.matpr.2022.02.350

    Article  Google Scholar 

  33. Mim TT et al (2019) Leaves diseases detection of tomato using image processing. In: 2019 8th international conference system modeling and advancement in research trends (SMART).  IEEE. https://doi.org/10.1109/SMART46866.2019.9117437

  34. Mishra M, Choudhury P, Pati B (2021) Modified ride-NN optimizer for the IoT based plant disease detection. J Ambient Intell Humaniz Comput 12(1):691–703. https://doi.org/10.1007/s12652-020-02051-6

    Article  Google Scholar 

  35. Mohapatra S et al. (2023) Rice leaf disease detection and classification using a deep neural network. in: computing, communication and learning: first international conference, CoCoLe 2022, Warangal, India, October 27–29, 2022, Proceedings. 2023. Springer. https://doi.org/10.1007/978-3-031-21750-0_20

  36. Murala S, Maheshwari RP, Balasubramanian R (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process 21(5):2874–2886. https://doi.org/10.1109/TIP.2012.2188809

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  37. Nanni L, Lumini A, Brahnam S (2010) Local binary patterns variants as texture descriptors for medical image analysis. Artif Intell Med 49(2):117–125. https://doi.org/10.1016/j.artmed.2010.02.006

    Article  PubMed  Google Scholar 

  38. Climate change fans spread of pests and threatens plants and crops, new FAO study. 2021 [cited 2023 Monday, May 1]; Available from: https://www.fao.org/news/story/en/item/1402920/icode/

  39. Nikith B et al (2023) Leaf disease detection and classification. Procedia Comput Sci 218:291–300. https://doi.org/10.1016/j.procs.2023.01.011

    Article  Google Scholar 

  40. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Patt Recogn 29(1):51–59. https://doi.org/10.1016/0031-3203(95)00067-4

    Article  ADS  Google Scholar 

  41. Panigrahi KP et al (2020) Maize leaf disease detection and classification using machine learning algorithms. In: Progress in Computing, Analytics and Networking. Springer, Singapore. 659–669. https://doi.org/10.1007/978-981-15-2414-1_66

  42. Pantazi XE, Moshou D, Tamouridou AA (2019) Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers. Comput Electron Agric 156:96–104. https://doi.org/10.1016/j.compag.2018.11.005

    Article  Google Scholar 

  43. Prabhakar M et al (2020) Deep learning based assessment of disease severity for early blight in tomato crop. Multimed Tools Appl 79(39–40):28773–28784. https://doi.org/10.1007/s11042-020-09461-w

    Article  Google Scholar 

  44. Rahman SU et al (2023) Image processing based system for the detection, identification and treatment of tomato leaf diseases. Multimed Tools Appl 82(6):9431–9445. https://doi.org/10.1007/s11042-022-13715-0

    Article  Google Scholar 

  45. Rath A, Meher J (2019) Disease detection in infected plant leaf by computational method. Arch Phytopathol Plant Protect 52(19–20):1348–1358. https://doi.org/10.1080/03235408.2019.1708546

    Article  Google Scholar 

  46. Rehman A et al. (2021) Cucumber leaf disease classification using local tri-directional patterns and haralick features. In: 2021 International Conference on Artificial Intelligence (ICAI). 2021. IEEE. https://doi.org/10.1109/ICAI52203.2021.9445237

  47. Shrivastava VK, Pradhan MK (2021) Rice plant disease classification using color features: a machine learning paradigm. J Plant Pathol 103(1):17–26. https://doi.org/10.1007/s42161-020-00683-3

    Article  Google Scholar 

  48. Singh K, Kumar S, Kaur P (2019) Support vector machine classifier based detection of fungal rust disease in Pea Plant (Pisam sativam). Int J Inf Technol 11(3):485–492. https://doi.org/10.1007/s41870-018-0134-z

    Article  Google Scholar 

  49. Srivastava D, Rajitha B, Agarwal S (2018) Accurate detection and quantization of leaf-diseases through soft computing. Int J Comput Phys Ser 1(1):236–247. https://doi.org/10.29167/A1I1P236-247

    Article  Google Scholar 

  50. Sutha P et al (2021) Plant disease detection using fuzzy classification. Ann Romanian Soc Cell Biol 25(4):9430–9441

    Google Scholar 

  51. Syed-Ab-Rahman SF, Hesamian MH, Prasad M (2022) Citrus disease detection and classification using end-to-end anchor-based deep learning model. Appl Intell 52(1):927–938. https://doi.org/10.1007/s10489-021-02452-w

    Article  Google Scholar 

  52. Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650. https://doi.org/10.1109/TIP.2010.2042645

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  53. Wang Y et al (2020) The verification of Jevons’ paradox of agricultural Water conservation in Tianshan District of China based on Water footprint. Agric Water Manag 239:106163. https://doi.org/10.1016/j.agwat.2020.106163

    Article  ADS  Google Scholar 

  54. Weizheng S et al (2008) Grading method of leaf spot disease based on image processing. In: 2008 international conference on computer science and software engineering. IEEE.https://doi.org/10.1109/CSSE.2008.1649

  55. Wen J et al (2020) Crop Disease classification on inadequate low-resolution target images. Sensors (Basel) 20(16):4601. https://doi.org/10.3390/s20164601

    Article  ADS  PubMed  Google Scholar 

  56. Yulita IN, Amri NA, Hidayat A (2023) Mobile application for tomato plant leaf disease detection using a dense convolutional network architecture. Computation 11(2):20. https://doi.org/10.3390/computation11020020

    Article  Google Scholar 

  57. Zafar MZ (2019) Efficient algorithm for MRI based brain tumor detection using computational intelligence, In: Department of Computer Science 2019 University of Engineering and Technology (UET) Unpublished Taxila - Pakistan 1 40

  58. Zafar MZ et al (2019) Brain tumor detection and classification using geometrical shapes as texture descriptors 24(1):83–89

    Google Scholar 

  59. Zhang R et al (2023) IBSA_Net: A network for tomato leaf disease identification based on transfer learning with small samples. Appl Sci 13(7):4348. https://doi.org/10.3390/app13074348

    Article  CAS  Google Scholar 

  60. Zhang S, Shang Y, Wang L (2015) Plant disease recognition based on plant leaf image. J Anim Plant Sci 25(3):42–45

    Google Scholar 

  61. Zhao S et al (2021) Tomato Leaf disease diagnosis based on improved convolution neural network by attention module. Agriculture 11(7):651. https://doi.org/10.3390/agriculture11070651

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan

    Wakeel Ahmad, Syed M. Adnan & Aun Irtaza

Authors
  1. Wakeel Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Syed M. Adnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aun Irtaza
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Dr. Wakeel Ahmad performed the experiments, derived the model, and analyzed the data with the help of Dr. Syed M. Adnan. Dr. Ahmad and Dr. Adnan wrote the paper in consultation with Dr. Aun Irtaza. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Wakeel Ahmad.

Ethics declarations

Conflict of interests

We declare that we have no conflict of interest.

Additional information

Publisher's note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmad, W., Adnan, S.M. & Irtaza, A. Local triangular-ternary pattern: a novel feature descriptor for plant leaf disease detection. Multimed Tools Appl 83, 20215–20241 (2024). https://doi.org/10.1007/s11042-023-16420-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16420-8

Keywords

Search

Navigation