Skip to main content

Advertisement

SpringerLink
Log in

PlantDiseaseNet: convolutional neural network ensemble for plant disease and pest detection

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Plant diseases and pests cause significant losses in agriculture, with economic, ecological and social implications. Therefore, early detection of plant diseases and pests via automated methods are very important. Recent machine learning-based studies have become popular in the solution of agricultural problems such as plant diseases. In this work, we present two classification models based on deep feature extraction from pre-trained convolutional neural networks. In the proposed models, we fine-tune and combine six state-of-the-art convolutional neural networks and evaluate them on the given problem both individually and as an ensemble. Finally, the performances of different combinations based on the proposed models are calculated using a support vector machine (SVM) classifier. In order to verify the validity of the proposed model, we collected Turkey-PlantDataset, consisting of unconstrained photographs of 15 kinds of disease and pest images observed in Turkey. According to the obtained performance results, the accuracy scores are calculated as 97.56% using the majority voting ensemble model and 96.83% using the early fusion ensemble model. The results demonstrate that the proposed models reach or exceed state-of-the-art results for this problem.

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

Similar content being viewed by others

References

  1. Kebapci, H., Yanikoglu, B., Unal, G.: Plant image retrieval using color, shape and texture features. Comput. J. 54(9), 1475–1490 (2011)

    Article  Google Scholar 

  2. Yanikoglu, B., Aptoula, E., Tirkaz, C.: Automatic plant identification from photographs. Mach. Vis. Appl. 25(6), 1369–1383 (2014)

    Article  Google Scholar 

  3. Turkoglu, M., Hanbay, D.: Apricot Disease Identification based on attributes obtained from Deep Learning Algorithms, In Proceedings of International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, (2018).

  4. Too, E.C., Yujian, L., Njuki, S., Yingchun, L.: A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric. 161, 272–279 (2019)

    Article  Google Scholar 

  5. Rahman, C.R., Arko, P.S., Ali, M.E., Khan, M.A.I., Apon, S.H., Nowrin, F., Wasif, A.: Identification and recognition of rice diseases and pests using convolutional neural networks. Biosys. Eng. 194, 112–120 (2020)

    Article  Google Scholar 

  6. Barbedo, J.G.A.: Plant disease identification from individual lesions and spots using deep learning. Biosys. Eng. 180, 96–107 (2019)

    Article  Google Scholar 

  7. Turkoglu, M., Hanbay, D.: Plant disease and pest detection using deep learning-based features. Turk. J. Electr. Eng. Comput. Sci. 27(3), 1636–1651 (2019)

    Article  Google Scholar 

  8. Shabanzade, M., Zahedi, M., Aghvami, S.A.: Combination of local descriptors and global features for leaf recognition. Signal and Image Processing 2(3), 23–31 (2011)

    Google Scholar 

  9. Singh, V., Misra, A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture 4(1), 41–49 (2017)

    Article  Google Scholar 

  10. Turkoglu, M., Hanbay, D.: Classification of the grape varieties based on leaf recognition by using SVM classifier. In 23rd Signal Processing and Communications Applications Conference (SIU), pp. 2674–2677 (2015).

  11. Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018)

    Article  Google Scholar 

  12. Geetharamani, G., Pandian, A.: Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput. Electr. Eng. 76, 323–338 (2019)

    Article  Google Scholar 

  13. Turkoglu, M., Hanbay, D.: Plant recognition system based on extreme learning machine by using shearlet transform and new geometric features. Journal of the Faculty of Engineering and Architecture of Gazi University 34(4), 2097–2112 (2019)

    Google Scholar 

  14. Athanikar, G., Badar, P.: Potato Leaf Diseases Detection and Classification System. Int. J. Comput. Sci. Mob. Comput. 5(2), 76–88 (2016)

    Google Scholar 

  15. Dubey, S.R., Jalal, A.S.: Apple disease classification using color, texture and shape features from images. SIViP 10(5), 819–826 (2016)

    Article  Google Scholar 

  16. Padol, P.B., Yadav, A.A.: SVM classifier based grape leaf disease detection, In Proceedings of 2016 Conference on Advances in Signal Processing (CASP), pp. 175–179 (2016).

  17. Pujari, J.D., Yakkundimath, R., Byadgi, A.S.: Classification of fungal disease symptoms affected on cereals using color texture features. International Journal of Signal Processing, Image Processing and Pattern Recognition 6(6), 321–330 (2013)

    Article  Google Scholar 

  18. Prasad, S., Peddoju, S.K., Ghosh, D.: Multi-resolution mobile vision system for plant leaf disease diagnosis. SIViP 10(2), 379–388 (2016)

    Article  Google Scholar 

  19. Waghmare, H., Kokare, R., Dandawate, Y.: Detection and classification of diseases of Grape plant using opposite colour Local Binary Pattern feature and machine learning for automated Decision Support System. In Proceedings of 3rd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 513–518 (2016).

  20. Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., Hughes, D.P.: Deep learning for image-based cassava disease detection. Front. Plant Sci. 8, 1852 (2017)

    Article  Google Scholar 

  21. Lu, Y., Yi, S., Zeng, N., Liu, Y., Zhang, Y.: Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267, 378–384 (2017)

    Article  Google Scholar 

  22. Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., Johannes, A.: Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput. Electron. Agric. 161, 280–290 (2019)

    Article  Google Scholar 

  23. Kurniawati, N.N., Abdullah, S.N.H.S., Abdullah, S., Abdullah, S.: Investigation on image processing techniques for diagnosing paddy diseases. In Proceedings of 2009 International Conference of Soft Computing and Pattern Recognition, pp. 272–277 (2009).

  24. Dandawate, Y., Kokare, R.: An automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective. In Proceedings of IEEE international conference on advances in computing, communications and informatics (ICACCI), pp. 794–799 (2015).

  25. Yun, S., Xianfeng, W., Shanwen, Z., Chuanlei, Z.: PNN based crop disease recognition with leaf image features and meteorological data. International Journal of Agricultural and Biological Engineering 8(4), 60–68 (2015)

    Google Scholar 

  26. Ramakrishnan, M.: Groundnut leaf disease detection and classification by using back probagation algorithm. In IEEE International Conference on Communications and Signal Processing (ICCSP), pp. 964–968 (2015).

  27. Sabrol, H., Satish, K.: Tomato plant disease classification in digital images using classification tree. In Proceedings of 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 1242–1246 (2016).

  28. Kaur, S., Pandey, S., Goel, S.: Semi-automatic leaf disease detection and classification system for soybean culture. IET Image Proc. 12(6), 1038–1048 (2018)

    Article  Google Scholar 

  29. Hossain, M.S., Mou, R.M., Hasan, M.M., Chakraborty, S., Razzak, M.A.: Recognition and detection of tea leaf's diseases using support vector machine. In Proceedings of 14th International Colloquium on Signal Processing and Its Applications (CSPA), pp. 150–154 (2018).

  30. Arsenovic, M., Karanovic, M., Sladojevic, S., Anderla, A., Stefanovic, D.: Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection. Symmetry, 11(7), Art. 939 (2019).

  31. Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, Art. 1419 (2016).

  32. Fujita, E., Kawasaki, Y., Uga, H., Kagiwada, S., Iyatomi, H.: Basic investigation on a robust and practical plant diagnostic system. In Proceedings of 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 989–992 (2016).

  33. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016, Art. 3289801 (2016).

  34. Liu, B., Zhang, Y., He, D., Li, Y.: Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 10(1), Art. 11 (2017).

  35. Amara, J., Bouaziz, B., Algergawy, A.: A Deep Learning-based Approach for Banana Leaf Diseases Classification, In B. Mitschang, N. Ritter, H. Schwarz, M. Klettke, A. Thor, O. Kopp, M. Wieland (Eds.), Datenbanksysteme für Business, Technologie und Web (BTW 2017), pp. 79–88 (2017).

  36. Wang, G., Sun, Y., Wang, J.: Automatic image-based plant disease severity estimation using deep learning. Computational Intelligence and Neuroscience, Art. 2917536 (2017).

  37. Brahimi, M., Boukhalfa, K., Moussaoui, A.: Deep learning for tomato diseases: classification and symptoms visualization. Appl. Artif. Intell. 31(4), 299–315 (2017)

    Article  Google Scholar 

  38. Sapkal, A.T., Kulkarni, U.V.: Comparative study of leaf disease diagnosis system using texture features and deep learning features. Int. J. Appl. Eng. Res. 13(19), 14334–14340 (2018)

    Google Scholar 

  39. Wallelign, S., Polceanu, M., Buche, C.: Soybean Plant Disease Identification Using Convolutional Neural Network. In K. Brawner and V. Rus (Eds.), Proceedings of 31st International Flairs Conference, pp. 146–151 (2018).

  40. Barbedo, J.G.: Factors influencing the use of deep learning for plant disease recognition. Biosys. Eng. 172, 84–91 (2018)

    Article  Google Scholar 

  41. Altuntaş, Y., Cömert, Z., Kocamaz, A.F.: Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach. Computers and Electronics in Agriculture, 163, Art. 104874 (2019).

  42. Özgüven, M.M., Adem, K.: Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Physica A: Statistical Mechanics and its Applications, 535, Art. 122537 (2019).

  43. Hu, G., Wu, H., Zhang, Y., Wan, M.: A low shot learning method for tea leaf’s disease identification. Computers and Electronics in Agriculture, 163, Art. 104852 (2019).

  44. Zhang, S., Huang, Z., Zhang, C.: Three-channel convolutional neural networks for vegetable leaf disease recognition. Cogn. Syst. Res. 53, 31–41 (2019)

    Article  Google Scholar 

  45. Wang, L., Sun, J., Wu, X., Shen, J., Lu, B., Tan, W.: Identification of crop diseases using improved convolutional neural networks. IET Comput. Vision 14(7), 538–545 (2020)

    Article  Google Scholar 

  46. Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173, 105393.

  47. Zhong, Y., & Zhao, M. (2020). Research on deep learning in apple leaf disease recognition. Computers and Electronics in Agriculture, 168, 105146.

  48. Li, Y., Wang, H., Dang, L. M., Sadeghi-Niaraki, A., & Moon, H. (2020). Crop pest recognition in natural scenes using convolutional neural networks. Computers and Electronics in Agriculture, 169, 105174.

  49. Jiang, F., Lu, Y., Chen, Y., Cai, D., & Li, G. (2020). Image recognition of four rice leaf diseases based on deep learning and support vector machine. Computers and Electronics in Agriculture, 179, 105824.

  50. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems pp. 1097–1105 (2012). Neural Information Processing Systems Foundation.

  51. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015).

  52. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016).

  53. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700–4708 (2017).

  54. Chen, X., Zhu, Y., Zhou, H., Diao, L., Wang, D.: Chinesefoodnet: A large-scale image dataset for Chinese food recognition, (2017). arXiv https://arxiv.org/abs/1705.02743v3.

  55. Kamal, U., Rafi, A. M., Hoque, R., Das, S., Abrar, A., Hasan, M.: Application of DenseNet in Camera Model Identification and Post-processing Detection, (2018). arXiv. https://arxiv.org/abs/1809.00576v2.

  56. Zhang, F., Xu, X., Xiao, Z., Wu, J., Geng, L., Wang, W., Liu, Y.: Automated quality classification of colour fundus images based on a modified residual dense block network. SIViP 14(1), 215–223 (2020)

    Article  Google Scholar 

  57. Tsang, S.-H. (2018, November, 25). Review: DenseNet – Dense Convolutional Network (Image Classification). Towards Data Science. https://towardsdatascience.com/review-densenet-image-classification-b6631a8ef803.

  58. Turkoglu, M. (2019). Image Processing Based Plant Species and Diseases Recognition. Ph.D. Thesis, Inonu University, Turkey.

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Software Engineering, Faculty of Engineering, Samsun University, Samsun, Turkey

    Muammer Turkoglu

  2. Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey

    Berrin Yanikoğlu

  3. Department of Computer Engineering, Faculty of Engineering, Inonu University, Malatya, Turkey

    Davut Hanbay

Authors
  1. Muammer Turkoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Berrin Yanikoğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Davut Hanbay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muammer Turkoglu.

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

Turkoglu, M., Yanikoğlu, B. & Hanbay, D. PlantDiseaseNet: convolutional neural network ensemble for plant disease and pest detection. SIViP 16, 301–309 (2022). https://doi.org/10.1007/s11760-021-01909-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-021-01909-2

Keywords

Search

Navigation