Abstract
Grape leaf diseases like Black Rot, Eska measles, Leaf Spot and Healthy are among the most common disease types of the grape crop. Accurate detection of grape leaf diseases in the initial stages can control the disease spread significantly and guarantee progressive development of the grape crop industry. The existing research provides several complex image processing algorithms and cannot assure high classification accuracy. Therefore, machine learning techniques are presented in this article to enhance leaf disease classification accuracy for efficiently detecting grape leaf diseases. Moreover, two classification models are introduced in which the simple Convolutional Neural Network based Classification (CNNC) Model is detailed. Then the improvised K-Nearest Neighbour (IKNN) model for precisely detecting grape leaf diseases is detailed. Moreover, pixel encoding methods are presented to obtain a histogram representation of extracted features. Training of simple CNNC and the proposed IKNN model is conducted on Plant-Village Dataset. Additionally, mathematical modeling is presented to formulate the problem in the feature extraction process. Moreover, Confined Intensity Directional Order Relation (CIDOR) operation ensures low dimensionality of histogram representation in the multiscale domain. Furthermore, Global Pixel Order Relation (GPOR) focuses on setting up a communication with long-reach pixels of an image outside of the central pixel neighborhood. Compared to the simple CNNC, the proposed IKNN model outperforms all the traditional leaf disease classification algorithms in terms of classification accuracy. However, the IKNN model provides superior results than CNNC comparatively in terms of classification accuracy.
Similar content being viewed by others
References
A report of the expert consultation on viticulture in Asia and the Pacific (2000) Bankok, Thailand. RAP publication: 2000/13
Adeel A, Khan MA, Sharif M, Azam F, Shah JH, Umer T, Wan S (2019) Diagnosis and recognition of grape leaf diseases: an automated system based on a novel saliency approach and canonical correlati analysis based multiple features fusion. Sustain Comput: Inf Syst 24:100349
Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification, pp 79–88
Burrell J, Brooke T, Beckwith R (2004) Vineyard computing: sensor networks in agricultural production. IEEE Pervasive Comput 3:38–45
Chollet F (2017) "Xception: deep learning with depthwise separable convolutions", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1800-1807
Chouhan SS, Kaul A, Singh UP, Jain S (2018) Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: an automatic approach towards plant pathology. IEEE Access 6:8852–8863. https://doi.org/10.1109/ACCESS.2018.2800685
Colomina I, Molina P (2014) Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J Photogramm Remote Sens 92:79–97
Dai Q, Cheng X, Qiao Y, Zhang Y (2020) Crop leaf disease image super-resolution and identification with dual attention and topology fusion generative adversarial network. IEEE Access 8:55724–55735. https://doi.org/10.1109/ACCESS.2020.2982055
El Massi I, Es-saady Y, El Yassa M et al (2020) Combination of multiple classifiers for automatic recognition of diseases and damages on plant leaves. SIViP. https://doi.org/10.1007/s11760-020-01797-y
Golhani K, Balasundram SK, Vadamalai G, Pradhan B (2018) A review of neural networks in plant disease detection using hyperspectral data. Inf Process Agric 5:354–371. https://doi.org/10.1016/j.inpa.2018.05.002
Hall A, Lamb DW, Holzapfel B, Louis J (2002) Optical remote sensing applications in viticulture – a review. Aust J Grape Wine Res 8:36–47
K. He, X. Zhang, S. Ren, J. Sun (2016) "Deep residual learning for image recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 770-778
Hu J, Shen L, Albanie S, Sun G, Wu E (2019) "Squeeze-andexcitation networks," IEEE Trans Pattern Anal Mach Intell. Early access
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) "Densely connected convolutional networks", in Proc. IEEE Conf. Comput. Vis.Pattern Recognit. (CVPR), pp. 4700-4708
Huang Z, Qin A, Lu J, Menon A, Gao J (2020) "Grape leaf disease detection and classification using machine learning," In 2020 international conferences on internet of things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE smart data (SmartData) and IEEE congress on Cybermatics (Cybermatics) (pp. 870–877). IEEE
International Organization of Vine and Wine (OIV) (2009) Balance de la OIV sobre la situaciónvitivinícolamundialen. Available online: http://www.infowine.com/docs/Communique_Stats_Tbilissi_ES.pdf. Accessed 31 July 2017
Ji M, Zhang L, Wu Q (2020) Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks. Inf Process Agric 7(3):418–426
Jia S, Zhang Y (2018) Saliency-based deep convolutional neural network for no-reference image quality assessment. Multimed Tools Appl 77
Jiang P, Chen Y, Liu B, He D, Liang C (2019) Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access 7:59069–59080. https://doi.org/10.1109/ACCESS.2019.2914929
Jogekar R, Tiwari N (2020) "Summary of leaf-based plant disease detection systems: a compilation of systematic study findings to classify the leaf disease classification schemes," 2020 fourth world conference on smart trends in systems, security and sustainability (WorldS4), London, United Kingdom, pp. 745–750, https://doi.org/10.1109/WorldS450073.2020.9210401
KrizhevskyISutskever A, Hinton GE (2012) "Imagenetclassi_cation with deep convolutional neural networks," in Proc. Adv. Neural Inf. Process. Syst., pp. 1097-1105
Liu B, Zhang Y, He D, Li Y (2018) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10:11
Liu B, Tan C, Li S, He J, Wang H (2020) A data augmentation method based on generative adversarial networks for grape leaf disease identification. IEEE Access 8:102188–102198. https://doi.org/10.1109/ACCESS.2020.2998839
Liu B, Ding Z, Tian L, He D, Li S, Wang H (2020) Grape leaf disease identification using improved deep convolutional neural networks. Front Plant Sci 11:1082. Published 2020 Jul 15. https://doi.org/10.3389/fpls.2020.01082
Mohammed KK, Darwish A, Hassenian AE (2021) Artificial intelligent system for grape leaf diseases classification. In: Hassanien A, Bhatnagar R, Darwish A (eds) Artificial intelligence for sustainable development: theory, practice and future applications. Studies in computational intelligence, vol 912. Springer
Ngugi LC, Abelwahab M, Abo-Zahhad M (2020) Recent advances in image processing techniques for automated leaf pest and disease recognition – a review. Inf Process Agric https://doi.org/10.1016/j.inpa.2020.04.004
Padol PB, Yadav AA (2016) SVM classifier based grape leaf disease detection. In: 2016 Conference on Advances in Signal Processing (CASP), Pune, pp 175–179. https://doi.org/10.1109/CASP.2016.7746160
Pereira CS, Morais R, Reis MJ (2019) Deep learning techniques for grape plant species identification in natural images. Sensors 19(22):4850
Pham TN, Tran LV, Dao SVT (2020) Early disease classification of mango leaves using feed-forward neural network and hybrid Metaheuristic feature selection. IEEE Access 8:189960–189973. https://doi.org/10.1109/ACCESS.2020.3031914
Plant Village dataset. https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset. Last accessed on March 2022
Ruiz-Garcia L, Lunadei L, Barreiro P, Robla JI (2009) A review of wireless sensor technologies and applications in agriculture and food industry: state of the art and current trends. Sensors 9:4728–4750
Sanath Rao U, Swathi R, Sanjana V, Arpitha L, Chandrasekhar K, Chinmayi, Naik PK (2021) Deep learning precision farming: grapes and mango leaf disease detection by transfer learning. Glob Trans Proc 2(2)
Shekhawat R, Sinha A (2020) Review of image processing approaches for detecting plant diseases. IET Image Process 14. https://doi.org/10.1049/iet-ipr.2018.6210
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proc. Int. Conf. Learn. Represent., pp 1–14
Tan M, Le QV (2019) "Ef_cientNet: rethinking model scaling for convolutional neural networks," in Proc. Int. Conf. Mach. Learn., pp. 6105-6114
Xie S, Girshick R, Dollar P, Tu Z, He K(2017) "Aggregated residual transformations for deep neural networks," in Proc. IEEE Conf. Comput.Vis. Pattern Recognit. (CVPR), pp. 5987-5995
Xie X, Ma Y, Liu B, He J, Li S, Wang H (2020) A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks. Front Plant Sci 11:751
XieXiaoyue MY, Bin L, Jinrong H, Shuqin L, Hongyan W (2020) A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks. Front Plant Sci 11:751
Zeng Q, Ma X, Cheng B, Zhou E, Pang W (2020) GANs-based data augmentation for Citrus disease severity detection using deep learning. IEEE Access 8:172882–172891. https://doi.org/10.1109/ACCESS.2020.3025196
Zhu J, Wu A, Wang X et al (2020) Identification of grape diseases using image analysis and BP neural networks. Multimed Tools Appl 79:14539–14551. https://doi.org/10.1007/s11042-018-7092-0
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
The authors declare that they have no conflict of interest.
Data availability
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
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.
About this article
Cite this article
Shantkumari, M., Uma, S.V. Machine learning techniques implementation for detection of grape leaf disease. Multimed Tools Appl 82, 30709–30731 (2023). https://doi.org/10.1007/s11042-023-14441-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-14441-x