Abstract
Grape leaf diseases have a major impact on the growth of grape industry and grape crop yield. Thus, there is a need of a grape disease detection in early stages of disease so that disease spread and their impact could be controlled and development and production of grape industry remain continuous and active. However, the detection of grape leaf disease in initial stages is highly critical and challenging. Therefore, in this article, machine learning technique is adopted for the early detection of grape leaf disease and accurately distinguish between various classes of disease. Furthermore, Convolutional Neural Network based Classification (CNNC) model and improvised K- Nearest Neighbor (IKNN) model are introduced for classification of leaf diseases. High quality histogram and extended histogram features are obtained to provide structural, pattern, boundary and discriminative information. Then, classification process is performed on the obtained high quality gradient based features. Classification accuracy is improved to a great extent using proposed CNNC and IKNN model. The accuracy of the proposed CNNC and IKKN model is tested with the help of public dataset named as Plant-Village Dataset. The performance of proposed CNNC and IKKN model is compared with various traditional classification models considering 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. https://www.fao.org/publications/card/en/c/3af06e4a-741b-5b3d-bd24-695cb079fb8a/
Amara J, Bouaziz B, Algergawy A (2017). A deep learning-based approach for banana leaf diseases classification. 79–88. http://btw2017.informatik.uni-stuttgart.de/slidesandpapers/E1-10/paper_web.pdf
Burrell J, Brooke T, Beckwith R (2004) Vineyard computing: sensornetworks 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. https://openaccess.thecvf.com/content_cvpr_2017/papers/Chollet_Xception_Deep_Learning_CVPR_2017_paper.pdf
Colomina I, Molina P (2014) Unmanned aerial systems for photogrammetryand remote sensing: a review. ISPRS J Photogramm Remote Sens 92:79–97
Hall A, Lamb DW, Holzapfel B, Louis J (2002) Optical remote sensingapplications in viticulture – a review. Aust J Grape Wine Res 8:36–47
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR), pp. 770-778. https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html
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. https://arxiv.org/abs/1608.06993
International Organization of Vine and Wine (OIV) (2009) Balance de la OIVsobre la situación vitivinícola mundial, Available online:http://www.infowine.com/docs/Communique_Stats_Tbilissi_ES.pdf. Accessed July 31, 2017
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.
KrizhevskyI Sutskever A, Hinton GE (2012) Imagenet classi_cation with deep convolutional neural networks. In Proc. Adv Neural Inf Process Syst, pp 1097–1105. https://papers.nips.cc/paper/4824-imagenet-classification-with-deepconvolutional- neural-networks
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
Padol PB, Yadav AA (2016) SVM classifier based grape leaf disease detection. 2016 Conference on Advances in Signal Processing (CASP), Pune, pp. 175–179. https://doi.org/10.1109/CASP.2016.7746160
Ruiz-Garcia L, Lunadei L, Barreiro P, Robla JI (2009) A review of wirelesssensor technologies and applications in agriculture and food industry:state of the art and current trends. Sensors 9:4728–4750
Seng KP, Ang L, Schmidtke LM, Rogiers SY (2018) Computer vision and machine learning for viticulture technology. IEEE Access 6:67494–67510. https://doi.org/10.1109/ACCESS.2018.2875862
Shekhawat R, Sinha A (2020) Review of image processing approaches for detecting plant diseases. IET Image Process 14:1427–1439. https://doi.org/10.1049/iet-ipr.2018.6210
Shikhamany S (2000) Grape production in India. Viticulture (Grape Production) in Asia and the Pacific. https://nrcgrapes.icar.gov.in/NRCG%20%20old%20website%20as%20on%2031-05-2019/The%20organisationframe.htm
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proc Int Conf Learn Represent, pp 1–14. https://arxiv.org/abs/1409.1556
Zhu J, Wu A, Wang X, Zhang H (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
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shantkumari, M., Uma, S.V. Grape leaf image classification based on machine learning technique for accurate leaf disease detection. Multimed Tools Appl 82, 1477–1487 (2023). https://doi.org/10.1007/s11042-022-12976-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-12976-z