A Hybrid Deep Learning and Optimized Machine Learning Approach for Rose Leaf Disease Classification
Published online first on September 24, 2021.
Analysis of the symptoms of rose leaves can identify up to 15 different diseases. This research aims to develop Convolutional Neural Network models for classifying the diseases on rose leaves using hybrid deep learning techniques with Support Vector Machine (SVM). The developed models were based on the VGG16 architecture and early or late fusion techniques were applied to concatenate the output from a fully connected layer. The results showed that the developed models based on early fusion performed better than the developed models on either late fusion or VGG16 alone. In addition, it was found that the models using the SVM classifier had better efficiency in classifying the diseases appearing on rose leaves than the models using the softmax function classifier. In particular, a hybrid deep learning model based on early fusion and SVM, which applied the categorical hinge loss function, yielded a validation accuracy of 88.33% and a validation loss of 0.0679, which were higher than the ones of the other models. Moreover, this model was evaluated by 10-fold cross-validation with 90.26% accuracy, 90.59% precision, 92.44% recall, and 91.50% F1-score for disease classification on rose leaves.
Keywords:hybrid deep learning, neural network, rose disease, support vector machine
"Top countries for Export of Roses," NationMaster. https://www.nationmaster.com/nmx/ranking/export-of-roses (accessed Sep. 21, 2021).
R. K. Horst and R. A. Cloyd, Compendium of Rose Diseases and Pests. Second Edition, 2nd ed. St. Paul, MI, USA: The American Phytopathological Society, 2007.
I. Vazquez-Iglesias et al., "High throughput sequencing and RT-qPCR assay reveal the presence of rose cryptic virus-1 in the United Kingdom," Journal of Plant Pathology, vol. 101, no. 4, pp. 1171–1175, Nov. 2019.
S. Minaee, M. Jafari, and N. Safaie, "Design and development of a rose plant disease-detection and site-specific spraying system based on a combination of infrared and visible images," Journal of Agricultural Science and Technology, vol. 20, no. 1, pp. 23–36, Jan. 2018.
D. Das, M. Singh, S. S. Mohanty, and S. Chakravarty, "Leaf Disease Detection using Support Vector Machine," in 2020 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, Jul. 2020, pp. 1036–1040.
A. A. Bharate and M. S. Shirdhonkar, "A review on plant disease detection using image processing," in 2017 International Conference on Intelligent Sustainable Systems (ICISS), Palladam, India, Dec. 2017, pp. 103–109.
K. Swetharani and V. Prasad, "Design and Implementation of an Efficient Rose Leaf Disease Detection using K-Nearest Neighbours," International Journal of Recent Technology and Engineering, vol. 9, no. 3, pp. 21–27, Sep. 2020.
A. Rajbongshi, T. Sarker, Md. M. Ahamad, and Md. M. Rahman, "Rose Diseases Recognition using MobileNet," in 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Istanbul, Turkey, Oct. 2020.
C. Rother, V. Kolmogorov, and A. Blake, "‘GrabCut’: interactive foreground extraction using iterated graph cuts," ACM Transactions on Graphics, vol. 23, no. 3, pp. 309–314, Aug. 2004.
"OpenCV: Miscellaneous Image Transformations," OpenCV. https://docs.opencv.org/master/d7/d1b/group__imgproc__misc.html (accessed Sep. 21, 2021).
K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv:1409.1556 [cs], Apr. 2015, Accessed: Sep. 21, 2021. [Online]. Available: http://arxiv.org/abs/1409.1556.
"tf.keras.losses.CategoricalHinge," TensorFlow. https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalHinge (accessed Sep. 21, 2021).
J. Weston and C. Watkins, "Support Vector Machines for Multi-Class Pattern Recognition," presented at the 7th European Symposium on Artificial Neural Networks, Bruges, Belgium, Jan. 1999.
L. Poomhiran, P. Meesad, and S. Nuanmeesri, "Improving the Recognition Performance of Lip Reading Using the Concatenated Three Sequence Keyframe Image Technique," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6986–6992, Apr. 2021.
S. Nuanmeesri, "Development of community tourism enhancement in emerging cities using gamification and adaptive tourism recommendation," Journal of King Saud University - Computer and Information Sciences, Apr. 2021.
S. Nuanmeesri and W. Sriurai, "Thai Water Buffalo Disease Analysis with the Application of Feature Selection Technique and Multi-Layer Perceptron Neural Network," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6907–6911, Apr. 2021.
A. N. Saeed, "A Machine Learning based Approach for Segmenting Retinal Nerve Images using Artificial Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 5986–5991, Aug. 2020.
H. Hasan, H. Z. M. Shafri, and M. Habshi, "A Comparison Between Support Vector Machine (SVM) and Convolutional Neural Network (CNN) Models For Hyperspectral Image Classification," IOP Conference Series: Earth and Environmental Science, vol. 357, Nov. 2019, Art. no. 012035.
How to Cite
MetricsAbstract Views: 66
PDF Downloads: 45
Copyright (c) 2021 S. Nuanmeesri
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.