A Hybrid Deep Learning and Optimized Machine Learning Approach for Rose Leaf Disease Classification
Open Access
- 12 October 2021
- journal article
- Published by Engineering, Technology & Applied Science Research in Engineering, Technology & Applied Science Research
- Vol. 11 (5), 7678-7683
- https://doi.org/10.48084/etasr.4455
Abstract
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
This publication has 11 references indexed in Scilit:
- Improving the Recognition Performance of Lip Reading Using the Concatenated Three Sequence Keyframe Image TechniqueEngineering, Technology & Applied Science Research, 2021
- Thai Water Buffalo Disease Analysis with the Application of Feature Selection Technique and Multi-Layer Perceptron Neural NetworkEngineering, Technology & Applied Science Research, 2021
- Rose Diseases Recognition using MobileNetPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2020
- Design and Implementation of an Efficient Rose Leaf Disease Detection using K Nearest NeighboursInternational Journal of Recent Technology and Engineering (IJRTE), 2020
- A Machine Learning based Approach for Segmenting Retinal Nerve Images using Artificial Neural NetworksEngineering, Technology & Applied Science Research, 2020
- Leaf Disease Detection using Support Vector MachinePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2020
- A Comparison Between Support Vector Machine (SVM) and Convolutional Neural Network (CNN) Models For Hyperspectral Image ClassificationIOP Conference Series: Earth and Environmental Science, 2019
- High throughput sequencing and RT-qPCR assay reveal the presence of rose cryptic virus-1 in the United KingdomJournal of Plant Pathology, 2019
- A review on plant disease detection using image processingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- "GrabCut"ACM Transactions on Graphics, 2004