A Study on the Performance of Hybrid Approach for Image Classification using CNNs and SVM for Plant Disease Detection

Authors : Nahid Hasan Ashik; Rakibul Islam; Md. Arif Hossain; Toukir Ahammed; Md Shakirul Islam

Volume/Issue : Volume 8 - 2023, Issue 10 - October

Google Scholar : https://tinyurl.com/bdfx23wh

Scribd : https://tinyurl.com/5222ap3e

DOI : https://doi.org/10.5281/zenodo.10029575

In a country where agriculture is the primary industry, plant diseases can have a wide range of adverse effects on the economy and the management of food resources. The increasing occurrence of plant diseases significantly threatens global food security and plant productivity. Classification is constantly constrained by issues like overfitting and low accuracy as potential diseases manifest on plant leaves. The agricultural sector needs accurate and error-free analysis to distinguish healthy products from defective ones. An effective model of autonomous feature extraction that has been demonstrated to be reasonably effective for detection and classification tasks is deep convolutional neural networks. Deep convolutional neural networks, on the other hand, frequently require a substantial amount of training data, cannot be translated, and require a number of parameters to be specified and adjusted. In this study, we suggested a hybrid deep learning approach for quickly identifying and categorizing different plant leaf diseases. This hybrid system combines support vector machines (SVM), convolutional neural networks (CNN), and both. To summarize, we extract features through model engineering (ME). To improve feature discrimination and processing speed, support vector machine (SVM) models are used. Using the datasets, 25 leaf image sets of healthy and diseased leaves of tomato, potato, grape, apple, and corn were analyzed. There were roughly 31397 images produced as a result of the classification process. SVM is a key component of hybrid feature selection; this algorithm's goal is to produce the desired output with the fewest features possible. By utilizing the aforementioned evaluation criteria, a comparative analysis of both techniques is presented.

Keywords : Plant Disease Detection, Image Processing, CNN, VGG19, Xception, DenseNet201, ResNet15V2, SVM.


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31 - December - 2023

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