Cognitive framework and learning paradigms of plant leaf classification using artificial neural network and support vector machine

Sharma, Gajanand; Kumar, Ashutosh; Gour, Nidhi; Saini, Ashok Kumar; Upadhyay, Aditya; Kumar, Ankit

doi:10.1080/0952813x.2022.2096698

Cited by 5 publications

(1 citation statement)

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“…At the end of the study, the CNN-SVM structure based on the selected features performed in the last step performed more successful classification with 97.60% accuracy. Sharma et al [27] performed leaf classification based on color and texture features. They used HSV (hue, saturation, value) color space to extract color features and the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to extract texture features.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of CNN Models and Bayesian Optimization-Based Machine Learning Algorithms in Leaf Type Classification

Aslan

2023

Balkan Journal of Electrical and Computer Engineering

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In this study, the leaves are classified by various Machine Learning (ML) and Deep Learning (DL) based Convolutional Neural Networks (CNN) methods. In the proposed method, first, image pre-processing is performed to increase the accuracy of the posterior process. The obtained image is a grayscale image without noise as a result of the pre-processing. These preprocessed images are used in classification with ML and DL. The Speeded Up Robust Features (SURF) are extracted from the grayscale image for ML-based learning. The features are restructured as visual words using the Bag of Visual Words (BoVW) method. Then, histograms are generated for each image according to the frequency of the visual word. Those histograms represent the new feature data. The histogram features are classified by four different ML methods, Decision Tree (DT), k-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). Before using the ML methods, Bayesian Optimization (BO) method, which is one of the Hyperparameter Optimization (HO) algorithms, is applied to determine hyperparameters. In the classification process performed with four different ML algorithms, the best accuracy is achieved with the KNN algorithm as 98.09%. Resnet18, ResNet50, MobileNet, GoogLeNet, DenseNet, which are state-of-the-art CNN architectures, are used for DL-based learning. CNN models have higher accuracy than ML algorithms.

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Section: Related Workmentioning

confidence: 99%