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access icon free DDLA: dual deep learning architecture for classification of plant species

© The Institution of Engineering and Technology
Received 21/03/2019, Accepted 23/07/2019, Revised 17/06/2019, Published 01/08/2019

Plant species recognition is performed using a dual deep learning architecture (DDLA) approach. DDLA consists of MobileNet and DenseNet-121 architectures. The feature vectors obtained from individual architectures are concatenated to form a final feature vector. The extracted features are then classified using machine learning (ML) classifiers such as linear discriminant analysis, multinomial logistic regression (LR), Naive Bayes, classification and regression tree, k-nearest neighbour, random forest classifier, bagging classifier and multi-layer perceptron. The dataset considered in the studies is standard (Flavia, Folio, and Swedish Leaf) and custom collected (Leaf-12) dataset. The MobileNet and DenseNet-121 architectures are also used as a feature extractor and a classifier. It is observed that the DDLA architecture with LR classifier produced the highest accuracies of 98.71, 96.38, 99.41, and 99.39% for Flavia, Folio, Swedish leaf, and Leaf-12 datasets. The observed accuracy for DDLA + LR is higher compared with other approaches (DDLA + ML classifiers, MobileNet + ML classifiers, DenseNet-121 + ML classifiers, MobileNet + fully connected layer (FCL), DenseNet-121 + FCL). It is also observed that the DDLA architecture with LR classifier achieves higher accuracy in comparable computation time with other approaches.

Inspec keywords: botany; regression analysis; multilayer perceptrons; biology computing; nearest neighbour methods; Bayes methods; random forests; image classification; feature extraction

Other keywords: bagging classifier; naive Bayes; Leaf-12 datasets; linear discriminant analysis; feature extractor; DDLA architecture; k-nearest neighbour; plant species recognition; plant species classification; Swedish leaf; multinomial logistic regression; machine learning classifiers; dual deep learning architecture approach; LR classifier; feature extraction; regression tree; feature vectors; ML classifiers; MobileNet; random forest classifier; DenseNet-121 architectures; fully connected layer; multilayer perceptron

Subjects: Neural computing techniques; Other topics in statistics; Image recognition; Computer vision and image processing techniques; Other topics in statistics; Biology and medical computing; Knowledge engineering techniques

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