NDLI: DDLA: dual deep learning architecture for classification of plant species

Content Provider	IET Digital Library
Author	Raj, Anubha Pearline Sundara Sobitha Vajravelu, Sathiesh Kumar
Abstract	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.
Starting Page	2176
Ending Page	2182
Page Count	7
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 12, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0346
Journal	IET Image Processing
Publisher Date	2019-08-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bagging Classifier Bayes Method Biology And Medical Computing Biology Computing Botany Computer Vision And Image Processing Technique DDLA Architecture DenseNet-121 Architectures Dual Deep Learning Architecture Approach Feature Extraction Feature Extractor Feature Vector Fully Connected Layer Image Classification Image Recognition K-nearest Neighbour Knowledge Engineering Technique Leaf-12 Datasets Linear Discriminant Analysis LR Classifier Machine Learning Classifier ML Classifier MobileNet Multilayer Perceptron Multilayer Perceptrons Multinomial Logistic Regression Naive Bayes Nearest Neighbour Method Neural Computing Technique Plant Species Classification Plant Species Recognition Random Forest Random Forest Classifier Regression Analysis Regression Tree Statistics Swedish Leaf
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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