Plant Recognition Using Morphological Feature Extraction and Transfer Learning over SVM and AdaBoost

Plant species recognition from visual data has always been a challenging task for Artificial Intelligence (AI) researchers, due to a number of complications in the task, such as the enormous data to be processed due to vast number of floral species. There are many sources from a plant that can be us...

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Bibliographic Details
Published in:Symmetry (Basel) 2021-02, Vol.13 (2), p.356
Main Authors: Mahajan, Shubham, Raina, Akshay, Gao, Xiao-Zhi, Kant Pandit, Amit
Format: Article
Language:English
Subjects:
Accuracy
AdaBoost
Algorithms
Artificial intelligence
Centroids
Classification
Classifiers
Datasets
Decision trees
Digital imaging
Feature extraction
Feature recognition
feature selection
Flowers
Flowers & plants
Machine learning
Mathematical functions
Morphology
Multilayer perceptrons
Neural networks
non-separable data
Object recognition
plant species recognition
Support vector machines
SVM
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Summary:Plant species recognition from visual data has always been a challenging task for Artificial Intelligence (AI) researchers, due to a number of complications in the task, such as the enormous data to be processed due to vast number of floral species. There are many sources from a plant that can be used as feature aspects for an AI-based model, but features related to parts like leaves are considered as more significant for the task, primarily due to easy accessibility, than other parts like flowers, stems, etc. With this notion, we propose a plant species recognition model based on morphological features extracted from corresponding leaves’ images using the support vector machine (SVM) with adaptive boosting technique. This proposed framework includes the pre-processing, extraction of features and classification into one of the species. Various morphological features like centroid, major axis length, minor axis length, solidity, perimeter, and orientation are extracted from the digital images of various categories of leaves. In addition to this, transfer learning, as suggested by some previous studies, has also been used in the feature extraction process. Various classifiers like the kNN, decision trees, and multilayer perceptron (with and without AdaBoost) are employed on the opensource dataset, FLAVIA, to certify our study in its robustness, in contrast to other classifier frameworks. With this, our study also signifies the additional advantage of 10-fold cross validation over other dataset partitioning strategies, thereby achieving a precision rate of 95.85%.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym13020356