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

Shubham Mahajan,Amit Kant Pandit,Akshay Raina,Xiao-Zhi Gao

doi:10.3390/sym13020356

Shubham Mahajan, Amit Kant Pandit + Show 2 more

Open Access

https://doi.org/10.3390/sym13020356

Copy DOI

Abstract

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%.

Highlights

There are potentially hundreds of thousands of species of plants that exist on earth presently, out of which a large number contribute to medicinal use to human beings, while others are poisonous
We presented an efficient and robust plant species classification model using features extracted from leaves, transfer learning, and adaptive boosting
We experimented with the model for realizing the effects to final results with the presence and absence of some architectural highlights, such as AdaBoost and transfer learning, to provide supplementary evidence of the right choices for these decisions, thereby making this proposal a novel study

Summary

Introduction

There are potentially hundreds of thousands of species of plants that exist on earth presently, out of which a large number contribute to medicinal use to human beings, while others are poisonous. There are many other uses in this regard as well. This corresponds to the necessity of recognizing such species using the resources available. This task of identification and classification can be solved in most promising way using various tools in the domain of artificial intelligence (AI). There are a number of methods in ML and deep learning that can be implemented to address this task, such as regression-based models as supervised machine learning models, or computer vision models using convolution neural networks. The authors clearly accomplished proving the necessity as well as effective implementation of AI-based

Methods

Results

Conclusion