A Deep Learning-Based Recognition Technique for Plant Leaf Classification

Paul Shekonya Kanda,Olanrewaju Hazzan Sanusi,Kewen Xia

doi:10.1109/access.2021.3131726

Paul Shekonya Kanda, Olanrewaju Hazzan Sanusi + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3131726

Copy DOI

Abstract

In the practice of plant classification, the design of hand-crafted features is more dependent on the ability of computer vision experts to encode morphological characters that are predefined by botanists. However, the distinct features that each plant has as demonstrated by its leaves can be automatically learned based on the end-to-end advantage of Deep Learning algorithms. Therefore, Deep Learning based plant leaf recognition methods is an important approach nowadays. In this article, we are applying three technologies to achieve a model with high accuracy for plant classification. A Conditional Generative Adversarial Network was used to generate synthetic data, a Convolutional Neural Network was used for feature extraction and the rich extracted features were fed into a Logistic Regression classifier for efficient classification of the plant species. The effectiveness of this method can be seen in the wealth of plant datasets that it was tested on. The paper contains results on seven datasets with different modalities. We utilized both Deep Learning and Logistic regression in effectively classifying the plants using their leaf images with accuracies averaging 96.1% for about eight datasets used, but greater for the individual datasets from 99.0 to 100% on some individual datasets. Extensive experiments on each of the datasets demonstrate the superiority of our method compared with others and are highlighted in our results.

Highlights

Owing to global warming and other factors, many plants nowadays are faced with the challenge of extinction
This paper proposes a method based on an ensemble of some Deep Learning techniques for efficient Plant Classification applied on plant leaf images
The utilization of a conditional Generative Adversarial Network to tackle the problem of a lack of sufficient training data or uneven class balance that could be found within datasets in performing Deep Learning tasks

Summary

Introduction

Owing to global warming and other factors, many plants nowadays are faced with the challenge of extinction. The utilization of a conditional Generative Adversarial Network to tackle the problem of a lack of sufficient training data or uneven class balance that could be found within datasets in performing Deep Learning tasks. This serves to augment leaf image datasets, which have not been large enough, as this field still lacks a large number of datasets for adequate training of Deep Neural Networks for better generalization. The result of the augmented leaf dataset produced more than 3.0% increase in accuracy This is a milestone in the field of Deep Learning

Methods

Results

Conclusion