Plant Leaf Classification using Convolutional Neural Network

Abstract

Introduction: Convolutional Neural Network (CNNet) has proven the indispensable system in order to perform the recognition and classification tasks in different computer vision applications. The purpose of this study is to exploit the marvelous learning ability of CNNet in the image classification field. Method: In order to circumvent the overfitting issues and to enhance the generalization potential of the proposed FLCNNet, augmentation has been performed on the Flavia dataset that impose translation and rotation techniques to perform the augmentation with the transformed leaves having the same labels as the original ones. Both the classification models executed using; one without augmentation and one with the augmentation data are compared to check the effectiveness of the augmentation hence the aim of the proposed work. Moreover, Edge detection technique has been applied to extract the shape of the leaf images, in order to classify them accordingly. Thereafter, the FLCNNet is trained and tested for the dataset, with and without augmentation. Results: The results are gathered in terms of accuracy and training time for both datasets. The Augmented dataset (dataset 2) has been found effective and more feasible for classification without misguiding the network to learn (avoid overfitting) as compared to the dataset without augmentation (dataset 1). Conclusion: This paper proposed the Five Layer Convolution Neural Network (FLCNNet) method to classify plant leaves based on their shape. This approach can classify 8 types of leaves using automatic feature extraction, by utilizing their shape characteristics. To avoid the overfitting condition and make the performance better. We aimed to perform the classification of the augmented leaf dataset. Discussion: We proposed a five Layer CNNet (FLCNNet) to classify the leaf image data into different classes or labels based on the shape characteristics of the leaves.