Going deeper on bioimages classification: A plant leaf dataset case study

Abstract

In this paper, we present and evaluate the accuracy of a Deep Convolutional Neural Network (DCNN) architecture, with other traditional methods, to solve a bioimage classification problem. The main contributions of this work are the application of a DCNN architecture and the further comparison of different types of classification and feature extraction techniques applied to a plant leaf image dataset. Furthermore, we go deeper on the analysis of a cross-domain transfer learning approach using a state-of-the-art deep neural network called Inception-v3. Our results show that we manage to classify a subset of 53 species of leafs with a notable mean accuracy of 98.2%.