Leaf Classification for Plant Recognition Using EfficientNet Architecture

Abstract

Automatic plant species classification has always been a great challenge. Classical machine learning methods have been used to classify leaves using handcrafted features from the morphology of plant leaves which has given promising results. However, we focus on using non-handcrafted features of plant leaves for classification. So, to achieve it, we utilize a deep learning approach for feature extraction and classification of features. Recently Deep Convolution Neural Networks have shown remarkable results in image classification and object detection-based problems. With the help of the transfer learning approach, we explore and compare a set of pre-trained networks and define the best classifier. That set consists of eleven different pre-trained networks loaded with ImageNet weights: AlexNet, EfficientNet BO to B7, ResNet50, and Xception. These models are trained on the plant leaf image data set, consisting of leaf images from eleven different unique plant species. It was found that EfficientNet-B5 performed better in classifying leaf images compared to other pre-trained models. Automatic plant species classification could be helpful for food engineers, people related to agriculture, researchers, and ordinary people.