Classification of Plant Species with Compound and Simple Leaves Using CNN Fusion Networks

Abstract

An automatic plant species identification system could help to identify plant species very easily. Deep learning is an AI function which works like the human brain, with artificial neural networks. In neural networks, neuron nodes are connected like a web and used to extract higher-level features from input. Convolutional neural network (CNN), deep belief network (DBF) and recurrent neural network (RNN) etc. are deep learning networks and can extract more detailed information compared to conventional machine learning techniques [1, 10]. CNN is a very good choice for image processing, and it can work with large datasets efficiently. In our project VGG16 CNN is used to extract the features from leaf images of a simple and compound leaf. For the identification of plant species with simple and compound leaves with real complex background images, this paper proposes a fusion CNN model using original whole leaf images and patch images. A transfer learned VGG16 CNN was used for feature extraction and classification of real complex background images [1]. Feature extraction and classification are done with original (model1) and patch (model2) images separately with VGG16 CNN models. The feature maps from intermediate levels of model1 and model2 are taken, then concatenated and classified using SVM and KNN. The CNN-SVM model has the best performance over model1, model2 and CNN-KNN model. The proposed fusion model shows the efficiency of 98.6% accuracy in model evaluation and 90% accuracy in plant identification using complex background leaf images.