Leaf classification by feature extraction using CNN

Abstract

Plants are an integral part of our nature. The identification and classification of plant leaves has always been a matter of interest for the botanists as well as the laymen. Classification of plant leaves will enable us to know the heritage and details of plants at a glance avoiding the duplication of popular names. This recognition system will be beneficial to different sectors of our society including botanic research, medical field, the study of plant taxonomy etc. As leaves carry a lot of information about plant species, extraction of feature is a better way to classify the leaves. In this paper, we have proposed Convolutional Neural Network (CNN) and analyzed plant leaves with different models. We have collected the dataset from Kaggle. By preprocessing the images and extracting the features we have trained our pre-trained model. In our research, we have chosen three models of CNN which are InceptionV3, VGG16 and MobileNet. MobileNet achieved the highest accuracy of 69.47% with a mean absolute error of 30.26, while VGG16 achieved the lowest accuracy of 57.05% with a mean absolute error of 42.95 and 66.13% accuracy for Inception V3.