Classification of Leaf using Teachable Machine

Abstract

Abstract: Leaf classification is an important stage in plant science. Manual classification of leaf is a quite difficult task for unknown person. For identification and classification of leaf type, machine learning (ML) based approach is simplest way. Henceforth, Google Teachable Machine (GTM) is an intuitive visual tool that provides workflow-oriented support for the development of machine learning models. This model helps to scan image object for leaf type identification. ML is a web-based tool allow user to train machine-learning model without any coding language. In the present work, Mango leaf, Orange leaf and Guava leaf has been selected for identification of its type. For the classification of leaves, hundred images of each type has been taken from available data set and categorized into three classes by giving Guava leaf, Orange leaf and Mango leaf name to created three classes. Teachable machine provides image, sound and pose training option for classification of an object. In this work, we used image option to train the model. This platform automatically trains the model using pre-trained deep learning algorithm. Leaf type verification is done by uploading new image of each leaf type that was not included in the training data set. After successful completion of experiment, result shows classification of each leaf type accurately.