Classification of philodendron plant species based on leaf images using a CNN algorithm

Abstract

Accurate identification of ornamental plants is becoming important as public interest in tropical plant collections increases, one of which is from the Philodendron genus. This ornamental plant has many varieties that are often difficult to distinguish due to visual similarities in the shape and pattern of their leaves. This research aims to develop a system for Philodendron type classification based on leaf images using the Convolutional Neural Network (CNN) algorithm to help the identification process. The method used is with a dataset of 5000 leaf images of five Philodendron species, which are divided into 80% training data, 10% validation data, and 10% test data. A CNN model with MobileNetV2 FPNLite SSD architecture was implemented and trained for 50,000 steps, then optimised for mobile devices using TensorFlow Lite. Performance analysis was conducted using confusion matrix to evaluate accuracy, precision, recall, and F1-Score metrics. The results show that the developed model is able to accurately classify leaf images, both in the form of static images and in real-time. This system has been successfully implemented in an Android application that is expected to be a practical identification tool for general users and ornamental plant enthusiasts.