Identification of Lantana Camara Distribution Using Convolutional Neural Networks

Lantana camera is an exotic invasive plant that has been a major threat to the biodiversity of several areas around the world. This paper presents a novel methodology to identify the distribution of Lantana camera flowers in aerial images. The proposed model uses aerial images as inputs and the model consist of three stages. The first step is the detection of possible flower patches in the aerial images using Local Binary Patterns mechanism. The second step is the recognition of Lantana camera flowers from the localized flower patches through a classification process using a Convolutional Neural Network (CNN). The third step is the marking the presence of the flowers of Lantana camera in the original image. Achieved sensitivity rate of this study is 40.71%. The proposed model succeeded in identifying Lantana camara distribution by identifying the presence of Lantana camara flowers in aerial images.