TreeLogy
Leaf-Based Tree Identification System
What's TreeLogy APK?
TreeLogy is a app for Android, It's developed by Duman Emre Akın author.
First released on google play in 8 years ago and latest version released in 6 years ago.
This app has 36.7K download times on Google play and rated as 2.74 stars with 125 rated times.
This product is an app in Education category. More infomartion of TreeLogy on google play
“Treelogy” is a mobile application, which can perform leaf-based tree identification among
tree species in Turkey using one picture of a given leaf.
There is only handful of applications interested in tree identification and they are designed
primarily for detecting North American and European tree species. There is no application
which has a good performance and localization support for identifying tree species native to
Turkey. This project is aimed to fill this gap.
While we are constructing this application, we worked on supervised learning for classi-
fication task. We focused on both Deep Learning (specifically Deep Convolutional Neural
Networks) and Support Vector Machines. Tree identification process uses leaf image features
gathered from Caffe, a convolutional neural network framework, and our image processing
module.
After several experiments, we reached the optimal classification accuracy of 93.59% for 57
tree species. Experiments involve 16096 training and 3020 testing leaf images. According to
our findings, we come to the following conclusion. Certain image processing procedures for
extracting features such as shape and texture descriptors, which we have used in our project,
does not produce features as feasible as convolutional neural networks.
"Created by group paY inekereG"
tree species in Turkey using one picture of a given leaf.
There is only handful of applications interested in tree identification and they are designed
primarily for detecting North American and European tree species. There is no application
which has a good performance and localization support for identifying tree species native to
Turkey. This project is aimed to fill this gap.
While we are constructing this application, we worked on supervised learning for classi-
fication task. We focused on both Deep Learning (specifically Deep Convolutional Neural
Networks) and Support Vector Machines. Tree identification process uses leaf image features
gathered from Caffe, a convolutional neural network framework, and our image processing
module.
After several experiments, we reached the optimal classification accuracy of 93.59% for 57
tree species. Experiments involve 16096 training and 3020 testing leaf images. According to
our findings, we come to the following conclusion. Certain image processing procedures for
extracting features such as shape and texture descriptors, which we have used in our project,
does not produce features as feasible as convolutional neural networks.
"Created by group paY inekereG"
