TreeLogy
Leaf-Based Tree Identification System
“Treelogy” is a mobile application, which can perform leaf-based tree identification amongtree species in Turkey using one picture of a given leaf.There is only handful of applications interested in tree identification and they are designedprimarily for detecting North American and European tree species. There is no applicationwhich has a good performance and localization support for identifying tree species native toTurkey. 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 NeuralNetworks) and Support Vector Machines. Tree identification process uses leaf image featuresgathered from Caffe, a convolutional neural network framework, and our image processingmodule.After several experiments, we reached the optimal classification accuracy of 93.59% for 57tree species. Experiments involve 16096 training and 3020 testing leaf images. According toour findings, we come to the following conclusion. Certain image processing procedures forextracting 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"Server update, bug fix
“Treelogy” is a mobile application, which can perform leaf-based tree identification amongtree species in Turkey using one picture of a given leaf.There is only handful of applications interested in tree identification and they are designedprimarily for detecting North American and European tree species. There is no applicationwhich has a good performance and localization support for identifying tree species native toTurkey. 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 NeuralNetworks) and Support Vector Machines. Tree identification process uses leaf image featuresgathered from Caffe, a convolutional neural network framework, and our image processingmodule.After several experiments, we reached the optimal classification accuracy of 93.59% for 57tree species. Experiments involve 16096 training and 3020 testing leaf images. According toour findings, we come to the following conclusion. Certain image processing procedures forextracting 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"Server update, bug fix
Read trusted reviews from application customers
It never id tree
It doesn't give images of all leaves
I dont like srry for the people that are looking at this text.
Even unable to identify mango leaves.
I tried several times with different pictures and it never loaded any info
happy
Got every sample leaf wrong.
It doesn't do what it claims
Tried 5 traditional leaves found in Minnesota and it was 1 for 5 only gettin a maple. Even missed a But Oak.
Just says "quarrying" doesn't do anything.