TreeLogy
2.0star
127 جائزے
+10 ہزار
ڈاؤن لوڈز
ہر کوئی
info
اس ایپ کے بارے میں
“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 classification 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" => METU Students
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 classification 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" => METU Students
اپ ڈیٹ کردہ بتاریخ
سیفٹی اس بات کو سمجھنے کے ساتھ شروع ہوتی ہے کہ ڈویلپرز آپ کا ڈیٹا کیسے اکٹھا اور اس کا اشتراک کرتے ہیں۔ ڈیٹا کی رازداری اور سیکیورٹی کے طریقے آپ کے استعمال، علاقے اور عمر کی بنیاد پر مختلف ہو سکتے ہیں۔ ڈویلپر نے یہ معلومات فراہم کی ہے اور وقت کے ساتھ ساتھ اسے اپ ڈیٹ کر سکتا ہے۔
فریقین ثالث کے ساتھ کسی بھی ڈیٹا کا اشتراک نہیں کیا گیا
ڈویلپرز کے اشتراک کے اعلان کے بارے میں مزید جانیں
کوئی ڈیٹا اکٹھا نہیں کیا گیا
ڈویلپرز کے اکٹھا کرنے کے اعلان کے طریقے بارے میں مزید جانیں
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121 جائزے
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