Plant leaf classification using dual path convolutional neural networks

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

Taxon identification is highly needed for a wide variety of research including ecology, agronomy and medicine. As of 1970, classification of plants was introduced into computer vision techniques. Most research conducted in this area focuses on leaves due to their availability as well as their ability to discretize. The most common features researchers base their work on are shape, textureand venation. This research study proposes a dual path, dual feature model for plant leaf identification. We weigh our research on shape and venation features. Sobel operators are used for primary and secondary vein extraction for vein patches generation. Then, a dual path convolutional neural network is employed for feature extraction. This architecture encloses two paths, the first for shape feature extraction and the second for venation feature extraction. The experiment was tested on the Flavia dataset and the results showed an accuracy of 96.8 %.

روابط وملفات

التعريف والنوع

رقم الوثيقة

fd60fbb3-0940-445d-ab81-b0e19abd7893

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

2294431_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":"Khaldoun Al Khalidi ","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Sara Rizk ","title_ar":"اعداد","title_en":"Preparation"}]

اللغة

English

المصدر والدورية

اسم المصدر

Plant leaf classification using dual path convolutional neural networks

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Leaves--Variation Neural networks (Computer science) Neural computers

إشراف وإعداد

الإشراف

Khaldoun Al Khalidi

الإعداد

Sara Rizk

الاقتباسات الببليوغرافية