Title: Recognition of flowers using convolutional neural networks

Authors: Abdulrahman Alkhonin; Abdulelah Almutairi; Abdulmajeed Alburaidi; Abdul Khader Jilani Saudagar

Addresses: Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia ' Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia ' Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia ' Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia

Abstract: Every human has curiosity about what's around them. Most of the people love the nature and visits different places like parks, flower shows etc., with family and children during free time. But due to lack of enough knowledge and information it is very difficult to decide which flowers are beneficial, non-poisonous and edible to mankind. To solve this problem, this work developed a mobile application which capture flower images and helps in recognising the flowers and categorise them into different categories using deep learning algorithms. This work uses a dataset which contains four different flowers (Sunflower, Dandelion, Rose, and Tulip) for training purpose and tested with a sample of flowers over the trained model. The percentage of overall accuracy achieved in recognition of flowers is approximately 83.13%.

Keywords: flower recognition; deep learning; keras; mobile application; TensorFlow; convolutional neural networks; accuracy; scalability; computer vision; image processing.

DOI: 10.1504/IJIEI.2020.111246

International Journal of Intelligent Engineering Informatics, 2020 Vol.8 No.3, pp.186 - 197

Received: 18 Mar 2020
Accepted: 01 Jun 2020

Published online: 03 Nov 2020 *

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