Scopus Indexed Paper

Paper Details


Title
PataNET: A Convolutional Neural Networks to Identify Plant from Leaf Images
Abstract
Plants are everywhere around the Earth. Plant identification is a very important problem for environmental protection and exploration. But getting to know various plants requires taking into account a large number of features which might be easy for a botanist but not easy for ordinary people. So having an automatic Plant identifier which uses a leaf to identifying plants will help many people. Here, a method is proposed where Convolutional Neural Network (CNN) technique is used to classify plants using leaf images. Using Adam optimizer and automatic Learning Rate reduction technique the model gave promising accuracy. This system was trained on 3600 RGB leaf images of 2 categories for 6 different plant species. The model reported promising results with validation accuracy was 95.86% and training accuracy was 96.54%. Different pre-processing techniques such as background whitening, noise removal are used. In the convolutional networks activation function ReLU is used in the hidden layer and Softmax for output layer.
Keywords
Convolutional neural network, Leaf classification, Adam optimizer, automatic Learning Rate Reduction
Authors
Md. Majedul Islam, AKM Shahahriar Azad Rabby, Md. Hafizur Rahman Arfin, Syed Akhter Hossain
Phone
Journal or Conference Name
2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019
Publish Year
2019
Indexing
Scopus