Machine Learning Based Flower Recognition System
Utkarsh Tiwari1, Rohit Kumar Singh2, Rohan Vijay Wargia3, P Uttareshwar Vikashrao4
1Utkarsh Tiwari, REVA University, India.
2Rohit Kumar Singh, REVA University, India.
3Rohan Vijay Wargia, REVA University, India.
4P Uttareshwar Vikashrao, REVA University, India.
Manuscript received on 05 June 2019 | Revised Manuscript received on 14 June 2019 | Manuscript Published on 29 June 2019 | PP: 165-169 | Volume-8 Issue-5S, May 2019 | Retrieval Number: E10350585S19/19©BEIESP
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Abstract: Automatic flower plucking systems for smart agriculture are being studied for many years to support flower harvesting. Such systems require flower recognition task to be integrated as part of the system. This paper presents an approach for classification of flowers using a machine learning algorithm. The method categorizes flowers into different species with the help of convolutional neural networks and deep learning techniques. The system uses a pre-trained CNN model to improve the accuracy rate. Concepts such as Feedforward, back-propagation and transfer learning are used to create the neural network model. Different hyper-parameter values have been tested on the model which provides maximum accuracy of 85.0 percentage on the testing dataset. The result is visualized in the form of bar-plots which provides the top 5 predictions of flower species for the given input image of a flower.
Keywords: Image Recognition, Machine learning, CNN, Feedforward.
Scope of the Article: Machine Learning