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Gender recognition of Luffa flowers using machine learning
conference contribution
posted on 2019-01-23, 00:00 authored by HN Gunasinghe, Rohan De SilvaAutomatic flower gender identification could be introduced to large farmlands to help artificial pollination of imperfect flowers. Incomplete flowers contain either male or female organs but not both. In this paper, we present a computer aided system based on image processing and machine learning to identify the gender of a Luffa flower automatically. A pre-trained machine learning model is used for gender segmentation of flowers. The system is developed using Tensorflow Machine Learning Tool, which is an open-source software library for Machine Intelligence. The network was selected as the Google’s Inception model and a dataset was prepared after capturing flower images from a Sri Lankan Luffa farm. The system was tested using two datasets. The first contained the captured original images and the second was prepared by cropping each image to extract male and female floral organs, stamen and pistil respectively. The prototype system classified the flowers as either male or female at 95% accuracy level. The experimental results indicate that the proposed approach can significantly support an accurate identification of the gender of a Luffa flower with some computational effort.
History
Start Page
94End Page
97Number of Pages
4Start Date
2018-03-29Finish Date
2018-03-29ISSN
2613-8662Location
Kelaniya, Sri LankaPublisher
University of KelaniyaPlace of Publication
Kelaniya, Sri LankaFull Text URL
Peer Reviewed
- Yes
Open Access
- No
External Author Affiliations
Sabaragamuwa University of Sri LankaEra Eligible
- Yes