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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 Silva
Automatic 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.


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Kelaniya, Sri Lanka


University of Kelaniya

Place of Publication

Kelaniya, Sri Lanka

Peer Reviewed

  • Yes

Open Access

  • No

External Author Affiliations

Sabaragamuwa University of Sri Lanka

Era Eligible

  • Yes

Name of Conference

International Research Conference on Smart Computing and Systems Engineering (SCSE 2018)