Abstract:
"
Image classification and identification have become significant use cases in most of the
domains. When it comes to agriculture, it is a preferred method to analyze the quality of the
crops. There are several studies on image classification to identify rice species around the
world. However, it cannot use image datasets from other countries to perform classifications
in the Sri Lankan context with contextual bottlenecks. As well as Sri Lanka has more than 3000
traditional rice varieties, and some are indigenous varieties. This study on two main things with
these concerns. The first one is developing an image dataset of Sri Lankan traditional rice
species. Also, under the dataset development, the research proposes methodologies for
performing image acquisition, segmentation, and background removal of the images. The
study’s initial version focuses on developing a dataset of six Sri Lankan traditional rice
varieties with five thousand seed images for each rice variety.
The second part is to develop a convolutional neural network image classification model to
classify the Sri Lankan traditional rice varieties. The developed neural network model is
embedded into a back-end application, and it is exposed as an API to perform the rice
classification. As the final part, the research focuses on a front-end mobile application to work
with the back-end application to perforce a rice identification and rice sample analysis. Finally,
the research is a complete solution for rice image identification using a mobile application.
"
