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http://dx.doi.org/10.9723/jksiis.2020.25.5.023

Deep Learning-based Rice Seed Segmentation for Phynotyping  

Jeong, Yu Seok (군산대학교 컴퓨터정보공학과)
Lee, Hong Ro (군산대학교 컴퓨터정보공학과)
Baek, Jeong Ho (농촌진흥청 국립농업과학원)
Kim, Kyung Hwan (농촌진흥청 국립농업과학원)
Chung, Young Suk (제주대학교 식물자원환경전공)
Lee, Chang Woo (군산대학교 컴퓨터정보공학과)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.25, no.5, 2020 , pp. 23-29 More about this Journal
Abstract
The National Institute of Agricultural Sciences of the Rural Developement Administration (NAS, RDA) is conducting various studies on various crops, such as monitoring the cultivation environment and analyzing harvested seeds for high-throughput phenotyping. In this paper, we propose a deep learning-based rice seed segmentation method to analyze the seeds of various crops owned by the NAS. Using Mask-RCNN deep learning model, we perform the rice seed segmentation from manually taken images under specific environment (constant lighting, white background) for analyzing the seed characteristics. For this purpose, we perform the parameter tuning process of the Mask-RCNN model. By the proposed method, the results of the test on seed object detection showed that the accuracy was 82% for rice stem image and 97% for rice grain image, respectively. As a future study, we are planning to researches of more reliable seeds extraction from cluttered seed images by a deep learning-based approach and selection of high-throughput phenotype through precise data analysis such as length, width, and thickness from the detected seed objects.
Keywords
Deep learning; Seed segmentation; Object detection; Phenotype;
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