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METHODS article

Front. Plant Sci.
Sec. Plant Bioinformatics
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1395558

Deep Learning-Based Elaiosome Detection in Milk Thistle Seed for Efficient High-Throughput Phenotyping

Provisionally accepted
Younguk Kim Younguk Kim 1Alebel Mekuriaw Abebe Alebel Mekuriaw Abebe 1Jaeyoung Kim Jaeyoung Kim 2Suyoung Hong Suyoung Hong 3Kwanghoon An Kwanghoon An 4Jeehyoung Shim Jeehyoung Shim 4Jeongho BAEK Jeongho BAEK 2*
  • 1 Gene Engineering Division, National Institute of Agricultural Science, Rural Development Administration, Republic of Korea, Jeonju 54874, Republic of Korea
  • 2 Gene Engineering Division, National Institute of Agricultural Science, Rural Development Administration, Jeonju 54874, Republic of Korea
  • 3 Genomics Division, National Institute of Agricultural Science, Rural Development Administration, Jeonju 54874, Republic of Korea
  • 4 EL&I Co. Ltd., 17, Jangdoek-ri, Namyang-eup, Gyeonggi-do, Hwaseong 18281, Republic of Korea

The final, formatted version of the article will be published soon.

    Milk thistle (Silybum marianum (L.) is a well-known medicinal plant used for the treatment of liver disease due to a high content of silymarin. The seeds contain elaiosome, a fleshy structure attached to the seeds which is believed to be a rich source of many metabolites including silymarin. Segmentation of elaiosome by using only image analysis is difficult and this makes it impossible to quantify the elaiosome phenotypes. This study proposes a new approach for semi-automated detection and segmentation of elaiosomes in milk thistle seed using Detectron2 deep learning algorithm. One hundred manually labeled images were used to train the initial elaiosome detection model. This model was used to predict elaiosome from new datasets and the precise predictions were manually selected and used as new labeled images for re-training the model. Such semi-automatic image labeling, i.e., using the prediction results of the previous stage for re-training the model allowed the production of sufficient labeled data for re-training. Finally, a total of 6,000 labeled images were used to train Detectron2 for elaiosome detection and attained a promising result. The results demonstrate the effectiveness of Detectron2 in detecting milk thistle seed elaiosomes with an accuracy of 99.9%. The proposed method automatically detects and segments elaiosome from the milk thistle seed. The predicted mask images of elaiosome were used to analyze its area as one of the seed phenotypic traits along with other seed morphological traits by image based high-throughput phenotyping in imageJ. Enabling high-throughput phenotyping of elaiosome and other seed morphological traits will be useful for breeding milk thistle cultivars with desirable traits.

    Keywords: Milk Thistle, Elaiosome, deep learning, object detection, Detectron2, phenotyping

    Received: 04 Mar 2024; Accepted: 04 Jul 2024.

    Copyright: © 2024 Kim, Abebe, Kim, Hong, An, Shim and BAEK. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jeongho BAEK, Gene Engineering Division, National Institute of Agricultural Science, Rural Development Administration, Jeonju 54874, Republic of Korea

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.