EmergeNet: A novel deep-learning based ensemble segmentation model for emergence timing detection of coleoptile

Frontiers in Plant Science

Plant Science

Amit Kumar Das¹  Aankit Das²  Ashok Samal³  Sruti Das Choudhury⁴  Tala Awada⁵ 

[1] Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, West Bengal, India;Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, West Bengal, India;Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, West Bengal, India;School of Computing, University of Nebraska-Lincoln, Lincoln, NE, United States;School of Computing, University of Nebraska-Lincoln, Lincoln, NE, United States;School of Natural Resources University of Nebraska-Lincoln, Lincoln, NE, United States;School of Natural Resources University of Nebraska-Lincoln, Lincoln, NE, United States;Agricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE, United States;

关键词: event-based plant phenotyping;    deep-learning;    ensemble segmentation;    emergence time detection;    benchmark dataset;   

DOI  :  10.3389/fpls.2023.1084778

 received in 2022-10-30, accepted in 2023-01-11,  发布年份 2023

来源: Frontiers

【 摘 要 】

The emergence timing of a plant, i.e., the time at which the plant is first visible from the surface of the soil, is an important phenotypic event and is an indicator of the successful establishment and growth of a plant. The paper introduces a novel deep-learning based model called EmergeNet with a customized loss function that adapts to plant growth for coleoptile (a rigid plant tissue that encloses the first leaves of a seedling) emergence timing detection. It can also track its growth from a time-lapse sequence of images with cluttered backgrounds and extreme variations in illumination. EmergeNet is a novel ensemble segmentation model that integrates three different but promising networks, namely, SEResNet, InceptionV3, and VGG19, in the encoder part of its base model, which is the UNet model. EmergeNet can correctly detect the coleoptile at its first emergence when it is tiny and therefore barely visible on the soil surface. The performance of EmergeNet is evaluated using a benchmark dataset called the University of Nebraska-Lincoln Maize Emergence Dataset (UNL-MED). It contains top-view time-lapse images of maize coleoptiles starting before the occurrence of their emergence and continuing until they are about one inch tall. EmergeNet detects the emergence timing with 100% accuracy compared with human-annotated ground-truth. Furthermore, it significantly outperforms UNet by generating very high-quality segmented masks of the coleoptiles in both natural light and dark environmental conditions.

【 授权许可】

Unknown   
Copyright © 2023 Das, Das Choudhury, Das, Samal and Awada