Classifying 3 Moss Species by Deep Learning, Using the “Chopped Picture” Method

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Open Journal of Ecology

ISSN Print: 2162-1985
ISSN Online: 2162-1993
www.scirp.org/journal/oje
E-mail: oje@scirp.org

Google-based Impact Factor: 1.38
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"Classifying 3 Moss Species by Deep Learning, Using the “Chopped Picture” Method"
written by Takeshi Ise, Mari Minagawa, Masanori Onishi,
published by Open Journal of Ecology, Vol.8 No.3, 2018

has been cited by the following article(s):

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[18]	How Useful Is Image-Based Active Learning for Plant Organ Segmentation? Plant Phenomics, 2022 DOI:10.34133/2022/9795275

[19]	Classifying Individual Shrub Species in UAV Images—A Case Study of the Gobi Region of Northwest China Remote Sensing, 2021 DOI:10.3390/rs13244995

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[21]	One-shot learning with triplet loss for vegetation classification tasks Computer Optics, 2021 DOI:10.18287/2412-6179-CO-856

[22]	Classifying Individual Shrub Species in UAV Images—A Case Study of the Gobi Region of Northwest China Remote Sensing, 2021 DOI:10.3390/rs13244995

[23]	Explainable identification and mapping of trees using UAV RGB image and deep learning Scientific Reports, 2021 DOI:10.1038/s41598-020-79653-9

[24]	VARENN: graphical representation of periodic data and application to climate studies npj Climate and Atmospheric Science, 2020 DOI:10.1038/s41612-020-0129-x

[25]	VARENN: graphical representation of periodic data and application to climate studies npj Climate and Atmospheric Science, 2020 DOI:10.1038/s41612-020-0129-x

[26]	Identifying the vegetation type in Google Earth images using a convolutional neural network: a case study for Japanese bamboo forests BMC Ecology, 2020 DOI:10.1186/s12898-020-00331-5

[27]	Forecasting Climatic Trends Using Neural Networks: An Experimental Study Using Global Historical Data Frontiers in Robotics and AI, 2019 DOI:10.3389/frobt.2019.00032

[28]	Automatic estimation of heading date of paddy rice using deep learning Plant Methods, 2019 DOI:10.1186/s13007-019-0457-1

[29]	Recent literature on bryophytes — 121(4) The Bryologist, 2018 DOI:10.1639/0007-2745-121.4.589

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