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Title: Biomass estimation of World rice (Oryza sativa L.) core collection based on the convolutional neural network and digital images of canopy
Authors: Nakajima, Kota
Tanaka, Yu
Katsura, Keisuke
Yamaguchi, Tomoaki
Watanabe, Tomoya
Shiraiwa, Tatsuhiko  kyouindb  KAKEN_id
Author's alias: 中嶌, 洸太
田中, 佑
白岩, 立彦
Keywords: Above-ground biomass
Biomass estimation
Convolutional neural network
Digital image
Rice
World rice core collection
Issue Date: May-2023
Publisher: Taylor & Francis Group
Journal title: Plant Production Science
Volume: 26
Issue: 2
Start page: 187
End page: 196
Abstract: Above-ground biomass (AGB) is an important indicator of crop productivity. Destructive measurements of AGB incur huge costs, and most non-destructive estimations cannot be applied to diverse cultivars having different canopy architectures. This insufficient access to AGB data has potentially limited improvements in crop productivity. Recently, a deep learning technique called convolutional neural network (CNN) has been applied to estimate crop AGB due to its high capacity for digital image recognition. However, the versatility of the CNN-based AGB estimation for diverse cultivars is still unclear. We established and evaluated a CNN-based estimation method for rice AGB using digital images with 59 diverse cultivars which were mostly in World Rice Core Collection. Across two years at two locations, we took 12, 183 images of 59 cultivars with commercial digital cameras and manually obtained their corresponding AGB. The CNN model was established by using 28 cultivars and showed high accuracy (R2 = 0.95) to the test dataset. We further evaluated the performance of the CNN model by using 31 cultivars, which were not in the model establishment. The CNN model successfully estimated AGB when the observed AGB was lesser than 924 g m−2 (R2 = 0.87), whereas it underestimated AGB when the observed AGB was greater than 924 g m−2 (R2 = 0.02). This underestimation might be improved by adding training data with a greater AGB in further study. The present study indicates that this CNN-based estimation method is highly versatile and could be a practical tool for monitoring crop AGB in diverse cultivars.
Rights: © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
URI: http://hdl.handle.net/2433/286020
DOI(Published Version): 10.1080/1343943X.2023.2210767
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