Robustness of the RGB image-based estimation for rice above-ground biomass by utilizing the dataset collected across multiple locations

Robustness of the RGB image-based estimation for rice above-ground biomass by utilizing the dataset collected across multiple locations

Above-ground biomass (AGB) is a critical phenotype representing crop growth. Non-invasive evaluations of AGB, including deep-learning-based red-green-blue (RGB) image analyses, are often specific to the training data. The robustness of the estimation model across untrained conditions is essential to...

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Bibliographic Details
Main Authors: Kota Nakajima, Kazuki Saito, Yasuhiro Tsujimoto, Toshiyuki Takai, Atsushi Mochizuki, Tomoaki Yamaguchi, Ali Ibrahim, Salifou Goube Mairoua, Bruce Haja Andrianary, Keisuke Katsura, Yu Tanaka
Format: Article
Language:English
Published: Elsevier 2025-08-01
Series:Smart Agricultural Technology
Subjects:
Robustness
RGB image
Rice, Above-ground biomass
Convolutional neural network
Online Access:http://www.sciencedirect.com/science/article/pii/S277237552500231X
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