A novel neural network model to achieve generality for diverse morphologies and crop science interpretability in rice biomass estimation-Reference-Cited by-同舟云学术

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A novel neural network model to achieve generality for diverse morphologies and crop science interpretability in rice biomass estimation

Published:2024-03 Issue: Volume:218 Page:108653
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Yamaguchi Tomoaki,Katsura Keisuke

Funder

Science and Technology Research Partnership for Sustainable Development

Japan Society for the Promotion of Science

Tokyo University of Agriculture and Technology

Japan Science and Technology Agency

Japan International Cooperation Agency

Publisher

Elsevier BV

Reference61 articles.

1. Exploiting centimetre resolution of drone-mounted sensors for estimating mid-late season above ground biomass in rice;Adeluyi;Eur. J. Agron.,2022

2. Permutation importance: a corrected feature importance measure;Altmann;Bioinformatics,2010

3. Crop height monitoring using a consumer-grade camera and UAV technology;Belton;PFG – J. Photogramm. Remote Sens. Geoinformation Sci.,2019

4. Basmati rice: a review;Bhattacharjee;Int. J. Food Sci. Technol.,2002

5. A survey on multi-output regression;Borchani;Wires Data Min. Knowl. Discov.,2015

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