A near real-time deep learning approach for detecting rice phenology based on UAV images-Reference-Cited by-同舟云学术

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A near real-time deep learning approach for detecting rice phenology based on UAV images

Published:2020-06 Issue: Volume:287 Page:107938
ISSN:0168-1923
Container-title:Agricultural and Forest Meteorology
language:en
Short-container-title:Agricultural and Forest Meteorology

Author:

Yang Qi,Shi Liangsheng,Han Jingye,Yu Jin,Huang Kai

Funder

N

ational Natural Science Foundation of China

Key Research and Development Program in Guangxi

Publisher

Elsevier BV

Subject

Atmospheric Science,Agronomy and Crop Science,Global and Planetary Change,Forestry

Reference76 articles.

1. Field high-throughput phenotyping: the new crop breeding frontier;Araus;Trends Plant Sci.,2014

2. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging;Bendig;Remote Sens.,2014

3. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics;Bolton;Agric. For. Meteorol.,2013

4. Multi-year monitoring of rice crop phenology through time series analysis of MODIS images;Boschetti;Int. J. Remote Sens.,2009

5. ORYZA2000: Modeling Lowland Rice;Bouman,2001

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