Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery

By Xiang Zhou, Hengbiao Zheng, Xinjie Xu, Jiaoyang He, X. Ge, Xia Yao, T. Cheng, Yan Zhu, W. Cao, Yongchao Tian. 2017

563 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/4830bc502c4b84ecc9674a7ed95dac5b934b1eaa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Zhenhai Li
2. Guijun Yang
3. Xiuliang Jin

In Environmental Engineering

1. Guijun Yang
2. Raffaele Casa
3. Zhenhai Li
4. Xiuliang Jin

Citations in the corpus

• Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review (2020, methodology): “The authors in [91] utilized single state vegetation indices (VIs) and multi-temporal VIs to predict the grain yield based on multi-spectral and digital images obtained by UAV.”
• A hierarchical interannual wheat yield and grain protein prediction model using spectral vegetative indices and meteorological data (2020, background): “extrapolate these yield and/or GPC predictions over larger regions (Tucker et al., 1980; Wang et al., 2004; Liu et al., 2006; Xue et al., 2007; Pettersson and Eckersten, 2007; Ryu, et al., 2011; Zhou et al., 2017).”