Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices

By Meiling Liu, Xiangnan Liu, Mi Li, Meihong Fang, Wen-xue Chi. 2010

132 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/03ba111bdd472c7c517fd07698e545f7105d849b

How researchers see this paper

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

In Analytical Chemistry

1. Huanfeng Shen
2. Liangpei Zhang

In Ecology

1. Qiangqiang Yuan
2. Huanfeng Shen
3. Liangpei Zhang

In Plant Science

1. A. Akyüz
2. S. Boyacı
3. Hande Küçükönder

Citations in the corpus

• A modeling study with an artificial neural network: developing estimationmodels for the tomato plant leaf area (2016, result): “Similar results were reported in many ANN studies in the field of agriculture (Liu et al., 2010; Vazquez-Cruz et al., 2012; Khoshnevisan et al., 2014; Guine, 2015; Were et al., 2015).”
• Deep learning in environmental remote sensing: Achievements and challenges (2020, methodology): “Corsini et al. (2003) compared RBF NN and MLP networks for chlorophyll concentration estimation in case II waters using MERIS data.”