Mid-season prediction of grain yield and protein content of spring barley cultivars using high-throughput spectral sensing

By Gero Barmeier, K. Hofer, Urs Schmidhalter. 2017

53 citations recorded by the source corpus.

Subfields: Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/870cef12c770a172e432245480ecd0c779521d23

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. Tao Yang
3. Pablo J. Zarco-Tejada
4. Bo Ming
5. Rajeev K. Varshney
6. Chenwei Nie
7. Urs Schmidhalter
8. Shaokun Li

In Plant Science

1. Pablo J. Zarco-Tejada
2. Rajeev K. Varshney
3. Chenwei Nie
4. Urs Schmidhalter
5. Matthew P Reynolds
6. Zhenhai Li
7. Yonggui Xiao
8. Malcolm J. Hawkesford

Citations in the corpus

• High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, background): “For example, multi/hyperspectral cameras can be used to detect crop water content [116], [266] and grain quality [64], [274] through spectral characteristics and machine learning methods, with the exception of the same key dynamic monitoring traits from an RGB camera.”