Simultaneous prediction of wheat yield and grain protein content using multitask deep learning from time-series proximal sensing
Abstract
This paper presents a multitask deep learning approach for simultaneous prediction of wheat yield and grain protein content using time-series proximal sensing data.
Citation
@article{sun2022simultaneous,
title={Simultaneous prediction of wheat yield and grain protein content using multitask deep learning from time-series proximal sensing},
author={Sun, Zhuangzhuang and Li, Qing and Jin, Shichao and Song, Yunlin and Xu, Shan and Wang, Xiao and Cai, Jian and Zhou, Qin and Ge, Yan and Zhang, Ruinan and others},
journal={Plant Phenomics},
year={2022},
publisher={AAAS}
}
- Posted on:
- January 1, 2022
- Length:
- 1 minute read, 79 words
- See Also:
- PhenoSR: Enhancing organ-level phenotyping with super-resolution RGB UAV imagery for large-scale field experiments
- SenNet: A dual-branch image semantic segmentation network for wheat senescence evaluation and high-yielding variety screening
- OSNet: an oriented instance segmentation network of breeding plot extraction from UAV RGB imagery