Comparison of Different Machine Learning Algorithms for the Prediction of the Wheat Grain Filling Stage Using RGB Images
Abstract
This paper presents a comparison of different machine learning algorithms for the prediction of the wheat grain filling stage using RGB images.
Citation
@article{song2023comparison,
title={Comparison of Different Machine Learning Algorithms for the Prediction of the Wheat Grain Filling Stage Using RGB Images},
author={Song, Yunlin and Sun, Zhuangzhuang and Zhang, Ruinan and Min, Haijiang and Li, Qing and Cai, Jian and Wang, Xiao and Zhou, Qin and Jiang, Dong},
journal={Plants},
volume={12},
number={23},
pages={4043},
year={2023},
publisher={MDPI}
}
- Posted on:
- January 1, 2023
- Length:
- 1 minute read, 76 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