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
Tags:
Machine Learning Agriculture Computer Vision
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