深度学习允许使用地面RGB图像即时和多功能估计产量
Yu Tanaka1,2, Tomoya Watanabe3, Keisuke Katsura4
1Graduate School of Agriculture, Kyoto University, Kitashirakawa Oiwake-chou, Sakyo-ku, Kyoto 606-8502, Japan.
Plant phenomics (Washington, D.C.)
|January 19, 2024
概括
本研究介绍了一种使用RGB图像来估计大米产量的深度学习方法. 该方法准确地预测了产量变化,为高通量表型和作物生产评估提供了低成本的解决方案.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
背景情况:
- 米 (Oryza sativa L.) 是全球重要的粮食来源,但准确的产量评估,特别是在全球南方,仍然具有挑战性.
- 高通量表型化对于提高作物生产率和粮食安全至关重要.
研究的目的:
- 开发和验证一种基于深度学习的方法,用于使用红绿蓝 (RGB) 图像即时估计大米产量.
- 评估模型的稳定性和可扩展性,用于实际的农业应用.
主要方法:
- 一个卷积神经网络 (CNN) 在22000多张RGB图像上受过训练,这些图像来自非洲和日本的4820个大米田.
- 该模型的准确性在预测产量变化,基因型差异和农业干预措施的影响方面得到了评估.
主要成果:
- 美国有线电视新闻网模型解释了68%的收益率变化,相对根的平均平方误差为0.22.
- 该模型在拍摄角度,照明和图像分辨率的变化中表现出强度,即使在降低分辨率的情况下,也预测了57%的产量变化.
- 该方法成功地确定了基因型差异和农学实践对大米产量的影响.
结论:
- 应用到RGB图像的深度学习为高通量大米表型和产量估计提供了低成本,快速和可扩展的方法.
- 这项技术可以帮助评估提高生产率的干预措施,确定需要干预的地区,并在收获前几周预测产量.
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