分段任何东西对葡萄藤集群架构和果属性的全面分析
Efrain Torres-Lomas1, Jimena Lado-Bega2, Guillermo Garcia-Zamora1
1Department of Viticulture and Enology, University of California Davis, Davis, CA 95616, USA.
Plant phenomics (Washington, D.C.)
|June 28, 2024
概括
分段任何模型 (SAM) 准确地识别图像中的葡萄,使集群架构和紧性的详细分析能够改善葡萄种植实践.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 植物科学 植物科学
背景情况:
- 葡萄群架构和紧性对于产量和质量至关重要,但难以量化.
- 葡萄图像分析现有的计算机视觉方法往往需要广泛的培训,缺乏概括性.
- 需要自动化,可扩展的方法来精确地评估葡萄.
研究的目的:
- 评估分段任何模型 (SAM) 用于在2D图像中进行自动化葡萄细分.
- 评估SAM的准确性和分析葡萄群架构和紧性的潜力.
- 探索SAM在葡萄栽培图像分析管道中的整合.
主要方法:
- 利用开箱即用的任何细分模型 (SAM) 在2D图像中对单个葡萄进行细分.
- 处理了大约3,500个集群图像,生成了超过15万个带有空间坐标的果面具.
- 采用线性回归来调整由于可见性而低估果数量.
主要成果:
- SAM在果识别方面表现出很高的准确性 (与人类计数相比,皮尔森的r=0.96).
- 一个线性回归模型有效地调整了可见果数异常 (调整R2=0.87).
- 图像采集角度显著影响果数和架构分析.
结论:
- 在没有额外的培训的情况下,SAM为葡萄细分提供了高度准确和可扩展的解决方案.
- 来自SAM的贝里位置数据有助于计算复杂的建筑和紧性特征.
- SAM显示了将其集成到精准农业的葡萄园图像分析工作流中的巨大潜力.
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