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分析对米发育的影响,通过米检测和时间序列跟踪来分析的影响

Qinyang Zhou¹, Wei Guo², Na Chen¹

¹College of Agriculture, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China.

Plant phenomics (Washington, D.C.)

|June 26, 2023

概括

本研究引入了一种自动化管道,用于使用人工智能模型和时间序列图像进行详细的饼表型化. 它准确地追踪了在不同含量下花的发育和开花特征,揭示了对产量形成的重大影响.

科学领域:

农业科学农业科学
植物表型化植物表型化
人工智能在农业中的应用

背景情况:

了解米产量形成需要详细观察花的发展,特别是在花的开花阶段.
目前缺乏在不同的处理下对恐慌发作的全面分析.
需要自动化表型化方法来捕捉微妙的发育差异.

研究的目的:

开发和验证一条自动化管道,使用时间序列图像进行详细的米饼表型化.
在不同的气条件下分析的发育和开花特征.
评估管道能够识别恐慌发育中的微妙差异的能力.

主要方法:

建议使用YOLO v5,ResNet50和DeepSORT模型进行图像分析的自动化管道.
利用时间序列图像来跟踪个体恐慌的发展.
结合自动化分析与现场观测数据进行验证.

主要成果:

实现了高精度的恐慌计数 (R2 = 0.96) 和精确的航线日期估计 (0.25天误差).
鉴定了应用对种群特征的显著影响,如形数量和开花时间.
揭示了较高的气增加了个体花的强烈开花的开花时间和总持续时间.

结论:

开发的管道准确地量化了大米的特征和它们对处理的反应.
自动化表型化为微妙的恐慌发育和开花动态提供了新的见解.
这些发现有助于更好地了解在米产量形成中的作用.

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