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基于集群的摄影和建模的综合方法,用于有效测量麻叶面积

Jittrawan Thaiprasit¹, Porntip Chiewchankaset¹, Saowalak Kalapanulak^1,2

¹Center for Agricultural Systems Biology (CASB), Systems Biology and Bioinformatics Research Laboratory, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi, Bang Khun Thian, Bangkok, Thailand.

|October 20, 2023

概括

一种新的混合方法使用摄影和建模准确估计麻叶面积 (LA). 这种实用方法为大规模植物表型化提供了有效的解决方案,特别是在偏远的田野条件下.

科学领域:

农业科学农业科学
植物生物学植物生物学
计算生物学计算生物学

背景情况:

叶面积 (LA) 和生物量是植物生长和健康的关键指标.
由于时间,成本和劳动力,传统的LA测量方法在大规模研究中通常是不切实际的.

研究的目的:

开发一种实用和高效的混合方法来估计麻叶面积.
创建可靠的LA估计模型,适用于各种麻豆基因型和生长条件.

主要方法:

收集了来自3种基因型和5种疾病的1,899个麻豆叶的数据.
根据长度 (L) 和宽度 (W) 按大小分组的叶子.
开发了一个预测模型,使用来自111个代表叶子的图像处理数据.

主要成果:

使用L和W的乘积的混合模型表现出高精度 (R2 = 0.9566,RMSE = 20.00).
开发的模型已成功验证,用于估计温室种植的麻的LA.

结论:

混合摄影和建模方法为LA估计的实际表型化工具提供了突破.
这种方法对于大型实验和机械有限的远程实地应用特别有价值.

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Light Acquisition

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