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这是一种新的方法,结合了多谱成像和机器学习,以识别光滑草中的种子成熟度和活力

Chengming Ou¹, Zhicheng Jia¹, Shiqiang Zhao¹

¹College of Grassland Science and Technology, Key Laboratory of Pratacultural Science, China Agricultural University, Beijing Municipality, Beijing, 100193, China.

|March 26, 2025

概括

这项研究开发了一个机器学习模型,使用多谱成像来准确识别光滑草 (Bromus inermis) 种子的成熟度和活力. 集体模型有效地根据含量和谷物位置预测种子质量.

科学领域:

农业科学农业科学
植物科学植物科学
数据科学数据科学数据科学

背景情况:

种子的成熟度和活力对于光滑的草 (Bromus inermis) 作物建立和产量至关重要.
的应用水平和谷物的位置在spikelet可以影响种子的发展和质量.

研究的目的:

调查化肥和谷物位置对光滑草种子成熟度和活力的影响.
开发和验证一种机器学习模型,用于使用多光谱和自光成像识别种子成熟度.

主要方法:

平滑的草种子经历了三种含量 (0, 100, 200 公斤 N ha-1) 和两个位置 (上方和下方的谷物).
多光谱和自光成像与堆叠组合学习方法相结合,以预测种子成熟度.
为了模型的可解释性,使用了夏普利添加式解释 (SHAP) 方法.

主要成果:

含量和谷物位置显著影响了种子活力,随着成熟而增加.
在不同处理过程中,Ensemble模型在识别种子成熟度方面表现出高准确度 (87-93%).
关键的光谱特征与新鲜体重,射击长度和活力指数有显著的相关性.

结论:

开发的Ensemble模型为评估光滑草种子的成熟度和活力提供了一个高度准确和可概括的策略.
多光谱成像与机器学习相结合,为种子质量评估提供了一种有效的非破坏性方法.

关键词:

多光谱成像技术的使用.气气是一种气.种子的成熟期为种子.平滑的草是一种平滑的草.堆叠组合组合堆叠组合组合

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