使用可解释的ANN来预测灰色朱果果的质量和机制
Mingyang Yu1,2, Yang Li1,2, Junkai Zeng1,2
1Tarim Basin Biological Resources Protection and Utilization Key Laboratory, Xinjiang Production and Construction Corps Alar China.
Food science & nutrition
|September 18, 2025
概括
使用可解释的人工神经网络模型预测灰色朱的质量. 关键因素包括芽延长,SPAD值,叶角和光透度,以实现最佳的果实发育.
科学领域:
- 农业科学 农业科学
- 植物生理学 植物生理学
- 计算生物学 计算生物学
背景情况:
- 灰色朱 (Ziziphus jujuba) 是中国新疆的一个重要的经济水果作物.
- 果实质量受到树木结构,生理学和环境之间的复杂相互作用的影响.
研究的目的:
- 开发一个可解释的人工神经网络模型来预测灰色朱的关键质量参数.
- 确定影响水果质量的关键结构和生理指标.
主要方法:
- 实地实验超过两年.
- 使用贝叶斯优化开发和优化人工神经网络模型.
- 整合了13个结构和生理指标.
主要成果:
- 为维生素C,可溶糖,可定位酸和糖酸比率实现了高预测准确度 (R2 = 0.89-0.98).
- 鉴定了射线延长和SPAD值对于维生素C积累至关重要.
- 确定了最佳的叶子倾斜角度和光透光度,用于糖的积累.
- 发现高净光合作用率降低有机酸含量.
结论:
- 开发的模型有效地预测了灰色朱的质量,并提供了机械的洞察力.
- 这些发现支持精准种植策略,以提高水果质量.
- 综合框架提供了一种全面的方法来管理作物质量.
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