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对作物产量动态的分析和多式联络神经网络预测模型的开发,使用G×E×M相互作用

Saiara Samira Sajid¹, Zahra Khalilzadeh¹, Lizhi Wang²

¹Iowa State University, Industrial Manufacturing & Systems Engineering, Ames, IA, United States.

Frontiers in plant science

|August 18, 2025

概括

准确的产量预测模型,考虑到基因型,环境和管理 (G×E×M),对于粮食安全至关重要. 一个多式联网CNN-DNN组合与XGBoost在预测田间水平作物产量方面表现出卓越的表现.

科学领域:

农业科学农业科学
数据科学数据科学数据科学
遗传学是一个遗传学.

背景情况:

基因型,环境和管理 (G×E×M) 相互作用显著影响作物产量.
准确的早期产量预测对于有效的农场管理和确保粮食安全至关重要.

研究的目的:

开发和评估田间农作物产量的预测模型.
识别能够适应各种条件的高产混合物.
了解G×E×M相互作用对作物生产的影响.

主要方法:

开发了一种与XGBoost相结合的多模式卷积神经网络 - 深度神经网络 (CNN-DNN).
该模型利用了包括基因型,空间,时间,环境和管理因素在内的综合数据.
该模型应用于G2F数据集 (2014-2021) 在21个州和各种试验条件.

主要成果:

与XGBoost相结合的多式联网CNN-DNN显示出优异的收益率预测性能.
该模型的整体根平均平方误差 (RMSE) 为2.45毫克/公,在稳定条件下 (RMSE 2.36毫克/公) 非常出色.
开发了一种经验工具,用于识别各种生长环境的高产混合物.

结论:

根据特定的当地和混合条件定制的定制管理策略对于优化作物产量至关重要.
农作物产量受到混合位置相互作用和种植时间的影响.
先进的预测建模增强了农业决策,并有助于粮食安全.

关键词:

基因型基因型基因型高收益混合动力分类的高收益混合动力分类.多式联网CNN-DNN多式联网种植日期种植日期精准农业是精准的农业.

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