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多层感知神经网络-基因算法用于模拟尼古提亚烟草叶子质量

Mohammad Reza Najafi¹, Mohammad Ali Aghajani², Naser Safaie³

¹Department of Plant Protection, College of Agriculture Sciences and Food Industries, Science and Research Branch, Islamic Azad University, Tehran, Iran.

|October 7, 2025

概括

预测烟草叶的质量对全球行业至关重要. 多层感知神经网络-遗传算法 (MLPNN-GA) 模型基于化学成分和蓝模重度 (BMS) 准确预测质量,优于传统回归.

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:

烟草 (Nicotiana tabacum L.) 的质量直接影响香烟的风味和行业的利能力.
关键的内部质量因素包括蓝模严重程度 (BMS),叶绿素 (Chl), (N),糖 (S),尼古丁 (Nt), (Cl) 和 (K).
准确预测这些因素可以提高农民的收入,特别是在低收入和中等收入国家.

研究的目的:

在不同的条件下评估四种烟草品种 (伯杰拉克,贝尔,伯利,巴斯玛) 的叶子质量.
开发和比较基于化学成分和BMS的烟草叶质量的预测模型.
确定影响烟草叶质量的最有影响的因素.

主要方法:

在两个生长季节进行的BMS,Chl,N,S,Nt,Cl,K,绿质量 (GW),干质量 (DW) 和叶子质量的现场评估.
应用多重线性回归,阶段回归,普通最小平方,部分最小平方,主要成分回归和MLPNN-GA用于质量预测.
使用R平方值对模型准确性的比较分析.

主要成果:

贝尔品种在两个生长季节都表现出最高的叶子质量.
与最好的回归模型 (R2至0.82) 相比,MLPNN-GA模型显示出更高的预测准确性 (R2高达1.00).
分析显示,BMS是影响所有测试品种叶子质量的最重要因素.

结论:

MLPNN-GA是一种高效的工具,可以根据化学成分和BMS预测烟草叶的质量.
开发的模型为优化烟草质量和潜在提高农民利能力提供了一种实际方法.
了解叶子质量对BMS等特定因素的敏感性,是有针对性的农业管理的关键.

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