预测芝麻 (Sesamum indicum L.) 叶面积的人工神经网络方法:一种非破坏性和准确的方法
João Everthon da Silva Ribeiro1, Ester Dos Santos Coêlho1, Anna Kézia Soares de Oliveira1
1Federal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil.
Heliyon
|July 28, 2023
概括
在植物研究中,非破坏性地估计芝麻叶面积至关重要. 人工神经网络 (ANN) 模型在预测叶面积方面被证明比回归更准确,提供了精确和快速的方法.
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:
- 准确的叶面积估计对于植物研究和作物管理至关重要.
- 对于在同一工厂进行重复测量时,最好采用非破坏性方法.
- 现有的方法可能需要昂贵的设备或缺乏精度.
研究的目的:
- 开发和比较人工神经网络 (ANN) 和回归模型来估计芝麻叶面积.
- 确定最有效的模型,用于精确和快速预测芝麻中的叶面积.
- 建立一个具有成本效益,非破坏性的方法来量化叶面积.
主要方法:
- 收集了来自四种芝麻品种的11000个叶子.
- 测量叶子的长度 (L),宽度 (W) 和实际叶子面积 (LA).
- 开发了ANN模型,L和W作为输入,LA作为输出.
- 开发了使用L和W预测LA的线性回归模型.
主要成果:
- 确定了最好的线性回归模型和ANN模型 (2-3-1配置).
- 在回归模型中,ANN模型的准确性高于回归模型.
- 在ANN模型中,以最小的误差 (RMSE,MAE,MAPE) 实现了0.9834 (培训) 和0.9828 (测试) 的R2.
结论:
- 人工神经网络模型对于估计芝麻叶面积非常准确和有效.
- 该ANN方法提供了一个精确,快速和非破坏性的方法来量化叶面积.
- 这项研究为农业研究和芝麻种植提供了有价值的工具.
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