番茄水果的动态压缩压力放松模型基于长期短期记忆模型
Mengfei Ru1,2, Qingchun Feng3,2, Na Sun3
1College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Foods (Basel, Switzerland)
|July 27, 2024
概括
使用长短期记忆 (LSTM) 的新动态粘弹性模型准确预测番茄的压力放松. 与经典方法相比,这种先进的模型显著减少了预测错误,最大限度地减少了收获期间的机械损伤.
科学领域:
- 农业工程 农业工程
- 材料科学 材料科学 材料科学
- 计算生物学 计算生物学
背景情况:
- 番茄的自动收获和收获后处理通常会由于不适当的抓取而导致机械损坏.
- 了解番茄的动态粘弹性特性对于防止这种损伤至关重要.
研究的目的:
- 开发和验证使用长短期记忆 (LSTM) 来预测番茄压力放松的动态粘弹性模型.
- 为了比较LSTM模型的性能与经典的应力放松模型 (马克斯韦尔和卡普托的分数导数).
主要方法:
- 基于LSTM的动态粘弹性模型被采用,以适应单个西红的压缩应力放松特性.
- LSTM模型的预测性能与三元的麦克斯韦和卡普托分数衍生模型进行了验证.
- 对不同大小和压缩位置的西红预测了压力放松特征.
主要成果:
- 在整个数据集上,LSTM模型实现了根平均平方误差 (RMSE) 2.829×10-5 MPa 和平均绝对百分比误差 (MAPE) 0.228%.
- 与卡普托分数衍生模型相比,LSTM模型显示RMSE下降了37%,MAPE下降了36%.
- 该模型在不同水果大小和压缩位置上显示出高精度,隔膜位置产生最小的误差 (RMSE:3.438×10-5 MPa,MAPE:0.375%).
结论:
- 基于LSTM的动态粘弹性模型在预测番茄应力松方面明显优于经典模型.
- 该模型提供了一种有前途的方法,可以在自动化处理过程中减少番茄的机械损伤.
- LSTM模型有可能取代传统的压力放松模型,用于单个水果的分析.
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