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相关概念视频

Responses to Drought and Flooding02:41

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Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
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Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.
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Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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相关实验视频

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在使用LSTM神经网络的连续干燥系统中预测玉米水分含量.

Marko Simonič1, Mirko Ficko1, Simon Klančnik1

  • 1Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia.

Foods (Basel, Switzerland)
|April 15, 2025
PubMed
概括

本研究引入了一种机器学习模型,用于预测玉米在干燥过程中的水分含量. 该模型提高了效率,减少了能源使用,并提高了农业4.0中的产品质量.

科学领域:

  • 农业工程 农业工程
  • 数据科学数据科学数据科学
  • 食品加工技术 食品加工技术

背景情况:

  • 优化粮食生产效率对于农业4.0至关重要.
  • 玉米干燥效率影响长期储存和经济可行性.
  • 需要创新技术来改善关键的食品工艺.

研究的目的:

  • 使用机器学习开发玉米水分含量的预测模型.
  • 评估模型在连续干燥系统中的准确性和实用性.
  • 为可持续的干燥技术和数据驱动的过程改进做出贡献.

主要方法:

  • 利用了各种干燥参数和天气条件的历史数据 (3826个样本).
  • 应用数据归算技术,以确保模型培训的数据完整性.
  • 实现了一个多层神经网络,其中有一个LSTM层和三个密集层.

主要成果:

  • 实现了高预测准确度,RMSE为0.645,MSE为0.416,MAE为0.352,MAPE为2.555. 这两种情况的预测准确度都很高.
  • 通过客观绩效指标和数据可视化来证明模型的有效性.
  • 验证了模型在连续干燥中预测出口含水量的能力.
关键词:
这是LSTM的LSTM.人工智能是一种人工智能.大数据就是大数据.干燥 干燥 干燥 干燥湿度预测的预测

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结论:

  • 拟议的数据驱动模型是预测玉米水分含量的宝贵工具.
  • 这种方法提高了工艺效率,降低了能源消耗,提高了产品质量.
  • 这些发现支持在食品工业中推进可持续的连续干燥.