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Predicting Corn Moisture Content in Continuous Drying Systems Using LSTM Neural Networks.

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

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Summary

This study introduces a machine learning model to predict corn moisture content during drying. The model enhances efficiency, reduces energy use, and improves product quality in Agriculture 4.0.

Keywords:
LSTMartificial intelligencebig datadryingmoisture prediction

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Area of Science:

  • Agricultural Engineering
  • Data Science
  • Food Processing Technology

Background:

  • Optimizing food production efficiency is crucial for Agriculture 4.0.
  • Corn drying efficiency impacts long-term storage and economic viability.
  • Innovative technologies are needed to improve critical food processes.

Purpose of the Study:

  • To develop a predictive model for corn moisture content using machine learning.
  • To assess the model's accuracy and utility in continuous drying systems.
  • To contribute to sustainable drying techniques and data-driven process improvements.

Main Methods:

  • Utilized historical data (3826 samples) with various drying parameters and weather conditions.
  • Applied data imputation techniques to ensure data integrity for model training.
  • Implemented a multilayer neural network with an LSTM layer and three dense layers.

Main Results:

  • Achieved high predictive accuracy with RMSE of 0.645, MSE of 0.416, MAE of 0.352, and MAPE of 2.555.
  • Demonstrated the model's effectiveness through objective performance metrics and data visualization.
  • Validated the model's capability to predict outlet moisture content in continuous drying.

Conclusions:

  • The proposed data-driven model is a valuable tool for predicting corn moisture content.
  • This approach enhances process efficiency, reduces energy consumption, and improves product quality.
  • The findings support the advancement of sustainable continuous drying in the food industry.