ArticleSensors (Basel, Switzerland)2025
Optimization of Online Moisture Prediction Model for Paddy in Low-Temperature Circulating Heat Pump Drying System with Artificial Neural Network.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Optimization of flavonoids extraction and elucidation of antioxidant mechanisms in Dendrobium flexicaule using metabolomics and machine learning.Ultrasonics sonochemistry · 2026Article
- Machine learning prediction and optimization of thermodynamic analysis and energy enhancement of a hybrid infrared dryer for onion slices.Scientific reports · 2026Article
- Microwave drying effects on modeling thin-layer drying kinetics, energy efficiency analysis, and physical properties of garlic.Scientific reports · 2026Article
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Authors and funding
6 authors.
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Abstract
The accurate prediction of moisture content is crucial for controlling the drying process of agricultural products. While existing studies on drying models often rely on laboratory-scale experiments with limited data, real-time and high-frequency data collection under industrial conditions remains underexplored. This study collected and constructed a multi-dimensional dataset using an industrial-grade data acquisition system specifically designed for heat pump low-temperature circulating dryers. An artificial neural network (ANN) prediction model for moisture content during the rice drying process was developed. To prevent model overfitting, K-fold cross-validation was utilized. The model's performance was evaluated using the mean squared error (MSE) and the coefficient of determination (R
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