Research on non-destructive detection model of tomato fruit quality based on electrical properties and machine learning algorithms
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new LSTMAE-XGBoost model for non-destructive tomato quality detection. It accurately predicts internal quality indicators, improving upon traditional methods for agricultural product assessment.
Area Of Science
- Agricultural Science
- Machine Learning
- Sensory Science
Background
- Traditional methods for assessing internal tomato quality are destructive and time-consuming.
- There is a need for rapid, non-destructive, and accurate methods for agricultural product quality assessment.
Purpose Of The Study
- To develop a novel predictive method for non-destructive detection of internal tomato quality indicators.
- To integrate Long Short-Term Memory Autoencoder (LSTMAE) and XGBoost for enhanced prediction accuracy.
Main Methods
- Collected electrical parameters (capacitance, resistance, quality factor) from 300 tomato samples.
- Performed physicochemical analysis for vitamin C, soluble sugar, soluble protein, and titratable acidity.
- Developed a LSTMAE-XGBoost model using electrical and physicochemical data for non-destructive quality prediction.
Main Results
- The LSTMAE-XGBoost model achieved high prediction accuracy, with coefficients of determination of 0.805 (vitamin C), 0.945 (soluble sugar), 0.838 (soluble protein), and 0.845 (titratable acidity).
- The model demonstrated superior performance compared to traditional machine learning models and improved feature extraction by up to 14.3%.
- The model efficiently predicts all four internal quality indicators simultaneously.
Conclusions
- LSTMAE-XGBoost is an effective ensemble model for non-destructive detection of internal tomato quality indicators.
- This method offers significant advancements for fruit quality assessment in the horticultural industry.
- The proposed model provides efficient technical means for agricultural product quality evaluation.
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