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Machine learning assisted-hyperspectral imaging for in-situ evaluation of compost maturity
Jianmei Zou1, Shengui Tang1, Chao Chen2
1College of Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, PR China; Sichuan Provincial Engineering Research Center of Agricultural Non-point Source Pollution, PR China.
Waste Management (New York, N.Y.)
|March 28, 2026
Summary
This study introduces a hyperspectral imaging (HSI) and machine learning framework for compost maturity assessment. The method uses characteristic spectral bands for accurate, rapid, and non-destructive monitoring in waste management.
Area of Science:
- Environmental Science
- Agricultural Engineering
- Remote Sensing
Background:
- Compost maturity assessment is crucial for organic waste recycling quality control.
- Current methods lack robustness due to multi-dimensional maturity and single-indicator models.
- Existing hyperspectral imaging (HSI) approaches often use full-spectrum data, limiting practical use.
Purpose of the Study:
- To develop an integrated HSI-machine learning framework for robust compost maturity assessment.
- To utilize a unified maturity representation from multiple indicators for improved model performance.
- To establish a rapid, non-destructive, and computationally efficient monitoring strategy.
Main Methods:
- Developed an integrated HSI-machine learning framework using a unified maturity representation.
- Applied principal component analysis (PCA) for spectral data reduction.
- Trained and evaluated four machine learning models (Random Forest, Extreme Gradient Boosting) for classification and regression.
- Utilized ten characteristic spectral bands instead of the full spectrum for modeling.
Main Results:
- Random Forest and Extreme Gradient Boosting models achieved high performance in maturity classification (96.30% and 98.72% recall) and regression (R² of 0.917 and 0.886).
- Using ten characteristic bands significantly reduced spectral redundancy and computational cost.
- The characteristic-band approach maintained model stability and decreased mean relative error to 2.42%.
Conclusions:
- The proposed HSI-machine learning framework offers a rapid, non-destructive, and efficient method for compost maturity monitoring.
- The characteristic-band strategy enhances practical applicability and reduces computational demands.
- This approach supports intelligent quality control in sustainable waste management and organic recycling.
