Using tree-based machine learning models to predict diverse compost maturity via one-hot encoding: Model deployment, experimental validation, and practical application.

Journal: Waste management (New York, N.Y.)
Published Date: Jul 5, 2025

Abstract

This study innovatively integrates various classification features like initial properties of composting materials and scale, with process parameters like time, temperature, pH, etc., to predict compost maturity of various types using the seed germination index (GI) with tree-based machine learning (Random Forest, Extra-Trees, Gradient Boost, AdaBoost, XGBoost, and LightGBM). The results indicate that the AdaBoost model achieves a high prediction accuracy (R = 0.9720) with low errors (RMSE = 5.3495, MAE = 2.7872), outperforming other models. Feature importance on the Gini index and SHAP analysis reveals that in process parameters, composting time and pH are vital factors influencing maturity, while the type of compost material and seed variety also significantly impact GI. A stacking method combined the mentioned models to enhance prediction accuracy and ubiquity, resulting in an improved R of 0.9733. Various animal and plant-based organic wastes were used to validate the fusion model's effectiveness, which resulted that accurately predicted maturation juncture, especially for high-nitrogen source materials. Based on these findings, an easy-to-use online application was developed, providing a practical tool for predicting compost maturity and promoting the safe application of organic fertilizers in agriculture. This application aims to assist farmers and agricultural professionals in optimizing compost usage, enhancing soil quality, and ensuring sustainable farming practices.

Authors

  • Xuanshuo Zhang
    School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
  • Yilin Kong
    State Key Laboratory of Nutrient Use and Management, Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China.
  • Yan Yang
    Department of Endocrinology and Metabolism, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
  • Yan Liu
    Department of Clinical Microbiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, People's Republic of China.
  • Qianlin Gao
    State Key Laboratory of Nutrient Use and Management, Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China.
  • Ji Li
    Department of Biostatistics and Epidemiology, College of Public Health, University of Oklahoma Health Sciences Center, 801 NE 13th Street, CHB 203, Oklahoma City, OK 73104, x 30126.
  • Guoxue Li
    State Key Laboratory of Nutrient Use and Management, Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China.
  • Jing Yuan
    School of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.

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