Work

Feedstock-Aware Machine Learning for Compost Maturity Classification: Cross-Domain Transfer Diagnosis and Threshold Calibration

Year: 2026

Type: article

Abstract: Compost maturity screening supports safe land application and organic waste recycling, but germination index (GI) assays are not always available for rapid process assessment. This study performed a secondary GI-based maturity classification reconstruction using a published Nature Food composting dataset. From this source, 184 observations from 24 manure-based composting batch trajectories across five feedstock domains were retained when GI and routine physicochemical variables were available. GI values were converted into three maturity stages and a binary mature/non-mature endpoint, while the GI itself was excluded from model inputs. Logistic regression, random forest, and extra trees models were evaluated under random split, batch-aware group split, and leave-one-feedstock-domain-out validation. Random and group splits showed stronger apparent performance than cross-feedstock validation, indicating sensitivity to feedstock-domain transfer. In binary classification, the area under the receiver operating characteristic curve (ROC-AUC) remained relatively high in several model–domain combinations, whereas mature-class F1 declined, revealing a discrimination decision gap under default thresholds. Training-domain threshold calibration partially improved mature-class detection without using the held-out feedstock domain for threshold selection. These results support feedstock-aware validation and calibrated decision thresholds for sustainable compost maturity screening.

Source: Sustainability

DOI: 10.3390/su18168481