Image-driven in situ grading of compost maturity using deep feature clustering and supervised prediction

Accurate and cost-effective grading of compost maturity is critical for agronomic safety and process optimization. This study developed an image-driven in situ grading framework for compost maturity using deep feature clustering and supervised prediction, enabling multi-level differentiation beyond conventional binary maturity identification. Compost images were first labeled as mature or immature based on indicator-derived maturity results and then used to train deep learning models for initial classification and feature extraction. Class incremental learning-based P-ResNet-18 achieved the best classification performance, with all key metrics exceeding 0.94 and only a 0.3% training-test gap, indicating strong generalization capability. For refined maturity grading, latent Dirichlet allocation (LDA) achieved the best overall grading performance, with over 80% coverage, more than 90% purity, and the strongest monotonic relationship with composting duration. The LDA-derived labels further enabled supervised multi-level prediction, with all four evaluated models achieving grading accuracy above 90%. Grad-CAM analysis revealed a dispersed-concentrated-extensive evolutionary pattern during composting, with color and texture identified as the dominant discriminative features. The framework also remained robust under noise perturbation, with over 93% consistency, demonstrating its potential to support intelligent composting management through improved end-point determination and reduced unnecessary over-composting.