A comprehensive review of CGBnet: A deep learning framework for compost classification

This research paper explores advancements in compost classification techniques, with a specific focus on the deep learning framework, CGBNet. The objectives are to address challenges in traditional compost classification methods and compare the performance of CGBNet with existing machine learning models. The methodology involves a comprehensive literature review of compost classification approaches, followed by an in-depth analysis of CGBNet’s architecture, training methodology, and applications in waste management. Key findings highlight CGBNet’s superior adaptability to dynamic compost compositions, hierarchical feature extraction, and transfer learning advantages. Performance metrics such as accuracy, precision, recall and F1 score are utilized for an in-depth evaluation. The paper also provides insights into potential applications of CGBNet in automated sorting systems, municipal waste management, and environmental monitoring. Future directions include improving model interpretability, addressing dataset imbalances, exploring sensor integration, and optimizing for edge computing. The implications of this research lie in the potential transformation of waste management practices through the automation and efficiency brought about by CGBNet.