A decision support framework using pruned trees for analytical quality assessment in agri-marine products

Seaweed is a globally significant aquaculture commodity with increasing economic and environmental importance. However, inconsistent quality standards and subjective procurement practices continue to hinder efficiency in its value chain, particularly for dried Eucheuma seaweed. This study presents a decision support system that applies the C4.5 decision tree algorithm, enhanced through six pruning techniques, to classify seaweed quality and guide purchasing decisions in informal market settings. A dataset of 259 entries was compiled, capturing nine key quality attributes. This dataset was used to develop and evaluate a pruned decision tree model that assigns seaweed samples to one of two procurement classes: worthy or not feasible. Model performance was evaluated using six pruning methods: threshold, cost complexity, reduced error, pessimistic error, critical value, and minimum number pruning. Among these, threshold and cost complexity pruning produced the highest classification accuracy at 63.4%, while maintaining model interpretability and minimizing overfitting. The most influential attributes were drying time, moisture content, and price, while the remaining features had a negligible impact. Validation was conducted through bootstrapping, confirming model robustness across sampling variations. The final model was implemented in a web-based interface using explainable artificial intelligence to support real-time, transparent decision-making for buyers and supply chain stakeholders. Despite limitations in the current feature set and the use of a single classifier, the system offers a practical and interpretable tool for quality-based procurement in agri-marine environments. Future research will aim to improve predictive performance by incorporating environmental data, image-based grading, biochemical profiling, and exploring ensemble methods.