Neuro-Symbolic Artificial Intelligence for Explainable Real-Time Anomaly Detection in Agricultural Commodity Markets: A Systematic Review and Architectural Framework for Developing Economies

Food security is seriously threatened by price irregularities in agricultural products, which disproportionately affect developing countries with inadequate data, low market visibility, and weak regulatory frameworks. The use of machine learning (ML) and deep learning (DL) methods for time-series anomaly detection has proven effective yet their operation relies on a 'black box' framework which fails to deliver understandable evidence needed for effective policy development. The development of Neuro-Symbolic Artificial Intelligence (NSAI) as a hybrid system unifies neural network pattern recognition with logical reasoning capabilities provides an effective solution to the problem of interpretability. The research paper conducts a systematic scoping review which includes 47 studies that the authors found through their structured search of IEEE Xplore Scopus Web of Science and Google Scholar between 2020 and 2025. We examine the theoretical underpinnings of NSAI technology together with existing methods for detecting anomalies in agricultural price data and we evaluate the capability of NSAI systems to function in African agricultural markets with limited resources. The research synthesis identifies four main architectural designs which enable NSAI-based anomaly detection. The research findings show that no research study has tested an integrated NSAI system for detecting crop price anomalies in sub-Saharan African countries. The research shows that NSAI systems provide better explainability in cyber-physical and financial systems but they need special design for particular application areas. The primary obstacles to implementation arise from data shortages, challenges in knowledge engineering, and limitations in computational power. The review establishes an organized research framework together with an architectural reference system which researchers can use to conduct studies in this developing research area.