A Rice Security Risk Assessment Method Based on the Fusion of Multiple Machine Learning Models

Abstract

With the accelerated digital transformation, food security data is exponentially growing, making it difficult to process and analyze data as the primary challenge for food security risk regulation. The promotion of “big data + food” safety supervision can effectively reduce supervision costs and improve the efficiency of risk detection and response. In order to improve the utilization of testing data and achieve rapid risk assessment, this paper proposes a rice security risk assessment method based on the fusion of multiple machine learning models, and conducts experimental validation based on rice hazard detection data from 31 provinces in China excluding Hong Kong, Macao and Taiwan in 2018. The model comparison verifies that the risk assessment model shows better performance than other mainstream machine learning algorithms, and its evaluation accuracy is as high as 99.54%, which verifies that the model proposed in this paper is more stable and accurate, and can provide accurate and efficient decision-making basis for regulatory authorities.