RESEARCH ON THE DOA-BP-BASED TEMPERATURE AND HUMIDITY PREDICTION MODEL FOR COMMERCIAL CULTIVATION OF AGARICUS BISPORUS

Abstract

Accurate prediction of environmental changes in Agaricus bisporus cultivation is essential for better managing climatic conditions within mushroom houses, ultimately enhancing the yield and quality of Agaricus bisporus. However, traditional control systems for Agaricus bisporus production environments can only monitor the current conditions and lack the ability to predict environmental changes, leading to issues such as delayed feedback on environmental data and the effectiveness of control measures. In response to these challenges, this study establishes a temperature and humidity prediction model based on the DOA-BP algorithm. Experimental results demonstrate that the DOA optimization algorithm exhibits strong global search capabilities. By rapidly searching for optimal weights and biases, it overcomes the drawback of the BP neural network getting stuck in local minima, accelerates network convergence, and improves the performance of the BP neural network. The MAE values for temperature and humidity prediction inside the mushroom house are 0.021 and 0.013, respectively. The RMSE values are 0.044 and 0.038, respectively, and the R2 values are 0.976 and 0.968, respectively. Through validation, the DOA-BP temperature and humidity prediction model proposed in this study accurately predicts the temperature and humidity inside mushroom houses. This model can enhance environmental control for cultivation, optimize resource utilization, and reduce production costs effectively.