Authors

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.

Abstract in Chinese

精准预测双孢菇生产环境变化有助于更好的管理菇房内的气候条件，提高双孢菇产量与质量。但传统的双孢菇生产环境控制系统只能对当前环境状况进行监测，无法对环境变化做出预判，导致环境数据的反馈和调控措施的生效都存在滞后性等问题。针对以上问题，本文建立了基于DOA-BP的温湿度预测模型，实验结果表明，DOA优化算法具有较强全局搜索能力，通过快速搜索最优权值和偏置，克服了BP神经网络陷入局部极小值的缺点，加快网络收敛速度，提高了BP神经网络的性能。该预测模型对菇房内温湿度预测的MAE值分别为0.021、0.013，RMSE值分别为0.044、0.038，R2值为0.976、0.968。通过验证，本研究提出的DOA-BP温湿度预测模型能够精准预测菇房温湿度，可以更好的控制栽培环境，还可以合理安排资源利用，降低生产成本。