{"title":"基于 DOA-BP 的双孢蘑菇商业栽培温湿度预测模型研究","authors":"Tianhua Li, Yinhang Dong, Guoying Shi, Guanshan Zhang, Chao Chen, Jianchang Su","doi":"10.35633/inmateh-73-13","DOIUrl":null,"url":null,"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.","PeriodicalId":514571,"journal":{"name":"INMATEH Agricultural Engineering","volume":"23 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"RESEARCH ON THE DOA-BP-BASED TEMPERATURE AND HUMIDITY PREDICTION MODEL FOR COMMERCIAL CULTIVATION OF AGARICUS BISPORUS\",\"authors\":\"Tianhua Li, Yinhang Dong, Guoying Shi, Guanshan Zhang, Chao Chen, Jianchang Su\",\"doi\":\"10.35633/inmateh-73-13\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":514571,\"journal\":{\"name\":\"INMATEH Agricultural Engineering\",\"volume\":\"23 5\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"INMATEH Agricultural Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35633/inmateh-73-13\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"INMATEH Agricultural Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35633/inmateh-73-13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
要更好地管理蘑菇房内的气候条件,最终提高双孢蘑菇的产量和质量,就必须准确预测双孢蘑菇栽培过程中的环境变化。然而,传统的双孢蘑菇生产环境控制系统只能监测当前条件,缺乏预测环境变化的能力,从而导致环境数据和控制措施的有效性反馈延迟等问题。针对这些挑战,本研究建立了基于 DOA-BP 算法的温湿度预测模型。实验结果表明,DOA 优化算法具有很强的全局搜索能力。通过快速搜索最优权值和偏置,它克服了 BP 神经网络陷入局部最小值的缺点,加速了网络收敛,提高了 BP 神经网络的性能。菇房内温度和湿度预测的 MAE 值分别为 0.021 和 0.013。RMSE 值分别为 0.044 和 0.038,R2 值分别为 0.976 和 0.968。通过验证,本研究提出的 DOA-BP 温湿度预测模型可以准确预测菇房内的温湿度。该模型可加强栽培环境控制,优化资源利用,有效降低生产成本。
RESEARCH ON THE DOA-BP-BASED TEMPERATURE AND HUMIDITY PREDICTION MODEL FOR COMMERCIAL CULTIVATION OF AGARICUS BISPORUS
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.