农业机械学报2026，Vol.57Issue(2)：375-383,9.DOI:10.6041/j.issn.1000-1298.2026.02.036
基于小样本迁移学习的跨地点菇房温度预测模型研究
Cross-location Mushroom House Temperature Prediction Model Based on Small Sample Transfer Learning
摘要
Abstract
Accurate temperature prediction in mushroom houses is crucial to ensuring the efficient industrial production of edible mushrooms.However,existing predictive models often lack generalizability when applied to mushroom houses located in different regions.Taking into account the disruptive effects of environmental changes caused by equipment in mushroom cultivation houses,and equipment operation state features was integrated to develop a temperature prediction model based on the temporal convolution network and long short-term memory model(TCN-LSTM).This model used TCN to extract local information along the temporal dimension,while LSTM captured the long-term dependencies of time series data.Compared with models that did not integrate device operating state features,the TCN-LSTM model that incorporated these features reduced the MSE by 17.1%,35.7%,and 44.1%,and reduced MAE by 4.3%,28.0%,and 38.0%for prediction horizons of 1 hour,2 hours,and 3 hours,respectively.This result indicated that incorporating equipment operating states had a significantly positive effect on the prediction performance.Compared with other shallow and deep learning models,the TCN-LSTM model achieved the best prediction accuracy for different prediction horizons,with R2 no less than 0.982,and both MSE and MAE no more than 0.57℃for horizons up to 3 hours,satisfying the requirements of accuracy and duration in temperature predictions for mushroom room environmental control.This study employed transfer learning via pre-training and fine-tuning to adjust network parameters in small sample datasets,achieving rapid construction of temperature prediction models for mushroom rooms at different locations.The results indicated that for prediction horizons of 1 hour,2 hours,and 3 hours,the prediction models built using different locations as target domains achieved R2 values no less than 0.912,MSE values no more than 4.02℃,and MAE values no more than 2.01℃in the test set.These results suggested that under small sample conditions,the temperature models constructed for different locations using transfer learning can achieve accurate temperature predictions at different steps.关键词
菇房/温度/时序预测模型/迁移学习/小样本Key words
mushroom house/temperature/time-series prediction model/transfer learning/small sample分类
信息技术与安全科学引用本文复制引用
胡瑾,刘行行,孙大虎,张莹,杨永霞,雷文晔..基于小样本迁移学习的跨地点菇房温度预测模型研究[J].农业机械学报,2026,57(2):375-383,9.基金项目
陕西省重点研发计划项目(2024NC-ZDCYL-05-07) （2024NC-ZDCYL-05-07）
