CONSTRUCTION OF FULL-SPACE STATE MODEL AND PREDICTION OF PLANT GROWTH INFORMATION

文献类型： 外文期刊

第一作者： Wang, Ruixue

作者： Wang, Ruixue;Chen, Kaikang;Zhao, Bo;Zhou, Liming;Zhu, Licheng;Lv, Chengxu;Han, Zhenhao;Lu, Kunlei;Feng, Xuguang;Zhao, Siyuan

作者机构：

关键词： Back propagation neural network; Digital twins technology; Lettuce; Plant factory; State prediction

期刊名称：JOURNAL OF THE ASABE （ 影响因子：1.0； 五年影响因子：1.0 ）

ISSN： 2769-3295

年卷期： 2025 年 68 卷 2 期

页码：

收录情况： SCI

摘要： This research proposed a full-space state prediction model based on Digital Twins (DTs) for intelligent prediction and optimization control of environmental parameters and crop growth in plant factories. Compared with traditional prediction models, this model significantly improved production efficiency and resource utilization in plant factories by dynamically adjusting environmental control strategies through real-time data collection and feedback. The model employed a Back Propagation Neural Network (BPNN) for accurate prediction of crop growth indexes, with experimental results showing a RootMean Squared Error (RMSE) of 0.868 and a Mean Absolute Error (MAE) of 0.625 on the test dataset, indicating high prediction accuracy. The innovative aspect of this model lies its integration of DTs technology, enabling full-cycle monitoring and intelligent regulation of the crop growth process, addressing the limitations of existing models in dynamic feedback and real-time adjustment capabilities. Future extensive validation and optimization of the model across different crop types and environmental conditions will further enhance its potential for application in plant factory management.

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