Construction of Full-Space State Model and Prediction of Plant Growth Information

Highlights This research proposes a model based on DTs and BPNN and accurately predicts the growth indexes and state of lettuce. Abstract. 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 Root Mean 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. Keywords: Back propagation neural network, Digital twins technology, Lettuce, Plant factory, State prediction.