Design of netted muskmelon digital twin growth model based on HMM+LSTM algorithm

【Objective】The purpose of this paper is to enhance agricultural water use efficiency, and to develop a digital twin system for simulating the entire growth life-cycle of crops, which holds significant importance for advancing smart agriculture in China and assisting farmers in formulating optimized management strategies. 【Method】Using netted muskmelon as a case, in the Yellow River Diversion Irrigation District of Huayuankou, Henan Province, we conducted controlled indoor experiments replicating local climatic conditions. An IoT-based monitoring network was employed to collect real-time data on environmental parameters and growth status throughout the cultivation process. The digital twin model was developed using 3ds Max for 3D modeling and Unity 3D for visualization, while the growth prediction model was built by integrating Hidden Markov Model (HMM) and Long Short-Term Memory (LSTM) algorithms. 【Result】Simulation results demonstrated high recognition accuracy across different growth stages: 85.3% for seed and seedling stages, 78.6% for leaf stage, with an overall average accuracy of 82.8%. 【Conclusion】The proposed system, combining wireless sensor networks with HMM+LSTM algorithms to generate 3D growth models of muskmelon digital twins, achieves precise, efficient, and non-destructive visualization of the entire growth process, and can be extended to construct digital twins for other crops.