U + LSTM-F: A data-driven growth process model of rice seedlings

Abstract

Accurately predicting the growth status of rice seedlings and understanding their growth rate and health status in a timely manner helps adjust the growth cycle and management measures. By predicting the growth status of the seedlings, the best time for transplanting can be selected, improving the survival rate and overall health of the seedlings, thereby enhancing yield and quality. Therefore, this study proposes a data-driven time-series model, the U + LSTM-F model, for predicting the growth status of Wuyou Rice 4 seedlings. First, the U-Net model is employed to segment sequentially collected images, extracting features such as leaf age and stem length of the rice seedlings. Subsequently, the collected ambient temperature and humidity data are aligned with the leaf age and stem length data. Finally, the LSTM model is used for time-series analysis, enabling the model to learn the temporal relationship between environmental and growth data and predict the growth trend of the rice seedlings. Additionally, an attention mechanism is introduced to enhance model performance, and the model's effectiveness is evaluated using multiple quantitative metrics. The proposed model achieves an RMSE of 0.032 and MAPE of 0.895 % for leaf age prediction, and an RMSE of 0.067 and MAPE of 0.814 % for stem length prediction. The experimental results show that this data-driven approach, which combines growth data with environmental data, exhibits high accuracy in predicting the leaf age and stem length of rice seedlings. This provides a more accurate tool for predicting the growth of rice seedlings, offering valuable insights for rice seedling cultivation research.

Keywords

• Rice seedlings growth status
• Data-driven time-series model
• Features extraction
• Attention mechanism