Growth Trend Prediction and Intervention of Panax Notoginseng Growth Status Based on a Data-Driven Approach

In crop growth, irrigation has to be adjusted according to developmental stages. Smart agriculture requires the accurate prediction of growth status and timely intervention to improve the quality of agricultural products, but this task faces significant challenges due to variable environmental factors. To address this issue, this study proposes a data-driven irrigation method to enhance crop yield. Our approach harvests extensive datasets to train and optimize an integrated deep-learning architecture combining Informer, Long Short-Term Memory (LSTM) networks, and Exponential Weighted Moving Average (EWMA) models. Controlled greenhouse experiments validated the reliability and practicality of the proposed prediction and intervention strategy. The results showed that the model accurately issued irrigation warnings 3-5 days in advance. Compared to traditional fixed irrigation, the model significantly reduced irrigation frequency while maintaining the same or even better growth conditions. In terms of plant quantity, the experimental group increased by 410.0%, while the control group grew by 50.0%. Additionally, the experimental group's average plant height was 21.8% higher than that of the control group. These results demonstrate the efficacy of the proposed irrigation prediction method in enhancing crop growth and yield, providing a novel strategy for future agricultural planning and management.