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Proactive Irrigation Timing Decision-Making for Greenhouse Tomatoes via STL-LSTM Deep Learning and Plant-Soil
Wei Zhou1,2, Zhenglin Li1, Yuande Dong1,2
1College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Sensors (Basel, Switzerland)
|May 27, 2026
Summary
This study introduces an intelligent irrigation system for greenhouse tomatoes, using stem diameter and soil moisture to optimize watering. The new method improves water efficiency in arid regions.
Area of Science:
- Agricultural Engineering
- Plant Physiology
- Data Science
Background:
- Traditional greenhouse tomato irrigation in arid regions relies on experience, leading to poor water efficiency.
- Current methods lack crop-specific physiological data for precise irrigation scheduling.
Purpose of the Study:
- Develop an intelligent, plant-centric irrigation framework for greenhouse tomatoes in Xinjiang's arid region.
- Enable precise, on-demand irrigation by accurately identifying optimal timing.
Main Methods:
- Monitored high-frequency stem diameter (SD) dynamics and soil moisture.
- Developed a hybrid STL-LSTM model to forecast 24-h SD trends.
- Implemented a dual-threshold irrigation mechanism using physiological (MDS) and soil moisture (VWC) triggers.
Main Results:
- Tomato SD showed distinct diurnal rhythms; Maximum Daily Shrinkage (MDS) and Daily Increment (DI) are sensitive indicators of water status.
- The STL-LSTM model achieved R² of 0.9184 for SD prediction, outperforming LSTM and ARIMA.
- The dual-threshold mechanism effectively balanced crop water demand and conservation.
Conclusions:
- The intelligent framework provides a scientific basis for precision irrigation in arid greenhouse agriculture.
- Optimized water resource management is achievable through plant-centric, data-driven irrigation strategies.
- This approach mitigates risks associated with traditional, experience-based irrigation methods.
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