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Research on Tea Tree Growth Monitoring Model Using Soil Information.

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  • 1Electronic Information School, Wuhan University, Wuhan 430072, China.

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Summary

This study developed advanced models for tea plantation growth prediction using soil temperature, moisture, and electrical conductivity. An optimized LSTM network significantly improved prediction accuracy for better agricultural management.

Keywords:
crop growth modellong short-term memory (LSTM)normalized difference vegetation index (NDVI)soil indicators

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Area of Science:

  • Agricultural Science
  • Agronomy
  • Soil Science
  • Data Science in Agriculture

Background:

  • Accurate crop growth monitoring is crucial for agricultural information systems.
  • Soil temperature (ST), soil moisture content (SMC), and soil electrical conductivity (SEC) are key factors influencing tea plantation growth.
  • Real-time monitoring of these soil parameters aids in predicting tea tree health and growth trends.

Purpose of the Study:

  • To develop and evaluate models for monitoring tea plantation growth based on ST, SMC, and SEC.
  • To compare the predictive performance of polynomial and power models, including cumulative parameter models.
  • To introduce and validate an optimized Long Short-Term Memory (LSTM) network for enhanced prediction accuracy.

Main Methods:

  • Construction of five models using polynomial and power functions to correlate soil parameters with tea growth.
  • Development of models based on the sum of soil temperature (SST), sum of soil water content (SSMC), and sum of soil conductivity (SSEC).
  • Implementation and optimization of a Long Short-Term Memory (LSTM) network using the Bald Eagle Search (BES) algorithm.

Main Results:

  • Initial models showed tea plantation growth positively correlated with ST and negatively with SMC and SEC, with the ternary cubic polynomial model performing best.
  • Models incorporating cumulative soil parameters (SST, SSMC, SSEC) significantly improved prediction accuracy, with ternary cubic polynomial models achieving R² values above 0.96.
  • The BES-optimized LSTM network demonstrated superior performance with an R² of 0.8666, outperforming the standard LSTM network.

Conclusions:

  • The study successfully developed predictive models for tea plantation growth, highlighting the importance of soil parameters.
  • Cumulative soil parameter models and the BES-optimized LSTM network offer significant improvements in prediction accuracy.
  • The proposed models provide a scientific basis for optimizing tea production management, agricultural policy, and advancing agricultural modernization.