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Environmental Simulation Model for Rapid Prediction of Tea Seedling Growth

文献类型： 外文期刊

作者： Li, He 1 ; Mao, Yilin 1 ; Wang, Yu 1 ; Fan, Kai 1 ; Shi, Hongtao 2 ; Sun, Litao 3 ; Shen, Jiazhi 3 ; Shen, Yaozong 1 ; Xu, Yang 1 ; Ding, Zhaotang 1 ;

作者机构： 1.Qingdao Agr Univ, Tea Res Inst, Qingdao 266109, Peoples R China

2.Qingdao Agr Univ, Sch Sci & Informat Sci, Qingdao 266109, Peoples R China

3.Shandong Acad Agr Sci, Tea Res Inst, Jinan 276800, Peoples R China

关键词： deep learning; environmental; internet of things; tea seedling growth

期刊名称：AGRONOMY-BASEL （ 影响因子：3.949； 五年影响因子：4.117 ）

ISSN：

年卷期： 2022 年 12 卷 12 期

页码：

收录情况： SCI

摘要： Accurate and effective monitoring of environmental parameters in tea seedling greenhouses is an important basis for regulating the seedling environment, which is crucial for improving the seedling growth quality. This study proposes a tea seedling growth simulation (TSGS) model based on deep learning. The Internet of Things system was used to measure environmental change during the whole seedling process. The correlation between the environmental parameters and the biomass growth of tea seedlings in various varieties was analyzed. A CNN-LSTM network was proposed to build the TSGS model of light, temperature, water, gas, mineral nutrition, and growth biomass. The results showed that: (1) the average correlation coefficients of air temperature, soil temperature, and soil moisture with the biomass growth of tea seedlings were 0.78, 0.84, and -0.63, respectively, which were three important parameters for establishing the TSGS model. (2) For evaluating the TSGS model of a single variety, the accuracy of ZM's TSGS based on the CNN-LSTM network was the highest (Rp(2) = 0.98, RMSEP = 0.14). (3) For evaluating the TSGS model of multiple varieties, the accuracy of TSGS based on the CNN-LSTM network was the highest (Rp(2) = 0.96, RMSEP = 0.17). This study provided effective technical parameters for intelligent control of tea-cutting growth and a new method for rapid breeding.

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