期刊
PLANTS-BASEL
卷 11, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/plants11030262
关键词
crop growth model; soil indicators; normalized difference vegetation index (NDVI); long short-term memory (LSTM)
资金
- 2018 Guangxi University High-Level Innovation Team and Excellence Scholars Program (Guijaioren (2018)) [35]
- 2021 Guangdong Province Modern Agricultural Key Technology Model Integration and Demonstration and Promotion Project, Guangdong Province Science and Technology Special Funds (Big Special + Task List) Project [2020020103]
- Guangdong Province Education Department Special Innovation Class Project [2019KTSCX013]
- Key-Area Research and Development Program of Guangdong Province [2019B020214003]
Crop growth monitoring is important for agriculture, and soil temperature, soil moisture content, and soil electrical conductivity play a key role in crop growth. In this study, different models were used to monitor the relationship between soil parameters and tea plantation growth. The experiments showed that the cubic polynomial model was the best, and using the sum of soil parameters improved prediction accuracy. The optimized LSTM network prediction model also achieved better performance. These models can predict the actual situation during tea leaf growth and provide support for agricultural modernization.
Crop growth monitoring is an important component of agricultural information, and suitable soil temperature (ST), soil moisture content (SMC) and soil electrical conductivity (SEC) play a key role in crop growth. Real-time monitoring of the three soil parameters to predict the growth of tea plantation helps tea trees grow healthily and to accurately grasp the growth trend of tea trees. In this paper, five different models based on the polynomial model and power model were used to construct the soil temperature, soil water content and soil conductivity and tea plantation growth monitoring models. Experiments proved that tea plantation growth were positively correlated with ST and negatively correlated with SMC and SEC, and among the constructed models, the ternary cubic polynomial model was the best, and R square (R-2) of the constructed models were 0.6369, 0.4510 and 0.5784, respectively, indicating that SEC was the most relevant to tea plantation growth maximum. To improve the prediction accuracy, a model based on sum of soil temperature (SST), sum of soil water content (SSMC) and sum of soil conductivity (SSEC) was proposed, and the experiments also showed that the ternary cubic polynomial model was the best, with 0.9638, 0.9733 and 0.9660, respectively. At the same time, a model incorporating three parameters such as soil temperature, soil water content and soil conductivity was also suggested, with 0.6605 and 0.9761, respectively, which effectively improved the prediction accuracy. Validation experiments were conducted. Twelve data sets were utilized to verify the performance of the model. The experiments showed that the regressions in the polynomial models achieved a better prediction effect. Finally, a long short-term memory (LSTM) network prediction model optimized by the bald eagle search algorithm (BES) was also constructed, and R-2, root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of prediction were 0.8666, 0.0629, 0.0040, 0.0436 and 10.5257, respectively, which significantly outperformed the LSTM network and achieved better performance. The model proposed in this paper can be used to predict the actual situation during the growing period of tea leaves, which can improve the production management of tea plantations and also provide a scientific basis for accurate tea planting and a decision basis for agricultural policy formulation, as well as provide technical support for the realization of agricultural modernization.
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