| Estimation of apple transpiration in the Loess Plateau based on an improved long short\|term memory deep learning model |
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| DOI：10.7606/j.issn.1000-7601.2025.06.25 |
| Key Words: apple tree, transpiration estimation deep learning algorithm quantile regression path analysis Loess Plateau |
| Author Name | Affiliation | | XIE Pengcheng | College of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650000, China | | LIU Xiaogang | College of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650000, China | | CUI Ningbo | College of Water Resources and Hydropower, Sichuan University, Chengdu, Sichuan 610000, China | | XING Liwen | College of Water Resources and Hydropower, Sichuan University, Chengdu, Sichuan 610000, China |
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| Abstract: |
| The transpiration of apple trees is a key factor affecting their growth, yield, fruit quality, and water management, and it also plays an important role in climate adaptability, ecological balance, and sustainable agriculture. However, the precise estimation of apple tree transpiration faces many challenges in practical applications, especially in the Loess Plateau of China, where complex climate conditions and soil characteristics make transpiration prediction more difficult. To address the issue of insufficient estimation accuracy of apple tree transpiration under the complex climate and soil conditions of the Loess Plateau, this study proposed a deep learning model combining Quantile Regression, Temporal Convolutional Network, and Bidirectional Long Short\|Term Memory network (QRTCN-BiLSTM), and compared it with traditional empirical models (MLR, MJS, S-W) and classical deep learning models (LSTM, Bi-LSTM). Based on the measured data (meteorological, soil moisture, leaf area index, etc.) from apple orchards in Taigu County, Jinzhong City, Shanxi Province, during 2020-2022, path analysis was used to quantify the dynamic effects of transpiration driving factors. It was found that leaf area index (LAI) and vapor pressure deficit (VPD) at certain stages of the canopy were the dominant factors, with VPD having the most significant impact during the dense canopy stage. The results showed that the QRTCN-BiLSTM model significantly outperforms traditional models in terms of estimation accuracy at all growth stages, with a coefficient of determination (R2) of 0.96 under full input conditions, and a relative root mean square error (RRMSE) reduced by more than 61%. Moreover, the model demonstrated strong robustness to missing input variables. By integrating temporal convolution with quantile regression, the model effectively captured the nonlinear relationships of environmental factors and historical dependence characteristics, providing reliable technical support for precise water management and efficient regional water resource utilization in dryland apple orchards of the Loess Plateau. |
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