基于深度强化学习的耕作层土壤水分、温度预测

刘会丹; 万雪芬; 崔剑; 蔡婷婷; 杨义

doi:10.7671/j.issn.1001-411X.202201032

基于深度强化学习的耕作层土壤水分、温度预测

Moisture and temperature prediction in tillage layer based on deep reinforcement learning

摘要

摘要:
目的利用土壤近表面空气温湿度与土壤内部参数的关联关系对耕作层土壤水分、温度进行精准预测，为实现精细化农业种植管理提供服务。

方法针对土壤耕作层水分、温度预测在训练集获取与模型验证等方面的实际需求，设计了基于嵌入式系统及窄带物联网(Narrow band internet of things，NB-IoT)无线通信技术的物联网数据采集系统。在此基础上基于深度Q学习(Deep Q network，DQN)算法探索了一种模型组合策略，以长短期记忆(Long short-term memory，LSTM)、门限循环单元(Gated recurrent unit，GRU)与双向长短期记忆网络(Bi-directional long short-term memory，Bi-LSTM)为基础模型进行加权组合，获得了DQN-L-G-B组合预测模型。

结果数据采集系统实现了对等间隔时间序列环境数据的长时间稳定可靠采集，可以为基于深度学习的土壤水分、温度时间序列预测工作提供准确的训练集与验证集数据。相对于LSTM、Bi-LSTM、GRU、L-G-B等模型，DQN-L-G-B组合模型在2种土壤类型(壤土、砂土)耕作层上水分与温度预测中的均方根误差(Root mean square error，RMSE)、平均绝对误差(Mean absolute error，MAE)、平均百分比误差(Mean absolute percentage error，MAPE)都有一定程度的降低，R²提高了约0.1%。

结论通过该物联网数据采集系统与DNQ-L-G-B组合模型，可以有效地完成基于土壤近表面空气温、湿度对耕作层土壤中水分、温度的精准预测。

Abstract:
Objective To accurately predict the water and temperature of the arable layer using the correlation between soil near surface air temperature and humidity and soil internal parameters, and serve for the realization of fine agricultural planting management.

Method Aiming at the actual needs of soil tillage layer moisture and temperature prediction in training set acquisition and model verification, an internet of things data acquisition system based on embedded system and narrow band internet of things (NB-IoT) wireless communication technology was designed. A model combination strategy was explored based on the deep Q network (DQN) deep reinforcement learning algorithm. Based on the weighted combination of long short-term memory (LSTM), gated recurrent unit (GRU) and Bi-directional long-short term memory (Bi-LSTM), the DQN-L-G-B combination prediction model was obtained.

Result The data acquisition system achieved long-term stable and reliable collection of time series environmental data with equal intervals, and provided accurate training set and verification set data for soil moisture, temperature time series prediction based on deep learning. Compared with models such as LSTM, Bi-LSTM, GRU and L-G-B, the DQN-L-G-B combined model not only lowered the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) in the prediction of moisture and temperature on the tillage layer of the two soil types (loam and sand), but also increased R² by about 0.1%.

Conclusion Through the internet of things data acquisition system and the DQN-L-G-B combined model, the accurate prediction of soil moisture and temperature in the cultivated layer based on soil near surface air temperature and humidity can be effectively completed.

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