基于物联网和Deep-LSTM的茶树净光合速率动态预测模型

Dynamic prediction model for the net photosynthetic rate of tea plants based on Internet of Things and Deep-LSTM

茶树的光合作用是其基本生理过程之一,快速评估其光合作用速率能够为茶树的水分控制提供重要依据。该研究构建了茶树物联网环境信息监测系统,通过设置100%、85%、70%、55%土壤持水量的4组水分胁迫梯度,实现对茶树生长环境和生理参数的采集,建立了茶树水分胁迫指数(Crop Water Stress Index,CWSI)模型以量化茶树的水分胁迫程度,并研究茶树净光合作用速率(NetPhotosyntheticRate,Pn)的变化特点。在此基础上,构建了基于物联网和深度长短期记忆(Deep Long Short-term Memory,Deep-LSTM)的茶树净光合作用速率动态预测模型,将不同水分处理下的茶树生长环境参数、冠层温度和CSWI作为输入特征,构建多层LSTM单元形成深度LSTM网络,实现特征提取,并引入全连接层实现降维,对茶树在不同水分胁迫程度下的Pn进行预测,计算模型的均方根误差(RMSE)和决定系数(R^(2))以评估其性能表现,并与经典的反向传播神经网络(Back Propagation Neural Network,BPNN)模型进行了性能对比。结果表明,茶树物联网环境信息监测系统能够有效获取其环境参数。茶树冠气温差下限与饱和水汽压差的线性方程拟合度为0.866。Deep-LSTM模型对100%、85%、70%、55%土壤持水量的水分处理下的光合作用速率的预测的RMSE分别为0.304、0.280、0.157和0.160μmol/m^(2)·s;其R^(2)分别为0.846、0.875、0.893和0.954,而BPNN模型的RMSE分别为0.980、0.897、0.633、0.417μmol/m^(2)·s,R^(2)分别为0.516、0.355、0.315、0.432,表明Deep-LSTM模型能够有效预测茶树的Pn,同时其性能好于BPNN模型。该研究可为快速评估茶树光合作用速率提供可行的方法,并为利用水分胁迫和光合作用指定茶树亏缺灌溉策略提供数据支持。

Tea plant has been cultivated as one of the perennial cash crops for years.The water condition of the tea plant has also posed an important influence on the growth and yield.Too much water can lead to the waste of water resources,while less water can produce water stress,indicating a negative impact on the development of plants.Since photosynthesis is one of the basic physiological processes,it is crucial to evaluate the water status of the tea plant using the net photosynthetic rate(Pn),further to provide the data supports for the water control.In this study,four water treatments of the tea plant were carried out to observe the Pn in the field for about five months.An environmental monitoring platform was also constructed for the tea plant to measure the soil moisture,air temperature,air humidity,canopy temperature,and photosynthetically active radiation(PAR)parameters using the Internet of Things(IoTs)and FreeRTOS operating system.The difference of canopy and air temperature(?T)upper and lower limit equations was applied to calculate the empirical crop water stress index(CWSI),in order to quantify the water stress degree of the tea plant.The P;data of the tea plant during the experiment was measured every day to obtain the original one.A dynamic P;prediction model of the tea plant was then established to name the deep long short-term memory(Deep-LSTM).The environmental parameters,canopy temperature,and CWSI of water treatments were integrated as the input features to predict the P;under various water stress.A classic back propagation neural network(BPNN)model was also referred to evaluate the performance of the new model,compared with the root mean square error(RMSE)and determination coefficient(R^(2)).The results showed that the environmental monitoring system of the tea plant performed better to collect the parameters.The linear fitting equation between the lower limit equation of?T and vapor pressure deficit(VPD)was y=2.7-2.13x,where the R^(2) was 0.866.The mean CWSI values of four water treatments were 0.251,0.437,0.621,and 0.858 with 100%,85%,70%,and 55%soil field capacity(SFC),respectively.The special photosynthesis system was used to measure the Pn values under the four water stress in the long-term experiment.The statistical analysis of photosynthesis data showed that the mean Pn values of water treatments were 4.66,4.15,3.40,and 2.61μmol/m^(2)·s,respectively,indicating closely related to the water stress degree.The Pn values of the T2 group were only reduced by 10.9%,compared with the T1 group,while the water supply decreased by 15%.The water stress was used to provide the reference for water-saving irrigation.The Deep-LSTM model performed better to predict the Pn of water treatments,compared with the BPNN.The RMSEs of Deep-LSTM in water treatments were 0.304,0.280,0.157,and 0.160μmol/m^(2)·s,respectively,while the RMSEs of BPNN model were 0.980,0.897,0.633,0.417μmol/m^(2)·s,respectively.The R^(2) values of the Deep-LSTM model were 0.846,0.875,0.893 and 0.954,respectively,and the R^(2) values of BPNN model were 0.516,0.355,0.315,and 0.432,respectively.It infers that the Deep-LSTM model presented better to accurately predict the Pn values of the tea plant.This finding can provide a reliable reference for the water-saving irrigation and water-stress strategy of the tea plant.The water stress was also quantified to construct the photosynthetic rate model,thereby rapidly estimating the Pn values of the tea plant with the IoTs system.Therefore,clear scheduling can be made for the deficit irrigation,particularly for the water-saving of the tea plant.