%0 Journal Article
%T 基于LSTM的作物需水量预测模型研究<br>Research on Forecast Model of Crop Water Demand Based on LSTM
%A 孙博瑞
%A 蒋敏
%A 薛山
%J Hans Journal of Agricultural Sciences
%P 69-76
%@ 2164-5523
%D 2022
%I Hans Publishing
%R 10.12677/HJAS.2022.121010
%X <div style=\"text-align:justify;\">
	<span style=\"font-size:14px;\">为了提高作物需水量的预测精度，提出基于LSTM (长短时记忆网络)的作物需水量预测模型。模型以空气温湿度、风速、日照时数为特征输入值，作物需水量为标签输出值，以枣树为试验对象，进行了相关预测试验，并将预测结果与RNN模型，FCNN模型的准确性进行了对比分析，结果表明LSTM预测模型的精确程度更高，该方法在节水灌溉领域具有一定的研究意义。<br />
In order to improve the prediction accuracy of crop water demand, a crop water demand prediction model based on LSTM (Long Short-term Memory Network) is proposed. The model uses air temperature and humidity, wind speed, and sunshine hours as the characteristic input values, and the crop water demand is the label output value. Jujube tree is the test object, related prediction experiments are carried out, and the prediction results are compared with the accuracy of the RNN model and FCNN model. The results show that the accuracy of the LSTM time series model is higher. This method has advantages in the field of water-saving irrigation. </span> 
</div>
%K 预测精度，作物需水量，LSTM预测模型，节水灌溉<br>Prediction Accuracy
%K Crop Water Demand
%K LSTM Prediction Model
%K Water-Saving Irrigation
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=48270