An accurate irrigation volume prediction method based on an optimized LSTM model.

ABSTRACT: Precise prediction of irrigation volumes is crucial in modern agriculture. This study proposes an optimized long short-term memory (LSTM) model-based irrigation prediction method that combines bidirectional LSTM networks. The model provides farmers with more precise irrigation management decisions, facilitating optimal utilization of water resources and effective crop production management. This proposed model aims to fully exploit spatio-temporal features and sequence dependencies to enhance prediction accuracy and reliability. We aim to fully leverage crop irrigation volumes' spatio-temporal features and sequence dependencies to improve prediction accuracy and reliability. First, this study adopts a bidirectional LSTM (BiLSTM) model to simulate the temporal features of irrigation volumes

SUBMITTER: Yan H 

PROVIDER: S-EPMC11636991 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature