The invention discloses an
irrigation area multi-
crop growth stage precise
irrigation method based on
deep learning, and relates to the technical field of agricultural intelligent
irrigation, and the method comprises the following steps: pre-constructing a data
collection system, and through a distributed sensor network, a
multispectral image collection module and a data preprocessing unit, carrying out multi-
crop growth stage precise irrigation; acquiring irrigation area soil information, meteorological data and multi-
crop growth indexes in real time, and generating a standardized
data set; and constructing a multiple-input-multiple-output LSTM model fused with the space-time attention mechanism. The
deep learning model and the crop
water demand mechanism are fused, the space-time attention mechanism is combined to accurately capture the
water demand difference of different crops at different growth stages, the
adaptation limitation of a traditional
system on complex terrains and extreme environments is broken through through the
terrain correction and special scene
adaptation algorithm, the irrigation scheme better meets the actual demand, and the
irrigation efficiency is improved. And in cooperation with a
priority scheduling and fault quick response mechanism, the irrigation instruction is ensured to efficiently fall into the ground, and the
automation and
collaboration level of irrigation area management is integrally improved.