The invention discloses a farmland precise
irrigation decision-making method based on fusion of a physical process and
machine learning, and the method comprises the following steps: S1, data collection: collecting meteorological data, soil data, fertilization and
irrigation data and cotton
growth data in a cotton growth period, S2, data preprocessing: carrying out the cleaning,
standardization and normalization
processing of an
original data set, and carrying out the calculation of the
data set; the method comprises the following steps: S1, preprocessing a
crop mechanism model, S3, data enhancement: constructing and calibrating the
crop mechanism model by using preprocessed data, S4, selecting key feature variables, and dividing the obtained
data set into a
training set and a
test set by using an
original data set and an enhanced
data set, S5, model training and optimization, and S6, model
verification and evaluation. According to the method, by
coupling the
crop growth mechanism model and the
machine learning
algorithm, not only can the growth conditions of crops under different
moisture conditions be simulated and predicted, but also the influence of
soil salinity and
alkalinity on
crop growth can be evaluated, so that comprehensive decision support is provided for
irrigation management.