This invention discloses a
machine learning-based method and
system for predicting the
stress level of
rapeseed under low light conditions, belonging to the field of agricultural meteorological disaster early warning technology. It constructs a training sample set by acquiring meteorological time-
series data and agronomic features; builds a
cumulative effect function of
photosynthetically active radiation, and generates physical adversarial negative samples through constraint optimization; constructs a time-series
knowledge graph and uses a time-series graph convolutional network to obtain
graph embedding vectors; constructs a dual-
tower contrastive prediction network to extract meteorological feature vectors and agronomic feature vectors that fuse agronomic static features with
graph embedding vectors; uses a contrastive
loss function to narrow down the meteorological and agronomic features of the same sample; constructs a physical constraint
loss function to impose
physical law penalties; and jointly optimizes and trains to obtain a prediction model, outputting the
stress level. This invention, through physical-guided contrastive learning and a time-series
knowledge graph, solves the problems of low prediction accuracy and poor model
interpretability for
small sample events, and improves cross-regional generalization ability.