The application discloses a rape whole
growth cycle monitoring method and
system based on three-dimensional
point cloud reconstruction, relates to the field of intelligent
agriculture and
computer vision technology, collects three-dimensional
point cloud data at multiple key growth periods of rape, constructs a semantic growth graph after pretreatment; the graph is input into a pre-trained
time sequence model, the energy flow among source nodes,
library nodes and channel nodes is simulated based on the energy
dynamic balance principle, the future growth state of the
plant is deduced, and
growth monitoring data is finally output. The application realizes nondestructive monitoring of rape from the
bud stage to the
silique stage through three-dimensional
point cloud technology, and overcomes the problems of low efficiency and strong destructiveness of traditional methods. Through the construction of the semantic growth graph, the organ recognition and segmentation problem is solved. Further, a calculable model is constructed by combining the source-
library theory, photosynthetic product flow and organ competition are simulated, the
biomass is accurately predicted before the
silique forms, and the phenomenon of
abortion is recognized, so that the leap from morphological monitoring to growth mechanism deduction is realized.