The present application belongs to the field of
machine vision and
data analysis, and proposes a complex bacterial
community effect identification method for detection of
leaf disease spot expansion inflection point, specifically: first, after applying the complex bacterial
community, daily
image acquisition is performed to obtain leaf images, the leaf images are preprocessed to obtain comparison images, then the comparison images are converted into
disease feature vectors, including
disease spot expansion boundary intensity and
chlorosis index, finally, through inflection point analysis of the
disease feature vectors, an inflection point factor is obtained, and whether the complex bacterial
community is effective is dynamically judged according to the inflection point factor. Quantitative interpretation of the occurrence trend of the disease spot from slow rise to short-term transition expansion provides a unified reference index for subsequent screening and sequencing of the complex bacterial community scheme,
inference of the supplement window and statistical comparison of the difference in the
effective time node, improves the timeliness and accuracy of the identification of the complex bacterial community effect state, and provides efficient and accurate theoretical support for the screening and effect mechanism research of the complex bacterial community for
leaf disease spots.