The invention discloses a potato
seedling grading environment parameter dynamic optimization regulation and control method, and relates to the technical field of agricultural informationization. Based on an
Internet of Things sensor deployed in a target area, stem
diameter,
chlorophyll content and
plant height data of potato seedlings are collected, the seedlings are divided into weak seedlings, middle seedlings and strong seedlings by adopting K-means clustering, and the weak seedlings, the middle seedlings and the strong seedlings are classified into a weak
seedling classification model, a middle
seedling classification model and a strong seedling classification model; setting the duration of the initial light period; and carrying out gradient photoperiod test on each type of potato seedlings by taking the
stem elongation rate and the
chlorophyll content as constraint conditions. Through grading photoperiod modeling and
ant colony
algorithm dynamic optimization, the limitation of traditional fixed photoperiod regulation and control is broken through, the initial photoperiod range is set based on the seedling physiological difference, and the photoperiod-growth rate response curved surface model is constructed in combination with the
stem elongation rate and
chlorophyll content constraint conditions. The influence of different photoperiod combinations on seedling growth is quantified, accurate matching of photoperiods and seedling requirements is ensured,
excessive growth or premature senility is avoided, and the growth
rhythm stability and the
resource utilization efficiency are improved.