The invention discloses a method for predicting and judging the
diarrhea risk grade of suckling piglets, and belongs to the technical field of piglet
epidemic disease prevention and control. Aiming at the problems that traditional piglet
diarrhea prediction depends on manual observation, multi-dimensional data relevance is insufficient, early warning lags and the like, a multi-parameter monitoring device is arranged in a piglet breeding environment to collect environment data in real time, and an
excretion behavior detection module is used for triggering an excrement image collection device to obtain a high-quality image; and inputting multi-
source data into a
random forest model for
risk level prediction by combining regular weight measurement and blood detection in a
specific time period after environment adjustment. The
system dynamically adjusts pig environment parameters according to a prediction result, after spraying, heating compensation is started to maintain
humidity and control
ammonia concentration, and closed-loop
feedback regulation is formed. According to the method, through multi-
modal data fusion and model iterative optimization, early-stage accurate early warning of
diarrhea risks is realized, the method is suitable for
intelligent management of a large-scale pig farm, the diarrhea occurrence rate can be effectively reduced, and the breeding benefits are improved.