A METHOD OF PREDICTING MILK YIELD, TMR FOR ACHIEVING TARGET MILK YIELD OR TMR FOR ACHIEVING MAXIMUM MILK YIELD BASED ON DEEP LEARNING PREDICTION MODEL

摘要

There is provided a method for predicting milk production performed by a livestock farm management server that communicates with the outside through a communication network. The production flow prediction method of the present disclosure includes n data sets (n is an integer of 2 or more) accumulated from a date before the previous p months (p is an integer of 2 or more) to the reference date retroactively from a reference date through the communication network. Receiving- Each data set of the n data sets includes status information data of the cattle to be managed, nutrient intake data of the cattle to be managed, and surroundings based on each specific date between the previous p months and the reference date. Including state data, and applying the received data set based on a first prediction model, it is possible to expect for the management target cattle for q months (q is an integer greater than or equal to 1) from the reference date. And predicting the amount of milk produced. The state information data of the cattle to be managed includes the date of birth or the age of the cattle to be managed, and postpartum parking on the specific date, and the nutritional intake data of the cattle to be managed includes daily dry matter intake, water intake, and Metabolic energy intake, metabolic protein intake, MET intake, LYS intake, calcium intake, and phosphorus intake are included, and the ambient state data includes average temperature and average humidity information on the specific date.