Research on intelligent agricultural decision-making system based on Bayesian optimization

Abstract

Along with social development, the problems of insufficient rural labor and mismatch between labor intensity and economic returns have become a greater obstacle to rural development; therefore, the development of agricultural intelligence to improve agricultural productivity will become the new direction of modern agricultural development. Based on internet technology, intelligent agriculture adopts digital technologies such as intelligent perception, network transmission and big data processing to provide decision basis for agricultural planting, production and pest control, or directly deploy decision information to automated farm equipment, so as to use agricultural resources reasonably and efficiently. However, the development of smart agriculture is constrained by the low accuracy of automation control and decision information. In this paper, we optimize the threshold value of each agricultural production decision data through machine learning algorithm, use Bayesian optimization to learn the agricultural production environment and crop growth data, iteratively optimize the threshold value to get the global best value, improve the accuracy of automation control and reduce the risk of agricultural production decision, and effectively improve the economic returns of agriculture.