Fuzzy min-max neural network with fuzzy lattice inclusion measure for agricultural circular economy region division in heilongjiang province in china

Abstract

Applying circular economy into agriculture is one of the most efficient methods to implement agricultural sustainable development. Designing different circular economy modes according to local characteristics of various regions can be more efficient. This paper focuses on agricultural circular economy region division in Heilongjiang province in China. First, the specific index system has been constructed. Then, according to these indexes to divide the regions via a novel but more efficient clustering method called Improved Fuzzy min-max neural network with fuzzy lattice inclusion measure (FL-IFMM) proposed in this paper. Heilongjiang province was divided into four agricultural circular economy regions which are respectively Farming(based on grain crops)-animal husbandry dominant region, Farming(based on rice) - animal husbandry dominant region, Vegetable and edible fungi - melons and fruits - animal husbandry dominant region, and Farming - Forestry - Animal husbandry dominant region. Finally, the circular economy modes fitting each region and some detailed policy suggestions have been proposed to help promote agricultural sustainable development in Heilongjiang province.