Evaluation of Scale Effect of Fragmented Agricultural Land Transfer Based on Neural Network

Abstract

The artificial neural network consists of a large number of neurons interconnected to each other, which simulates the information process mode of human brains. This is a complex network system that carries out parallel processing of information and nonlinear transformation. The agricultural land in China is characterized by fine fragmentation, and people-land relation is tense. In order to promote the intensive development of agriculture in China, this paper takes 31 provinces and cities as research objects and proposes an evaluation model based on adaptive fuzzy neural network (ANFIS) for the scale effect of fragmented agricultural land transfer to overcome the subjectivity of traditional evaluation methods. In order to improve the accuracy of the evaluation, the combination of qualitative and quantitative methods is used to screen the indicators, and the subtraction clustering method is used to obtain the initial fuzzy rules of the adaptive fuzzy neural network. Then, the adaptive fuzzy neural network model can be trained and tested and the convergence results of the model are good. The results of model analysis show that under different resource endowments and production conditions, the scale effect of land transfer in different regions presents significant differences. On this basis, it is proposed that the intensification process of the agricultural land use cannot be blindly promoted, and the land transfer should follow the principle of moderation. Appropriate agricultural production models should be formulated based on different geographical environments.