Research on the Relationship between Agricultural Supply Chain Optimization and Farmers’ Income Increase Based on PLS Algorithm

With the implementation of rural revitalization strategy, the development of agricultural supply chain is of great significance in promoting rural economic development and farmers' income growth.Starting from the two directions of both definitions and influencing factors, the evaluation index system of both is determined, followed by proposing the use of entropy weighting method to analyze the evaluation indexes in terms of weighting and measurement.The results are set as explanatory variables, explanatory variables, in addition to supplementing the corresponding control variables, on the basis of which the partial least squares method is used to complete the construction of the regression model.Setting the data source of this research, with the help of this paper's model to explore the mechanism of action between the two in depth.The regression coefficients of the explanatory variables and the explained variables are 0.419, 0.157, 0.216 respectively, while both of them have a positive and significant correlation at the 1% level, i.e., there is a positive correlation between the optimization of supply chain of agricultural products and the increase of farmers' income.Index Terms entropy weight method, partial least squares method, regression model, supply chain optimization I. IntroductionTraditional agriculture is accelerating the transition to a new type of modern agriculture, the biggest obstacle to the sustainable and healthy development of agriculture is not only the quality of agricultural products, but also the circulation problem has become a key factor [1], [2].Agricultural supply chain as a connection between production, circulation and sales of the core link, its operational efficiency and management level directly affects the agricultural producers, processing enterprises, wholesalers and retailers and other aspects of the main body's personal interests [3]- [5].With the accelerated process of agricultural modernization, the traditional agricultural supply chain model has been difficult to adapt to the new period of agricultural development needs.Traditional supply chain information asymmetry, high logistics costs, poor production and marketing interface and other issues, seriously restricting the farmers' income space [6], [7].The digital supply chain has a powerful force of change, which can completely break the limitations inherent in the traditional agricultural product circulation model, thus realizing the rapid circulation and efficient supply of agricultural products [8], [9].However, in the context of the current digital economy, the optimization of the agricultural supply chain faces many challenges and opportunities.On the one hand, the application of digital technology provides the possibility of transparent and intelligent management of the agricultural supply chain, which helps to reduce operating costs, improve response speed, and enhance market competitiveness [10]- [12].On the other hand, how to effectively integrate the resources of all parties, realize the close synergy of all links in the supply chain, as well as how to enhance the flexibility and resilience of the supply chain while guaranteeing the quality and safety of agricultural products have become urgent issues [13]- [15].Therefore, exploring the optimization path of agricultural supply chain supported by intelligent algorithms has important theoretical value and practical significance for promoting the quality and efficiency of agriculture and realizing the sustainable income of farmers.The construction and management of digital supply chain is not only an indispensable key path to promote the modernization of agriculture, but also an important engine to realize the efficient, green and sustainable development of agriculture, for this reason, a large number of scholars take advantage of the advanced digital technology to help the supply chain management of agricultural products towards a brand new stage of intelligence, automation and refinement.Tao, Q., et al. investigated the scheduling optimization of the supply chain of agricultural products under the environment of big data.method, proposing a data mining-based management architecture that incorporates evolutionary algorithms to solve large-scale complex structure supply chain scheduling optimization