Cotton farming industry development and policy finance support for demand estimation in Aksu.

This article focuses on forecasting the growth of the cotton cultivation industry and the demand for policy-based financial support in the Aksu region using an advanced data mining approach. It proposes a novel method combining the Cascade-PSPNET deep learning semantic segmentation algorithm for remote sensing images with nonlinear regression and ARIMAX (autoregressive integrated moving average with exogenous variables) models to improve accuracy in predicting cotton planting area, yield, production value, and corresponding financial support needs. The study addresses limitations in previous forecasting methods by incorporating disaster cycle fluctuations, inflation, and price factors, and demonstrates that its integrated model achieves higher precision than traditional approaches. The results suggest that this methodology can enhance policy finance regulation and risk control in agricultural development, offering a systematic tool for timely and accurate financial support allocation in cotton farming.