A Reverse QFD Framework Integrating Interval Type-2 Fuzzy Sets and Regret Theory for Seasonal Demand Analysis in Smart Farmland Abandonment Monitoring Platforms

The abandonment of farmland necessitates the implementation of intelligent monitoring platforms that are tailored to the specific requirements of relevant stakeholders. However, the prevailing solutions in this domain tend to prioritize technical performance over considerations of seasonality. The present study proposes a Reverse Quality Function Deployment (R-QFD) framework integrating interval type-2 fuzzy sets, Regret theory, and grey-entropy TOPSIS to analyze and prioritize user requirements for smart farmland abandonment governance. To address these challenges, the model incorporates interval type-2 fuzzy sets to manage linguistic uncertainty, regret theory to capture behavioral biases, and a feedback-driven R-QFD mechanism for adaptive design. Using data from 204 stakeholders in Jiangxi Province, China, this model revealed distinct seasonal demand fluctuations: land abandonment warnings predominate in spring; online customer service reaches its zenith in summer; land transfer information becomes paramount in autumn; and online service experiences a resurgence in winter. The recommendations made in this study include prioritizing seasonal features, incorporating behavioral feedback loops, and using modular architectures. The present study proposes a replicable, behavior-aware demand analysis model for smart agriculture and associated technology governance.