Enhancing Agricultural Monitoring: Integrating ANP with QFD for the Design of a Smart Agriculture Tracking System

Abstract Monitoring agriculture over large areas is crucial for improving crop management practices and maximizing yields. On the other hand, current technologies continue to fail at high-level integration of user needs and technical capabilities, resulting in more complexity and dissatisfaction on the user’s end. The study fills this gap by offering a Smart Agriculture Tracking System framework based on QFD and ANP. This process includes evaluating consumer needs via surveys and interviews, determining technical characteristics, and developing a House of Quality (HoQ) matrix. Weighted ANP analysis was performed to prioritize these needs, and the results suggest that accuracy in sensor readings, reliability in scheduling notifications, and convenience of use are the top priority for users. The study demonstrates that the proposed architecture removes constraints for other similar systems by boosting functionality and usability. In simple terms, the Smart Agriculture Tracking System delivers practical improvements in the range of agricultural monitoring by aligning user needs with relevant technical characteristics, which impacts the way crops are handled, and productivity is increased.