Multi-criteria decision support system for the evaluation of UAV intelligent agricultural sensors

Abstract

Precision agriculture is an emerging approach aimed at enhancing agricultural productivity through advanced technological solutions. One of the key technologies integrated into modern agriculture is Unmanned Aerial Vehicles (UAVs), which rely on various sensors to provide critical information about crop fields. However, selecting the most suitable UAV sensors remains a significant challenge due to multiple evaluation criteria and compromises. This paper proposes a novel decision-support framework based on multi-criteria decision-making/analysis (MCDA/MCDM) methods to facilitate UAV sensor selection in precision agriculture. The framework incorporates objective weight selection techniques-Standard Deviation, Entropy, CRITIC, and MEREC-eliminating the need for subjective expert involvement. Furthermore, four MCDA/MCDM methods, including the newly proposed COmbined COmpromise solution with Characteristic Objects METhod (COCOCOMET), are applied to evaluate sensor alternatives. To validate the framework, a case study is conducted using a dataset of UAV sensors, where multiple evaluation criteria are analyzed to determine the most suitable sensor. The results confirm the framework’s effectiveness, demonstrating its robustness and stability in decision-making. Sensitivity analysis and comparative studies further highlight its reliability, particularly in addressing rank reversal issues commonly found in existing MCDA methods such as TOPSIS and AHP. The proposed framework not only provides a structured and adaptable evaluation process for UAV sensors but also offers broader applicability in agricultural decision-making.