A Trace to Median Index Based Fuzzy Decision Making Technique for Weed Management in Agricultural Systems

Abstract

Weeds pose a significant challenge to environmental sustainability and agricultural productivity, contributing to land degradation and reduced forest health. Reliance on chemical herbicides for weed control has raised serious concerns for food safety and public health, and has necessitated the development of sustainable weed management strategies. To identify viable weed-eradication techniques, this study introduces a novel hybrid fuzzy multi-criteria decision-making (F-MCDM) approach. Numerical illustration of the proposed framework is shown through a case study to demonstrate strong practical applicability. A total of eight contradictory criteria are used to evaluate the sustainability of the alternatives. To effectively balance subjective expert opinions and objective data, the significance of each criterion is thoroughly assessed using the integration of factor relationship analysis (FARE) and criterion impact loss assessment (CILOS). With a weight of 0.2507, weed density was shown to be the most significant factor, outweighing factors like cost and time consumption. The designed fusion model applied the trace to median index method to rank alternatives (RATMI), with precision weed control emerging as the optimal solution in a t-spherical probabilistic hesitant fuzzy (t-SPHF) environment. By targeting only the specific areas where weeds are present, precision techniques which includes smart machines and autonomous robots decrease chemical runoff and the risk of harming beneficial plants and organisms, ultimately leading to healthier ecosystems. The model’s reliance on criterion weights was supported by sensitivity analysis, and comparative analysis demonstrated the t-SPHF set-based method’s resilience in managing intricate, multi-criteria scenarios. This framework for hybrid decision-making is a dependable resource for directing decisions on sustainable weed control, with possible consequences for agricultural policy and practice. Future research may delve deeper into its utilization in various environmental scenarios.

Keywords

• Agricultural systems
• multi-criteria decision making
• sustainability
• t-spherical probabilistic hesitant fuzzy set
• weed eradication