Enhancing Agricultural Diagnostics through Linear Diophantine Multi-Fuzzy Soft Matrices with Lattice Implementation

Abstract

A sophisticated conceptual process that is essential for system analysis and farming practice decision-making is agricultural modeling. Since, agricultural processes are rife with uncertainties and often contain untrustworthy information, applying fuzzy set theory is a beneficial strategy. A key component of improving agricultural systems is fuzzy modeling, which is renowned for producing ideal solutions. The idea of the Lattice-ordered Linear Diophantine Multi-Fuzzy Soft Matrix (LLDMFSM) and its mathematical modeling, especially suited for agricultural diagnostics, are the main topics of this paper. In addition to discussing the basic characteristics of LLDMFSM, the mean operators that apply to this novel matrix are also discussed. One unique aspect of our methodology is the use of the min-max composition strategy to handle an example model that includes multiple factors impacting agriculture. Notably, farmers can use our suggested methodology as a useful tool to help them decide which course of action is best for their farming endeavors. This work adds to the expanding research data on agricultural diagnostics by highlighting the usefulness and advantages of the LLDMFSM in assisting with well-informed agricultural decision-making.