optimizing farmer selection through neutrosophic soft matrix-based decision making

Complex decisions in varying domains necessitate making sense of information that is uncertain,deficient, or internally contradictory. the neutrosophic soft set (nss) theory fulfills this need byphysically modeling truth, indeterminacy, and falsity. this research investigates nss and theirrepresentation via a matrix, neutrosophic soft matrices (nsm), with emphasis on the operationsand comparatives indices of score, certainty, and accuracy. a series of aggregation operators aredeveloped to combine neutrosophic evidence, which support the development of a neutrosophicmulti-criteria decision-making (mcdm) method based upon traditional decision rules.academic experience is used to illustrate the methodology applied to an agricultural context forevaluating farmers based upon attributes like crop yield, soil condition, use of fertilizers, and pestcontrol. analysis shows that use of nsm facilitates systematic evaluation and selection of a bestalternative in the presence of imprecision and uncertainty. in summary, this research supports theidea that neutrosophic models can be useful methods for decision analysis in complicated realworld situations.