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Sensing the content of macronutrients in the agricultural soil is an essential task in precision agriculture. It helps the farmers in the optimal use of fertilizers. It reduces the cost of food production and also the negative environmental impacts on atmosphere and water bodies due to indiscriminate dosage of fertilizers. The traditional chemical-based laboratory soil analysis methods do not serve the purpose as they are hardly suitable for site specific soil management. Moreover, the spectral range used in the chemical-based laboratory soil analysis may be of 350-2500 nm, which leads to redundancy and confusion. Developing sensors based on the discovery of spectral wavebands that respond to soil macronutrient concentrations, on the other hand, is an innovative and successful technology since the results are dependable and timely. The goal of this article is to use a supervised neuro-fuzzy based dimensionality reduction approach in the sensor development process to determine sensitive wavebands of soil macronutrients. Accordingly, the spectral signatures of the soil are collected in an outdoor environment and mapped with its macronutrient concentrations. In this spectral analysis, the spectral reflectance of 424 wavelengths has been obtained and these wavelengths are evaluated through combined and individual modes as well. Appropriate wavelengths are selected in each case by minimizing the fuzzy reflectance assessment index. The effectiveness of these selected wavelengths in each mode is validated by modeling the relation between the reduced reflectance space and each macronutrient concentration using Partial Least Squares Multi Variable Regression (PLS-MVR) method. Set of optimal wavebands are identified and the results are compared with the existing systems.