FUZZY LOGIC BASED SYSTEM FOR INTELLIGENT CROP RECOMMENDATION USING MULTI-PARAMETER SOIL ANALYSIS

Abstract

Inappropriate crop selection remains a major global challenge in Sustainable agricultural productivity due to soil variability. This study proposes Fuzzy Inference System (FIS) for soil-based crop recommendation using key soil parameters like Nitrogen (N), Phosphorus (P), Potassium (K), soil pH, and Organic Carbon (OC).  Fuzzy logic is apply to soil nutrient values to convert into linguistic variables such as low, medium, and high. For each input variable, the triangular membership functions are defined and the output variable represents crop suitability including pulses, rice, maize, wheat, cotton, soybean and sugarcane. The Python program is used and output is expressed as percentage suitability for each crop, to help the stakeholders for crop planning rather than generating a single deterministic recommendation.