Intelligent Crop Recommendation System Utilizing Fuzzy Logic and Bayesian Network Approaches for Dynamic Environmental Adaptation

Accurate crop selection plays a crucial role in improving agricultural productivity, yet traditional decision-making methods often struggle to handle uncertainty arising from varying soil nutrients and climatic conditions. This study aims to develop an intelligent crop recommendation system using a hybrid Artificial Intelligence approach that integrates Fuzzy Logic and a Tree Augmented Naive Bayes (TAN) model. Fuzzy Logic is employed to process continuous and uncertain environmental variables, while the TAN model is used to perform probabilistic crop classification by modeling dependencies among features. The research methodology includes data preprocessing, fuzzification of soil and climate parameters, probabilistic structure learning using the TAN algorithm, and inference for crop recommendation. System implementation and testing were conducted using Python-based simulations with scikit-fuzzy and pgmpy libraries across multiple experimental scenarios. The system is evaluated using the Crop Recommendation Dataset, created by Atharva Ingle and publicly available on Kaggle at https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset. The results demonstrate that the proposed system effectively captures non-linear relationships and uncertainty in agricultural data, achieving an accuracy of 97.73% in recommending 22 different crop varieties, which shows that the hybrid Fuzzy–TAN approach provides a reliable and accurate decision support system for crop selection, offering improved adaptability and performance compared to conventional recommendation methods.