A Study on AI in Precision Agriculture: Fuzzy Logic for Crop Disease Prediction

This study explores the efficacy of various AI models in predicting crop diseases, with a particular focuson fuzzy logic, a method renowned for its ability to handle uncertainty and imprecision in agriculturaldata. By comparing fuzzy logic with other AI approaches, including Decision Trees, Random Forests,Support Vector Machines (SVMs), and Convolutional Neural Networks (CNNs), we assess theirperformance based on accuracy, precision, recall, F1 score, and AUC (Area Under the Curve). Ourexperimental results indicate that the fuzzy logic model achieves an accuracy of 85.2%, with a robustbalance between precision (83.5%) and recall (87.0%), and an AUC of 0.88. While the fuzzy logic modelperforms admirably, the Random Forest and CNN models surpass it, with Random Forest achieving thehighest accuracy (87.5%) and CNN the highest performance metrics overall, including an accuracy of89.1% and an AUC of 0.92. This study highlights the strengths of fuzzy logic in managing imprecise dataand suggests that while it is a valuable tool for crop disease prediction, advanced models like CNNsoffer superior performance in handling complex prediction tasks