Mushroom classification has become an important area of research due to the increasing demand for accurate identification of edible and poisonous mushroom species. Traditional identification methods rely on expert knowledge and morphological characteristics, making the process time-consuming and susceptible to human error. Recent advancements in artificial intelligence, machine learning, and deep learning have significantly improved the accuracy and efficiency of mushroom classification systems. This review presents a comprehensive analysis of conventional and intelligent mushroom classification approaches reported in recent literature. Various image processing techniques, feature extraction methods, and classification algorithms, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), are examined. Publicly available mushroom datasets and evaluation metrics such as accuracy, precision, recall, and F1-score are also discussed. Furthermore, the review highlights current challenges, including dataset imbalance, environmental variations, and real-time deployment issues, while identifying future research opportunities involving explainable artificial intelligence, lightweight deep learning models, and mobile-based mushroom recognition systems. This review provides researchers with a consolidated understanding of recent developments and emerging trends in automated mushroom classification.
