Mushroom Classification of Toxicity Using Deep Learning

Mushroom classification has undergone significant advancements, evolving from traditional methods based on observable morphological traits to modern molecular techniques that offer greater accuracy and reliability.This paper explores the history and development of mushroom taxonomy, emphasizing the limitations of morphology-based approaches, particularly with cryptic species that are morphologically similar but genetically distinct.The shift towards molecular phylogenetics, especially DNA sequencing methods such as the Internal Transcribed Spacer (ITS) region, has revolutionized fungal classification, allowing for a more precise understanding of genetic relationships and the discovery of new species.This study also highlights the role of bioinformatics and genomic data integration in constructing phylogenetic trees, contributing to a comprehensive view of fungal biodiversity.The applications of accurate classification extend beyond taxonomy, impacting fields such as agriculture, medicine, biotechnology, and environmental conservation.The paper concludes by discussing the future of mushroom classification, with promising applications in bioremediation, industrial biotechnology, and medicinal mushroom development.