A Review of Plant Classification Using Deep Learning Models

Plant classification using deep learning models has witnessed significant progress in recent years, contributing to advancements in agriculture, environmental monitoring, and biodiversity conservation. This paper comprehensively analyzes the state-of-the-art deep learning models used for plant classification. This paper discusses various architectures, along with hybrid models and ensemble approaches. It examines the challenges and limitations of existing plant classification datasets and addresses the issues of data scarcity, overfitting, and interpretability. Furthermore, this paper explores the practical applications of deep learning in plant classification, such as agriculture, disease detection, and environmental monitoring. Finally, it outlines future directions, including multimodal data integration, explainable models, real-time classification, and ethical considerations. This comprehensive review will equip researchers and practitioners with valuable insights into the current landscape of plant classification using deep learning models, fostering further advancements in the field.