Incremental and Zero-Shot Machine Learning for Vietnamese Medicinal Plant Image Classification

This paper presents a study on the use of incremental and zero-shot learning for classifying Vietnamese medicinal plants using image analysis. Traditional machine learning methods often struggle with the constant emergence of new plant species and variability in appearances. Our methodology combines incremental learning, which continuously updates the model with new data while retaining prior knowledge, and zero-shot learning, which classifies unseen plant species by leveraging semantic similarities. Evaluated on a unique dataset from Vietnam, our approach shows improved adaptability, robustness, and reduced dependency on extensive labeled data, making it suitable for dynamic environments like medicinal plant identification.