MYLPHerb-1: a dataset of Malaysian local perennial herbs for the study of plant images classification under uncontrolled environment - Pushpanathan, Kalananthni; Hanafi, Marsyita; Mashohor, Syamsiah; Fazlil Ilahi, Wan Fazilah (Universiti Putra Malaysia Press, 2022)
Research in the medicinal plants’ recognition field has received great attention due to the need of producing a reliable and accurate system that can recognise medicinal plants under various imaging conditions. Nevertheless, the standard medicinal plant datasets publicly available for research are very limited. This paper proposes a dataset consisting of 34200 images of twelve different high medicinal value local perennial herbs in Malaysia. The images were captured under various imaging conditions, such as different scales, illuminations, and angles. It will enable larger interclass and intraclass variability, creating abundant opportunities for new findings in leaf classification. The complexity of the dataset is investigated through automatic classification using several high-performance deep learning algorithms. The experiment results showed that the dataset creates more opportunities for advanced classification research due to the complexity of the images. The dataset can be accessed through https://www.mylpherbs.com/.