MTJNet: Multi-task joint learning network for advancing medicinal plant and leaf classification

Abstract

Identifying and classifying medicinal plants is crucial for pharmaceutical research, traditional medicine, and biodiversity conservation. However, manual classification is time-consuming, error-prone, and requires expert knowledge. This problem statement highlights the need for an automated, efficient, and accurate method by which to distinguish between different species and ensure the quality and safety of medicinal plant leaves. The state-of-the-art methods employ only leaf or plant images for the classification; however, these methods work in particular scenarios (only leaf or plant). In this study, we first integrate the dataset of medicinal plants with corresponding leaves to develop a generalizable model and then apply pre-processing techniques (using vein morphometric features and Sobel edge detection) to highlight the significant characteristics in the images. Then, we propose a novel multi-task joint learning network (MTJNet) for classifying medicinal plant leaves. The proposed MTJNet includes local and global feature extractors that retrieve prominent features for robust classification. Further, we blend the features extracted from the local and global networks to enhance the contextual features. Last, we assign the embedded features to dense layers for additional feature extraction and classification. To evaluate the proposed MTJNet, we employ an Indian medicinal leaf dataset and achieve a precision of 99.60%, a recall of 99.62%, an accuracy of 99.71%, and an F1 score of 99.58%. The experimental results show that the MTJNet statistically defeats prevalent models, thereby proving its effectiveness for medical and industrial applications.