Identification of Medicinal Leaves and Recommendation of Home Remedies using Machine learning - Kale, Swati G.; Jain, Sachin; Rangari, Sudhir; Dharmik, R. C.; Gardi, Manish; Lande, Vipul S. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In today’s era there is a growing need for programmed medical assistance systems. Many medical assistance systems are available for recommendation of allopathic medicine but ayurvedic medicine identification and assistance system is still unexplored. However, with the growing urbanization and diminishing knowledge of traditional practices, the accurate identification of ayurvedic leaves and the appropriate recommendation of home remedies and medicines have become challenging. To address this issue the proposed system presents state-of-art deep learning algorithms to identify the ayurvedic leaf based on images. This system also helps in recommendation for ayurvedic medicine based on symptoms of patient along with some suggested home remedies. This study involves the development of an Android-based application aimed at classifying medicinal leaves and identifying diseases. The dataset comprises 6,541 images representing 115 distinct species of medical leaves. To achieve leaf classification, well-known pre-trained neural networks like Convolutional Neural Networks (CNN), VGG16, MobileNet, and Inception are utilized. Furthermore, disease identification is a part of this research, which involves a dataset encompassing 35 symptoms and 20 diseases.The conventional approach of splitting the dataset into training and testing subsets is employed, a practice that not only assists in training the model but also in evaluating its performance without overfitting. To evaluate the system’s performance comprehensively, diverse measurement parameters are implemented. These parameters encompass widely used evaluation metrics such as accuracy, precision, recall, F1-score, as well as confusion matrices. This array of metrics enables a thorough understanding of the model’s effectiveness and its ability to classify different classes accurately.