Deep Learning-Enhanced IoT System for Real-Time Identification of Ayurvedic Raw Materials

The aim of this study is to design an innovative system that leverages on-chip Internet of Things (IoT) and deep learning technologies for the identification of Ayurvedic raw materials. The system employs an ESP32-CAM module that possesses on-chip processing capabilities to immediately capture high-quality photos on the device, therefore addressing crucial concerns regarding the validity and quality of these materials. These photos are analyzed using robust on-chip deep learning algorithms that were trained on a large dataset of 4,500 photos from 15 different classifications. To increase the accuracy of their input material classification, the models use advanced preprocessing approaches to extract important aspects including form, color, and texture. This method ensures the precise identification of resources, which not only encourages sustainable harvesting techniques but also guarantees the quality and safety of Ayurvedic goods. This approach greatly increases the efficacy and dependability of Ayurvedic therapies by fusing innovative technology with conventional medical procedures. This has a huge positive impact on both practitioners and patients.