Vol.:(0123456789) SN Computer Science (2024) 5:73 https://doi.org/10.1007/s42979-023-02398-5 SN Computer Science ORIGINAL RESEARCH Medicinal Plant Identification in Real‑Time Using Deep Learning Model S. Kavitha 1  · T. Satish Kumar 2  · E. Naresh 3  · Vijay H. Kalmani 4  · Kalyan Devappa Bamane 5  · Piyush Kumar Pareek 6 Received: 18 June 2023 / Accepted: 8 October 2023 © The Author(s) 2023 Abstract Medicinal plants have a long tradition of being cultivated and harvested in India. The Indian Forest is the principal reposi- tory for many useful medicinal herbs. As a result of their critical role in maintaining people's life, medicinal plants have traditionally been the subject of intensive research and consideration. Yet, correctly identifying plants used in medicine is a laborious process that takes a lot of time and expertise. Because of this, a vision-based approach may aid scientists and regular people in the rapid and precise identification of herb plants. Therefore, this research suggests a vision-based smart method to recognize herb plants by creating a deep learning (DL) model. Although there is a wide variety of useful plants, we limit ourselves to just six from the Kaggle database: betel, curry, tulsi, mint, neem, and Indian beech. For each medicinal plant, we collected 500 images. The data undergo a process of resizing and augmentation to increase the sample size. For the fully automatic identification of medicinal leaves, the MobileNet DL model is selected. To determine the model's effectiveness, it must first be trained, then validated, and ultimately tested. The DL model is evaluated using measures including accuracy, precision, and recall. For this reason, the DL model was able to correctly identify medicinal leaves at an accuracy rate of 98.3%. After being thoroughly investigated, the DL model is uploaded to the cloud, and a mobile app is created for the real- time identification of medicinal leaves. To recognize leaf images, the built mobile app accesses the DL model on the cloud. The automated recognition of plants represents an extremely promising option for filling the taxonomic gap and gaining a lot of interest from the fields of botany and machine vision. Keywords Plant · Resize · Augmentation · Deep learning · Accuracy · Mobile application · Cloud · Medicine Introduction Every life on Earth depends on the oxygen that plants pro- duce. By providing oxygen and water, plants of diverse sizes and forms play a crucial role in maintaining the diversity of This article is part of the topical collection “Diverse Applications in Computing, Analytics and Networks” guest edited by Archana Mantri and Sagar Juneja. * E. Naresh naresh.e@manipal.edu S. Kavitha kavitha.s@nmit.ac.in T. Satish Kumar satisht@bmsit.in Vijay H. Kalmani vijaykalmani@hotmail.com Kalyan Devappa Bamane kdbamane@dypcoeakurdi.ac.in Piyush Kumar Pareek piyush.kumar@nmit.ac.in 1 Department of MCA, Nitte Meenakshi Institute of Technology, Bengaluru, India 2 Department of Computer Science and Engineering, BMS Institute of Technology and Management, Bengaluru, India 3 Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India 4 Department of Computer Science and Information Technology, Rajarambapu Institute of Technology, Affiliated to Shivaji University, Kolhapur, Sakharale, Islampur, Maharashtra, India 5 Department of Information Technology, D. Y. Patil College of Engineering, Akurdi, Pune, India 6 Department of AIML and IPR Cell, Nitte Meenakshi Institute of Technology, Bengaluru, India