DeepCNNMed: Enhancing Medicinal Plant Identification Through Deep Convolutional and Hybrid Neural Networks

• Published in: Brazilian Archives of Biology and Technology Vol. 68
• Main Authors: Gurusamy, Rajalakshmi, Chandrasekaran, Parameswari, Malu, Yamuna Devi Manickam, Sampath, Madhusudhanan, Abraham, Nesarani
• Format: Journal Article
• Language: English
• Published: Instituto de Tecnologia do Paraná (Tecpar) 01.01.2025
• Subjects: Computer Vision; Fully Connected Neural Network (FCNN); Grey Level Co-occurrence Matrix (GLCM); Medicine Plant Identification; Radial Basis Probabilistic Neural Network (RPNN)
• ISSN: 1516-8913; 1678-4324
• DOI: 10.1590/1678-4324-2025240881

Abstract There are innumerable types of plants, many of which have therapeutic applications. Traditional medicine frequently makes use of medicinal herbs. Accurate identification of medicinal plants would be highly advantageous to the forest service, life scientists, physicians, pharmaceutical companies, governments, the public, and the people who take drugs. Manual methods are quick and accurate for identifying plants, but only subject-matter specialists should use them. However, it does take some time. False positives are possible, and they could not only cause serious problems but also negative consequences. Deep learning techniques are rapidly being used to solve computer vision challenges. In this study, we suggested DeepCNNMed (RPNN + FCNN), an automated system for categorizing medicinal plants that will enable individuals to recognize valuable plant species fast. It is well known that feature extraction and classification are impacted by RPNN + FCNN. The RPNN + FCNN classifier was used to separate the exception properties and classify them, and the resulting DeepCNNMed model exhibits 97.2% accuracy with negligible losses on real-time images.