International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
Medicinal Plant Detection Using Deep Learning N.S. Chidhvii Reddy1, P. Deepika Reddy 2, S. Pranavi3, Dr. D. Subhashini4 123Student, Electronics and Communication Engineering, MGIT College, Telangana, India
4 Assistant Professor, Electronics and Communication Engineering, MGIT College, Telangana, India
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Abstract - India is known for its incredible plant diversity, especially when it comes to medicinal species. For generations, plants have been at the heart of traditional healing practices like Ayurveda, Siddha, and Unani, offering a wide range of natural remedies. Today, as more people turn to nature-based solutions, medicinal plants are becoming increasingly popular as affordable, eco-friendly, and generally safer alternatives to chemical-based medicines. Still, one of the biggest challenges lies in accurately identifying and classifying these plants. With so many species that often look alike, it’s easy to make mistakes—even for experts. Misidentifying a plant can lead to using the wrong one, which might not only reduce the treatment’s effectiveness but could also be harmful. This challenge highlights the need for a dependable and easy-touse identification method. In response, this research aims to develop intelligent software that can accurately recognize medicinal plants using image processing and advanced deep learning technologies. By combining computer vision with modern neural networks, the project hopes to make plant identification both faster and more reliable. Such a tool could be incredibly helpful—not just for researchers and botanists, but also for everyday users, herbal medicine enthusiasts, and community health workers who rely on the safe and effective use of medicinal plants
plant can lead to ineffective remedies or even pose health risks. To address these challenges, an innovative software solution is being developed that uses image recognition and advanced deep learning techniques. This intelligent tool aims to transform how medicinal plants are identified and used. By analyzing plant images, the system will be able to accurately distinguish between species and provide detailed insights into their health benefits, medical uses, and safety guidelines. At the heart of this platform are powerful deep learning models like ResNet50 and VGG16, trained on large collections of plant images. In addition, the platform will feature a rich database of medicinal plants, offering users easy access to essential information such as active compounds, therapeutic purposes, and usage precautions—all presented in a user-friendly and accessible way.
2. RELATED WORKS 1. L. D. S. Pacifico, L. F. S. Britto, and collaborators [14] emphasize the crucial role that medicinal plants play in healthcare, while also acknowledging the challenges in identifying them accurately due to the sheer number and diversity of species. They point out that many existing recognition systems lack the precision and adaptability needed for reliable identification. To tackle this issue, the team created a specialized dataset focused on capturing key leaf characteristics such as texture and color. They then developed an automated recognition system powered by five different machine learning algorithms. Their most successful model achieved over 97% accuracy, proving that carefully selected visual features can significantly boost identification performance. Their findings demonstrate the powerful synergy between traditional botanical knowledge and modern AI tools, with promising implications for both accurate identification and conservation of medicinal plants.
Keywords- Deeplearning, CNN, Resnet50, VGG16, Softmax, Convolution, Confidence Score,
1. INTRODUCTION India stands out as one of the world’s most biologically rich nations, thanks to its vast and varied plant life. Among this natural abundance, medicinal plants have held a place of importance for centuries, serving as the backbone of traditional healing systems like Ayurveda, Siddha, and Unani. Known for their natural healing properties, these plants are now gaining renewed attention as environmentally friendly and sustainable alternatives to modern pharmaceuticals. However, to truly benefit from these valuable resources, it’s crucial to accurately identify each plant and understand its specific qualities. Despite the wealth of traditional and modern knowledge available, the large number of plant species—many of which look strikingly similar—makes manual identification a slow, difficult, and often unreliable task. Mistakes in recognizing the right
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2. C. Sivaranjani, L. Kalinathan, R. Amutha, and their team [4] addressed the common problem of segmenting plant leaves in photos taken under varying lighting conditions. They introduced an innovative technique using the ExG-ExR index, which blends Excess Green and Excess Red components for
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