Identification Of Medicinal Plants And Disease Detection Through Image Processing Using Machine Learning Algorithms

NANDINI R; CHANDANA K; VINUTHA V; REDDY SREYA; DR. ANAND RAJ SP

doi:10.71097/IJSAT.v16.i2.4816

Identification Of Medicinal Plants And Disease Detection Through Image Processing Using Machine Learning Algorithms

Author(s)	NANDINI R, CHANDANA K, VINUTHA V, REDDY SREYA, DR. ANAND RAJ SP
Country	India
Abstract	The accurate identification and disease detection of medicinal plants are essential for advancements in pharmacology, precision agriculture, and biodiversity conservation. This research presents an intelligent, image-based system that integrates advanced image processing techniques with machine learning (ML) algorithms to automate the identification and health assessment of medicinal plants. High-resolution images undergo pre-processing—including noise reduction and normalization—followed by the extraction of morphometric features using Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Scale-Invariant Feature Transform (SIFT), and spectral colour histograms. These features are processed through optimized ML models such as Convolutional Neural Networks (CNNs), Random Forests, and Support Vector Machines (SVMs), validated using metrics like ROC curves, confusion matrices, and F1 scores. To enhance usability and scalability, the platform includes modules such as Disease Detection for foliar symptom analysis, My Garden for personalized plant tracking, and AI Assistant for interactive support using NLP and medicinal plant ontologies. Additionally, a Community module encourages collaborative dataset expansion, while the Remedies and Disease Library modules translate identification outputs into actionable knowledge rooted in ethnobotanical data. Leveraging transfer learning and cloud integration, the system supports regional adaptability and real-time performance. Aligned with the United Nations Sustainable Development Goals (SDGs), this multidimensional framework bridges traditional knowledge with digital innovation to foster sustainable agro-ecological practices, promote biodiversity, and support eco-centric development.
Keywords	Medicinal Plant Identification, Image Processing, Machine Learning, Morphometric Analysis, Disease Detection, Feature Extraction, Convolutional Neural Networks (CNN), Ethnopharmacology.
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-05-11
DOI	https://doi.org/10.71097/IJSAT.v16.i2.4816
Short DOI	https://doi.org/g9kc7c

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Identification Of Medicinal Plants And Disease Detection Through Image Processing Using Machine Learning Algorithms

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