Identifying Medicinal Plant Diseases using Image Processing and Deep Learning Techniques
Amruta K. Jadhav
, Pravin Yannawar , Shivraj Marathe
Medicinal Plants, Machine Learning, Leaf Identification, Classification
Ayurveda unquestionably carries considerable income to India by unfamiliar trade through the fare of ayurvedic prescriptions. Plants' diseases cause significant creation and financial misfortunes in the horticultural and medicinal ventures around the world. Medicinal plants are acquiring consideration in the drug business due to having less destructive impacts responses and less expensive than present day medication. There are different freedoms for headway in delivering a strong classifier that can group medicinal plants precisely progressively. Checking of health and recognition of diseases in plants and trees is a basic issue. This paper presents a strategy for the identification of diseases in medicinal plants based on some significant features removed from its leaf images. The main piece of exploration on a plant disease to distinguish the disease based on CBIR (content-based image retrieval) that is principally worried about the precise identification of diseased medicinal plants. This paper presents a methodology where the plant is recognized based on its leaf highlights, for example, shading histogram and edge histogram. Vigilant edge recognition is likewise valuable to track down the solid edges of leaf of plants and that is utilized to draw the edge histogram which is one of the boundaries for testing. In this paper, different successful and dependable machine learning calculations for plant classifications utilizing leaf images that have been utilized lately are investigated. The survey incorporates the image handling strategies used to identify leaf and concentrate significant leaf features for some machine learning classifiers. These machine learning classifiers are arranged by their presentation when grouping leaf images based on commonplace plant highlights, in particular shape, surface, and a mix of various highlights. The leaf data sets that are freely accessible for programmed plants acknowledgment are investigated too and we finish up with a conversation of conspicuous continuous exploration and openings for improvement around here.
"Identifying Medicinal Plant Diseases using Image Processing and Deep Learning Techniques", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.6, Issue 7, page no.109 - 113, July-2021, Available :https://ijsdr.org/papers/IJSDR2107019.pdf
Volume 6
Issue 7,
July-2021
Pages : 109 - 113
Paper Reg. ID: IJSDR_193459
Published Paper Id: IJSDR2107019
Downloads: 000347237
Research Area: Engineering
Country: Aurangabad, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
