Paper Title : HERBS AND USAGES PREDICTION USING DEEP LEARNING TECHNIQUES

ISSN : 2394-2231
Year of Publication : 2021

10.29126/23942231/IJCT-v8i3p1

Authors: M.Saravanan, M.Dhanaraj, S.Santhosh, K.Velumani

         



MLA Style: M.Saravanan, M.Dhanaraj, S.Santhosh, K.Velumani " HERBS AND USAGES PREDICTION USING DEEP LEARNING TECHNIQUES " Volume 8 - Issue 3 May-June , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: M.Saravanan, M.Dhanaraj, S.Santhosh, K.Velumani " HERBS AND USAGES PREDICTION USING DEEP LEARNING TECHNIQUES " Volume 8 - Issue 3 May-June , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
Automatic plant image identification is the most promising solution towards bridging the botanical taxonomic gap, which receives considerable attention in both botany and computer community. As the machine learning technology advances, sophisticated models have been proposed for automatic plant identification. Medicinal plants are gaining attention in the pharmaceutical industry due to having less harmful effects reactions and cheaper than modern medicine. Based on these facts, many researchers have shown considerable interest in the research of automatic medicinal plants recognition. There are various opportunities for advancement in producing a robust classifier that has the ability to classify medicinal plants accurately in real-time. In this paper, various effective and reliable machine learning algorithms for plant classifications using leaf images that have been used in recent years are reviewed. The review includes the image processing methods used to detect leaf and extract important leaf features for some machine learning classifiers. These deep learning classifiers are categorized according to their performance when classifying leaf images based on typical plant features, namely shape, vein, texture and a combination of multiple features. And then retrieve the results about usage of herbs with improved accuracy rate. Experimental results show that the proposed system provides improved accuracy rate.

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Keywords
-Botanical Taxonomic Gap, Machine Learning, Pharmaceutical, Image Processing, Herbs