Bose, P.; DUTTA, S.; Goyal, V.; Bandyopadhyay, S.K. Leaf Diseases Detection of Medicinal Plants based on Image Processing and Machine Learning Processes. Preprints2021, 2021070638. https://doi.org/10.20944/preprints202107.0638.v1
APA Style
Bose, P., DUTTA, S., Goyal, V., & Bandyopadhyay, S.K. (2021). Leaf Diseases Detection of Medicinal Plants based on Image Processing and Machine Learning Processes<strong> </strong>. Preprints. https://doi.org/10.20944/preprints202107.0638.v1
Chicago/Turabian Style
Bose, P., Vishal Goyal and Samir K. Bandyopadhyay. 2021 "Leaf Diseases Detection of Medicinal Plants based on Image Processing and Machine Learning Processes<strong> </strong>" Preprints. https://doi.org/10.20944/preprints202107.0638.v1
Abstract
: On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.
Keywords
Image Processing; Automated Plant Diseases Detection; Histogram Oriented Gradient (HOG); Local Binary Pattern (LBP); Support Vector Machine (SVM)
Subject
Biology and Life Sciences, Plant Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
