Volume & Issue no: Volume 10, Issue 3, May - June 2021
____________________________________________________________________________________________________
Title: |
Rose Leaf Disease Detection using Digital Image
Processing & Deep Learning
|
Author Name: |
Varsha J. Sawarkar, Dr. Seema Kawathekar |
Abstract: |
Abstract—Rose plant is used to process for a research in
this paper.Leaf disease detection is the input for to
prevent the losses in the farming and also the product.
Diseases decrease the efficiency of plant, which restricts
the plant growth and also loss the quality and quantity. In
this paperthe approach is to the progress of rose leaf
disease detection model that is based on basic image
classification, by the use of deep CNN. For detection on
rose leafs we used here the image processing and deep
learning techniques. Deep learning is the exact and
precise model for the plant disease detection. Infected
leaves are collected and labeled as per the diseases finding
on it. Processing of taken image is performed along with
the pixel wise operation to get better the image
information. Extracting the features and fit into the
neural network. By the detection with CNN in image
processing is the success for representing the possibility of
this approach in the category leaf disease detection.
Keywords—Convolutional neural networks, deep learning,
Image processing, Plant disease, Rectified Linear Units |
Cite this article: |
Varsha J. Sawarkar, Dr. Seema Kawathekar , "
Rose Leaf Disease Detection using Digital Image
Processing & Deep Learning
" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 10, Issue 3, May - June 2021 , pp.
001-004 , ISSN 2278-6856.
|
| Full Text [PDF] Back to Current Issue | |
NOTE: Authors note that paper cannot be withdrawn at any condition once it is accepted. The Team of IJETTCS advise you, do not submit same article to the multiple journals simultaneously. This may create a problem for you. Please wait for review report which will take maximum 01 to 02 week.