Open AccessImplementation of Deep Dictionary Learning and Coding Network for Plant Disease Detection in Agricultural Field
Author(s) -
Snehal A. Lale,
Vijaya K. Shandilya
Publication year - 2021
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit2173116
Subject(s) - computer science , artificial intelligence , deep learning , neural coding , coding (social sciences) , task (project management) , field (mathematics) , machine learning , set (abstract data type) , artificial neural network , natural language processing , mathematics , engineering , statistics , systems engineering , pure mathematics , programming language
A web based online application is built to detect the diseases present on plant leaves. This will help farmer/end user to find out which diseases are present on that leaves. In older times, this task requires much time & resources to do the detection. A approach has of DDLCN has been taken in this project which will help to find out the best match features. In this approach. I have taken up to 3 layers of DDLCN which will filter the most match features of the diseases present on plant leaves. The proposed DDLCN combines the two things that is deep learning & dictionary learning. Deep learning which the branch of AI. It has a structures inspired by the human brain. It has the artificial neural networks which helps to train the data. Now taking about the dictionary learning which has a literal meaning having large number of data set. Dictionary learning is also called as sparse representation which will help to arrange the data in proper manner and without wasting the storage. Because it will only count the non zeros numbers present in the sparse matrix.