LEAF DISEASE IDENTIFICATION USING DEEP LEARNING
Author(s):
Navya shree K N, Sindhu K V, Spoorti G Shirbur, Vanitha BM
Keywords:
Leaf disease, machine learning, deep learning, depth wise convolution, point wise convolution.
Abstract
Tomato is one of the most important agricultural commodities in the world with a number of cuisines scattered across the globe, which are incomplete without it. In developing countries like India, tomato has spurred agriculture driven growth in the past century, when export of agricultural produce was the major source of foreign exchange. At times, the prices face a blow from the demand side, while at times facing drastic conditions on the supply side, owing to which, the prices of the commodity have seen a drastic fall. In such years, farmers often cannot afford the services of agricultural consultants for tasks such as detection of leaf diseases and addressing them at the earliest. The solution we have proposed, is a low cost system that uses image processing to detect leaf diseases in the leaves of tomato plants to make things easier for both the farmers as well as consumers, since this would balance the prices at a median price, which is both profit-earning for farmers as well as affordable to the consumers at all times of the year. In this project, the affected leaves are captures as images using a camera. These images are then processed further using various methods and the dominant features are extracted from them using various methods. Comparison of the features is done using various algorithms that detect the variance in color and its dominance in the recorded samples. This will help in faster and cost effective addressing of such diseases.
Article Details
Unique Paper ID: 160164

Publication Volume & Issue: Volume 9, Issue 12

Page(s): 1452 - 1455
Article Preview & Download


Share This Article

Join our RMS



Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Latest Publication

Call For Paper

Volume 11 Issue 1

Last Date for paper submitting for Latest Issue is 25 June 2024

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies

Important Links

Browse Rsearch papers