PREDICTION OF DISEASE IN TOBACCO LEAF USING DEEP BELIEF NETWORK

Abstract
The quality of crop yield declines as far as leaf diseases in agriculture are concerned. Leaf diseases may therefore be recognised immediately, in order to increase plant yields. Most of the device is therefore impaired by the absence of different patterns of the leaf disease that impair the detection accuracy. In this paper we build an IT model that helps shape the context of collecting images, extracting features and classifying images in real time. The classifiers indicate the outcome, whether or not the leaf is ill. We use the Deep Belief Network (DBN) in this paper to categorise images in real-time. Tobacco plant experimental findings indicate that a higher sorting rate has been suggested than other current approaches.

Authors
D Sindhu
Anna University, India

Keywords
Real-Time Image Acquisition, Artificial Neural Network, Leaf Disease Detection, Tobacco Plant
Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 2 , Issue: 1 )
Date of Publication :
December 2020

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