


Volume 20 No 20 (2022)
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DEEP LEARNING MODEL BASED EARLY PLANT DISEASE DETECTION
G. Tirumala Vasu , Samreen Fiza , Afreen Kubra , Ata. Kishore Kumar , Koteswararao Seelam
Abstract
Early plant leaf disease detection is a major challenge in agriculture field. The easiest way, to control the
plant leaf disease infection is an Challenging task But the excessisive use of plant leaf disease are
harmful to plants, animals as well as human beings. Integrated plant leaf disease management combines
biological and physical methods to prevent plant leaf disease infection. The techniques of machine vision
and digital image Processing are extensively applied to agricultural science and it have great perspective
especially in the plant protection field, which ultimately leads to plant leafs management. This paper
deals with a new type of early detection of plant leaf diseases system. Images of the leaves affected by
plant leaf diseases are acquired by using a digital camera. The leaves with plant leaf disease images are
processed for getting a gray colored image and then using feature extraction, image classification
techniques to detect plant leaf diseases on leaves. The images are acquired by using a digital camera .
The images are then transferred to a PC and represented in python software. The RGB image is then
converted into gray scale image and the feature extraction techniques are applied on that image. The
Support Vector Machine classifier is used to classify the plant leaf disease types. Here in this paper we
implement the deep learning and machine learning approach for identification of plat leaf disease and
we found that deep learning apporch using Bidrectional CNN gives the better performace in term of
accuracy
Keywords
svm,cnn,opencv,plant leaf disease,image processing
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