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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