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A Novel Deep Learning Method for Detection and Classification of Plant Diseases Using Resnets Algorithm

Author(s):

M.Hemavathi , Vivekananda College of Arts and Sciences for Women, ; Mrs.J.K.Kanimozhi, Vivekananda College of Arts and Sciences for Women,

Keywords:

Deep Learning, Plant Diseases, RESNETS Algorithm

Abstract

The position of agriculture product is pivotal to a nation's profitable growth. The biggest handicap to the product and quality of food, however, is factory complaint. Beforehand discovery of factory conditions is essential for maintaining global health and weal. The standard system of opinion entails a pathologist visiting the position and visually assessing each factory. still, due to lower delicacy and limited availability of mortal coffers, homemade examination for colorful factory conditions is limited. To address these problems, it's necessary to develop automated styles that can snappily identify and classify a wide range of factory conditions. The presence of low- intensity information in the image background and focus, the extreme colour similarity between healthy and diseased factory areas, the presence of noise in the samples, and changes in the position, chrominance, structure, and size of factory leaves make it delicate to directly identify and classify factory conditions. We've developed a dependable factory complaint bracket system using an InceptionV3 Architecture to address the forenamed issues. In this exploration, we suggested a deep literacy strategy grounded on InceptionV3 Architecture to identify splint conditions in a variety of shops. Chancing the factory complaint and its bracket is our end. The substantiated dataset is taken from the well- known public source kaggle. The dataset consists of 70,295 Factory images of Apple, Blueberry, Cherry, Corn(sludge), Grape, Orange, Peach, Pepper bell, Potato, jeer, Soybean, Strawberry and Tomato. The suggested system has the capacity to handle complex situations from a shops area and can successfully identify colorful forms of conditions.

Other Details

Paper ID: IJSRDV11I40071
Published in: Volume : 11, Issue : 4
Publication Date: 01/07/2023
Page(s): 58-60

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