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Deep Convolution Neural Network-Based Analysis of Tomato Plant Leaves

Published: 30 May 2023 Publication History

Abstract

Plant diseases that may seriously impact agriculture are often discovered with the naked eye, albeit this can take more time and increase the likelihood of a false positive. This issue may be resolved and the chance of decreased plant output is decreased with early discovery. The aim of this experimental research is to deploy intelligence, which can be effectively used for picture classification utilising numerous convolutional neural network (CNN) manners, to automatically identify tomato plant leaf illnesses more quickly. For better performance measurement, the Visual Geometry Group (VGG) model, which is based on CNN, is employed. To diagnose illnesses, this research concludes to categorise photos using VGG-19 transfer learning architectures with various optimizers. In the experimental comparative research, an accuracy of 97.67% and 87.67% was achieved as training and testing with nadam optimizer.

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ICIMMI '22: Proceedings of the 4th International Conference on Information Management & Machine Intelligence
December 2022
749 pages
ISBN:9781450399937
DOI:10.1145/3590837
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 30 May 2023

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  1. CNN
  2. DL
  3. Optimizer
  4. Tomato
  5. Vgg19

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