Detection of Plant Leaf Disease Directly in the JPEG Compressed Domain using Transfer Learning Technique

Plant leaf diseases pose a significant danger to food security and they cause depletion in quality and volume of production. Therefore accurate and timely detection of leaf disease is very important to check the loss of the crops and meet the growing food demand of the people. Conventional technique...

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Bibliographic Details
Published inarXiv.org
Main Authors Sharma, Atul, Bulla Rajesh, Javed, Mohammed
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 10.07.2021
Subjects
Artificial neural networks
Depletion
Disease
Domains
Food
Image classification
Image compression
JPEG encoders-decoders
Machine learning
Neural networks
Plant diseases
Scientific papers
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Summary:Plant leaf diseases pose a significant danger to food security and they cause depletion in quality and volume of production. Therefore accurate and timely detection of leaf disease is very important to check the loss of the crops and meet the growing food demand of the people. Conventional techniques depend on lab investigation and human skills which are generally costly and inaccessible. Recently, Deep Neural Networks have been exceptionally fruitful in image classification. In this research paper, plant leaf disease detection employing transfer learning is explored in the JPEG compressed domain. Here, the JPEG compressed stream consisting of DCT coefficients is, directly fed into the Neural Network to improve the efficiency of classification. The experimental results on JPEG compressed leaf dataset demonstrate the efficacy of the proposed model.
ISSN:2331-8422