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

TL;DR

This paper proposes a novel method for detecting plant leaf diseases directly in the JPEG compressed domain using transfer learning, which enhances classification efficiency without decompressing images.

Contribution

It introduces a transfer learning approach that operates directly on JPEG DCT coefficients for plant disease detection, bypassing the need for image decompression.

Findings

Effective classification in JPEG compressed domain

Improved efficiency over traditional methods

Validated on leaf disease dataset

Abstract

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.