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

arXiv: https://arxiv.org/abs/2107.04813 · HTML: https://mlsanity.com/paper/2107.04813/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 10 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [DCT-CompCNN: A Novel Image Classification Network Using JPEG Compressed DCT Coefficients](https://mlsanity.com/paper/1907.11503/) (2019)

## Built on by (most cited first, 2 of 2)

- [Machine Learning for Leaf Disease Classification: Data, Techniques and Applications](https://mlsanity.com/paper/2310.12509/) (2023)
- [Leaf-Based Plant Disease Detection and Explainable AI](https://mlsanity.com/paper/2404.16833/) (2023)

## Abstract (arXiv)

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.