Detection of Plant Leaf Disease Directly in the JPEG Compressed Domain using Transfer Learning Technique
This addresses the problem of costly and inaccessible disease detection for agriculture, though it is incremental as it adapts existing methods to a compressed domain.
The paper tackled plant leaf disease detection by applying transfer learning directly on JPEG compressed DCT coefficients, achieving improved classification efficiency as demonstrated on a JPEG compressed leaf dataset.
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.