  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.
