Not AvailableIn recent years, deep learning techniques have become very popular in the field of image recognition and
classification. Image-based diagnosis of diseases in crops using deep learning techniques has become trendy in the
current scientific community. In this study, a deep convolutional neural network (CNN) model has been developed
to identify the images of maydis leaf bight (MLB) (Cochliobolus heterostrophus) disease of maize (Zea mays L.)
crop. A total of 1547 digital images of maize leaves (596 healthy and 951 infected with maydis leaf blight disease)
have been collected from different agricultural farms using hand-held camera and smartphones. The images have
been collected from the experimental plots of BCKV, West Bengal and ICAR-IARI, New Delhi during 2018–19. The
architectural framework of popular state-of-the network ‘GoogleNet’ has been used to build the deep CNN model.
The developed model has been successfully trained, validated and tested on the above-mentioned dataset. The trained
model has achieved an overall accuracy of 99.14% on the separate test dataset.Not Availabl
