Image-based identification of maydis leaf blight disease of maize (Zea mays) using deep learning - HAQUE, MD ASHRAFUL; MARWAHA, SUDEEP; ARORA, ALKA; PAUL, RANJIT KUMAR; HOODA, KARAMBIR SINGH; SHARMA, ANU; GROVER, MONENDRA (The Indian Journal of Agricultural Sciences, 2021)
In 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.