Deep learning aided automatic and reliable detection of tomato begomovirus infections in plants

Begomoviruses have become the most potential plant pathogens and endanger livelihood of billions of farmers worldwide. Tomato leaf curl New Delhi virus (ToLCNDV) and Tomato leaf curl Gujarat virus (ToLCGV) are two destructive tomato-infecting begomoviruses that can infect diverse plant species including tobacco. Infection due to these begomoviruses induces a range of characteristics symptoms such as curling of leaves, puckering, leaf yellowing, and stunting of plants. Early detection of disease is desirable to contain the spread of the pathogens to manage the disease. Recent developments in the field of artificial intelligence and machine learning have resulted in various tools that enable in the accurate detection objects. In this study, inspiring from these, we have designed a deep learning model to help in early detection of the begemoviruses diseases. Specifically, we employ a deep learning architecture based on a convolutional neural network known as Visual Geometry Group 16 or VGG-16 to detect begomovirus infections in tobacco plants inoculated with ToLCNDV and ToLCGV. The outcome has been quite promising, as the modified model can detect symptomatic leaf curl plants with an accuracy of 97.211%.