Detection of citrus leaf diseases using a deep learning technique.

The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is ve1y impo1tant. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine leaming (ML) models, but the combination of increasing advances in computer vision appears the deep leaming (DL) area research to achieve a great potential in tenns of increasing accuracy. In this paper, two ways of conventional neural networks are used named AlexNet and ResNet models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small d11tasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for ResNet and AlexNet respectively. [ABSTRACT FROM AUTHOR]