Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks.

ABSTRACT: Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach t

SUBMITTER: Fernandez-Campos M 

PROVIDER: S-EPMC8248543 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature