Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset.

ABSTRACT: Fusarium head blight (FHB) is one of the most prevalent wheat diseases, causing substantial yield losses and health risks. Efficient phenotyping of FHB is crucial for accelerating resistance breeding, but currently used methods are time-consuming and expensive. The present article suggests a noninvasive classification model for FHB severity estimation using red-green-blue (RGB) images, without requiring extensive preprocessing. The model accepts images taken from consumer-grade, low-cost RGB cameras and classifies the FHB severity into 6 ordinal levels. In addition, we introduce a novel dataset consisting of around 3,000 images from 3 different years (2020, 2021, and 2022) and 2 FHB severity assessments per image from independent raters. We used a pretrained EfficientNet (size b0),

SUBMITTER: Roßle D 

PROVIDER: S-EPMC10348660 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature