SeptoSympto: a precise image analysis of Septoria tritici blotch disease symptoms using deep learning methods on scanned images.

ABSTRACT:

Background

Investigations on plant-pathogen interactions require quantitative, accurate, and rapid phenotyping of crop diseases. However, visual assessment of disease symptoms is preferred over available numerical tools due to transferability challenges. These assessments are laborious, time-consuming, require expertise, and are rater dependent. More recently, deep learning has produced interesting results for evaluating plant diseases. Nevertheless, it has yet to be used to quantify the severity of Septoria tritici blotch (STB) caused by Zymoseptoria tritici-a frequently occurring and damaging disease on wheat crops.

Results

We developed an image analysis script in Python, called SeptoSympto. This script uses deep learning models based on the U-Net and YOLO architectures to

SUBMITTER: Mathieu L 

PROVIDER: S-EPMC10832182 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature