SeptoSympto: A high-throughput image analysisof Septoria tritici blotch disease symptoms using deep learning methods

Abstract

Abstract Background Quantitative, accurate, and high-throughput phenotyping of crop diseases is needed for breeding programs and plant-pathogen interaction investigations. However, difficulties in the transferability of available numerical tools encourage maintaining visual assessment of disease symptoms, although this is laborious, time-consuming, requires expertise, and rater dependent. Deep learning has produced interesting results for plant disease evaluation, but has not yet been 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 a Python-coded image analysis script, called SeptoSympto, in which deep learning models based on the U-net and YOLO architectures were used to quantify necrosis and pycnidia, respectively. Small datasets of different sizes (containing 50, 100, 200, and 300 leaves) were trained to create deep learning models and to facilitate the transferability of the tool, and five different datasets were tested to develop a robust tool for the accurate analysis of STB symptoms. The results revealed that (i) the amount of annotated data does not influence the good performance of the models, (ii) the outputs of SeptoSympto are highly correlated with those of the experts, with a similar magnitude to the correlations between experts, and that (iii) the accuracy of SeptoSympto allows precise and rapid quantification of necrosis and pycnidia on both durum and bread wheat leaves inoculated with different strains of the pathogen, scanned with different scanners and grown under different conditions. Conclusions Although running SeptoSympto takes longer than visual assessment to evaluate STB symptoms, it allows the data to be stored and evaluated by everyone in a more accurate and unbiased manner. Furthermore, the methods used in SeptoSympto were chosen to be not only powerful but also the most frugal, easy to use and adaptable. This study therefore demonstrates the potential of deep learning to assess complex plant disease symptoms such as STB.