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

Table 2 Metrics of models for necrosis and pycnidia

A Necrosis
Model	Leaf number in training dataset	Leaf number in validation dataset	Epochs	Precision	Recall	F1
N0	50	75	47	0.87	0.84	0.85
N1	100	75	42	0.95	0.80	0.87
N2	200	75	26	0.95	0.82	0.88
N3	300	75	20	0.95	0.85	0.90

B Pycnidia
Model	Leaf number in training dataset	Leaf number in validation dataset	Epochs	Precision	Recall	F1
P0	50	40	199	0.54	0.27	0.36
P1	100	40	162	0.53	0.25	0.34
P2	200	40	183	0.56	0.25	0.34

The model trainings were carried out on a small number of leaves and epochs. The resulting models were evaluated using different metrics: precision, recall and F1. High precision indicates minimal false positive detection. High recall indicates maximum detection of true positives. F1 is the harmonic mean of recall and precision. A Four models (N0, N1, N2 & N3) for necrosis detection were trained on training datasets of different sizes, using a segmentation based deep learning architecture called U-net. B Three models (P0, P1 & P2) for pycnidia detection were trained on training datasets of different sizes, using an object detection based deep learning architecture called YOLOv5

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