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

Table 1 Information about datasets used to develop SeptoSympto

Dataset	Usage	Leaf number	Plant species	Varieties	Zymoseptoria strains	Growth conditions	Scanner	Institute
1	Model training	375	Triticum aestivum	19	IPO9415	Greenhouse 16 h/8 h photoperiod, at 24 °C/20 °C and with 250 µmol/s/m²	Epson Perfection V370 Photo	1
2	Model evaluation	40	Triticum aestivum	19	IPO9415	Greenhouse 16 h/8 h photoperiod, at 24 °C/20 °C and with 250 µmol/s/m²	Epson Perfection V370 Photo	1
3	Model evaluation	50	Triticum aestivum	13	IPO9415	Growing chamber 16 h/8 h photoperiod, at 24 °C/20 °C and with 250 µmol/s/m²	Epson Perfection V370 Photo	1
4	Model evaluation	50	Triticum turgidum	18	P1A	Growing chamber 16 h/8 h photoperiod, at 24 °C/20 °C and with 250 µmol/s/m²	Epson Perfection V370 Photo	1
5	Model evaluation	115	Triticum aestivum	3	IPO9415	Growing chamber 16 h/8 h of photoperiod, at 21 °C/18 °C and with 400 µmol/s/m²	Epson Perfection V750 Pro	2
6	Model evaluation	55	Triticum aestivum	1	Descendants of a biparental population (Parental strains: INRA09-FS0813 & INRA09-FS0732)	Growing chamber 16 h/8 h of photoperiod at 22 °C/18 °C and with 300 µmol/s/m²	CanoScan 9000F MarkII	3

Six different datasets were used to create (dataset 1) or evaluate (dataset 2 to 6) the deep learning models. All the scanned images contain only wheat leaves inoculated with Z. tritici and were taken at 1200 dpi in TIFF format. The datasets consist of different varieties, strains, growth conditions, and scans used to evaluate the models

ISSN: 1746-4811