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Septoria tritici blotch (STB) is one of the main causes of wheat yield loss in the world. Apart from good agrotechnical practice, disease‐resistant cultivars are required to prevent yield losses. Breeding of such cultivars is time‐consuming and laborious, mainly due to the quantitative character of such resistance, but can be shortened using precise phenotyping. We show that fluorescence parameters, collected in a high‐throughput system, can be used for estimating wheat STB resistance. Using machine learning methods (deep learning network, random forest), we demonstrate that disease resistance based on the percentage of necrotic leaf area can be estimated by alterations in fluorescence parameters 8 days after inoculation, and before disease symptoms become visible. Moreover, we applied a random forest classifier to assess the significance of fluorescence parameters in an informative way (classification accuracy = 64.84%, p value [Accuracy >No information rate] <0.001). Based on this, we observed extensive alterations in parameters of sensitive cultivars, reflecting worse photosynthetic performance. The most highly altered parameters were fluorescence intensity measurements and descriptors of energy flux through photosystem II (PSII) reaction centres towards PSI such as F2, F3 and F4 fluorescence intensities at 0.1, 0.270 and 2 ms; initial to maximal fluorescence ratio (F0/Fm); the time derivative of relative variable fluorescence (dV/dT0); maximum quantum yield of PSII in the dark‐adapted state (Fv/Fm); maximum electron transport flux at PSII (ET0/RC); maximum trapped excitonic energy flux per excited PSII cross section (CS) at time T0 (TR0/CS0); and quantum efficiency of the reduction of end acceptors dR0 [dR0/(1 − dR0)]. [ABSTRACT FROM AUTHOR]