Tomato is one of the most grown and the second most consumed vegetable in the world. Alternaria solani is recognized as the most dangerous tomato pathogen. Currently, the diagnostics of this disease requires the proper symptoms assessment of plant tissue damages. Therefore, it is necessary to propose a fast and reliable method able to assess the degree of plants’ damage. Hyperspectral measurements and machine learning algorithms are one of the possibilities to address the problem of finding fast and even more important nondestructive plant diseases detection method. The presented work describes the application of two ensemble learning algorithms: Decision tree and Random Forest adapted for Alternaria solani detection for two varieties of tomatoes cultivated under foil tunnels. The final model was trained on the hyperspectral measurements from 350-2500nm spectral range. With a resulting accuracy of the method:0.78 and 0.98 for decision tree and random forest algorithms, respectively.