An accuracy between 93.4% and 96.1% is experimentally measured in this case. Ten tomato diseases are used as a case study, and the applications are trained with 40–100 segmented and normalized photographs for each disease. The new photograph is classified as the disease with the highest grade. When a new photograph is analyzed, its feature values are compared with the reference ranges, and different grades are assigned depending on whether a feature value falls within a range or not. Multiple reference ranges are determined for each feature during training. These features are extracted from photographs that display a plant part such as a leaf or a fruit. The employed classification method is based on features that represent distinct aspects of the sick plant such as, for example, the color level distribution in the regions of interest. Professional agronomists can select the species and their diseases that are supported by the developed tool and follow an automatic training procedure using a small number of indicative photographs. Mobile applications that can be used for the training and classification of plant diseases are described in this paper.
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