Abstract
The study classifi es eight types of plants’ diseases using an adaptive neuro-fuzzy inference system (ANFIS). Haralick texture features obtained from plants’ images are applied as input data for a system. A hybrid algorithm consisting of a backward propagation of error and a gradient descent performed the ANFIS training. The ANFIS effi ciency was assessed on a test set through calculating accuracy, comprehensiveness, and the F1 score. The indicators obtained by this method were compared with other modern classifi cation methods.
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