CLASSIFYING PLANTS’ HEALTH USING AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)
https://doi.org/10.35266/1999-7604-2024-2-3
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.
About the Authors
Valentin V. Brykin
Surgut State University, Surgut
Russian Federation
Russian Federation
Postgraduate
Mikhail Ya. Braginsky
Surgut State University, Surgut
Russian Federation
Russian Federation
Candidate of Sciences (Engineering), Docent
Dmitry V. Tarakanov
Surgut State University, Surgut
Russian Federation
Russian Federation
Candidate of Sciences (Engineering), Docent
Inessa L. Nazarova
Surgut State University, Surgut
Russian Federation
Russian Federation
Postgraduate, 1st Category Engineer
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Review
For citations:
Brykin V.V., Braginsky M.Ya., Tarakanov D.V., Nazarova I.L. CLASSIFYING PLANTS’ HEALTH USING AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS). Proceedings in Cybernetics. 2024;23(2):23-30. (In Russ.) https://doi.org/10.35266/1999-7604-2024-2-3
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