Adaptive neuro-fuzzy inference system for recognition of cotton leaf diseases

Author

Rothe, P.R. ; Kshirsagar, R.V.

Author_Institution

Dept. of Electron. Eng., Priyadarshini Coll. of Eng., Nagpur, India

fYear

2014

fDate

28-29 Nov. 2014

Firstpage

12

Lastpage

17

Abstract

The objective of this work was to develop and to evaluate adaptive neuro-fuzzy inference system as methodology to identify the leaf diseases on cotton. This paper presents automatic system for classification of three cotton leaf diseases namely Bacterial Blight, Myrothecium and Alternaria. Graph cut method is used for segmentation of images to extract color layout descriptors as features to train the adaptive fuzzy inference system. The testing samples are collected from Central Institute for Cotton Research, Nagpur and from the fields in Buldana and Wardha district.

Keywords

agriculture; cotton; feature extraction; fuzzy neural nets; fuzzy reasoning; image classification; image colour analysis; image segmentation; plant diseases; Alternaria; Buldana district; Central Institute for Cotton Research; Myrothecium; Nagpur; Wardha district; adaptive neuro-fuzzy inference system; automatic classification system; bacterial blight; color layout descriptor extraction; cotton leaf disease classification; cotton leaf disease recognition; feature extraction; graph cut method; image segmentation; Cotton; Diseases; Feature extraction; Image color analysis; Image edge detection; Image segmentation; Microorganisms; Color layout descriptors; Cotton leaf diseases; Gaussian filter; Graph cut; Image segmentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence on Power, Energy and Controls with their impact on Humanity (CIPECH), 2014 Innovative Applications of

Conference_Location

Ghaziabad

Type

conf

DOI

10.1109/CIPECH.2014.7019039

Filename

7019039