Leaf images classification for the crops diseases detection

Crop production affects the economy of a specific region or country. To maintain the economic development of any territory crops disease detection is a leading factor in agriculture. There exists various techniques to detect the crops disease, but they offers lilimted accuracy. The proposed mulistage technique utilizes leaf region separation from background prior to the feature extraction for classification of images as healthy and diseased. Initially color transformation-based foreground separation is performed followed by the features extraction. The novelty of the method is attruibuts that are computed though adaptive analytic wavelet transform (AAWT). The AAWT decomposes the preprocessed images into various sub-band images as features. The attributes utilized for categorization are a combination of bag of visual word, Fisher vectors, and AAWT based-features extracted from leaf images. Performance of the proposal is analyzed through PlantVillage dataset. The simulation outcomes validate the superiority of the proferred classification system as compared with the existing techniques of the field. The proposed leaf classification method provides an average accuracy of 94.07 % with the area under the characteristic curve 0.961.