Screening based sparse classification method for the detection of corn leaf diseases

Abstract

Detection of diseases in corn plant at early stage reduces the chance of productivity loss. Recognizing the different kind of disease in corn plant requires the identification of lesion part of the plant along with the extraction of proper features from the region of interest. Although several imaging tools and classifier were developed in the recent past towards identifying different kinds of disease in corn plant but most of the classification models do not meet the specific requirement due to improper selection of feature and classifier. In this paper, we proposed a two-step based sparse recognition model for the identification of corn disease. First step emphasizes with the construction of image dictionary by extracting multiple features from the lesion part of training image and followed it in the second phase, uses the screening and angle rule based sparse classifier to recognize the different corn diseases. Utilizing the data from the kaggle dataset and comparing with the other state of art, our method yielded an average classification accuracy of 88.55%. This makes the framework feasible and effective for the detection of corn plant disease.