Identification of Plant Diseases Using Gray-Level and Color-Based Features of Leaf Images

The detection of plant diseases is a vital factor in agricultural production worldwide, which if ignored, can lead to tremendous losses of plant products and revenue. Farmers and researchers from many centuries ago have learnt to identify some plant disease manually by inspection, but presently, technological development have advanced cultivation to an industrial scale, therefore detection of plant diseases has also become a great issue of concern as the farmers may be unable to identify the diseases, their point of origin or even the infected plants early enough. This can lead to a disease outbreak. Early detection of plant diseases can immensely reduce or avoid massive potential losses as it will provide the opportunity for active and cautionary measures. In view of the aforementioned issue, carrying out researches on different ways and methods to curb this problem is a vital necessity. This thesis study employs the application of computer vision and image processing techniques for plant disease identification. Principal Component Analysis (PCA), Local Binary Patterns (LBP) and Completed Local Binary Patterns (CLBP) feature extraction methods are used for the extraction of texture-based and appearance-based image features. Disease symptoms are analyzed and identified from four different plant leaves to evaluate the performance of the proposed method. We propose a method that incorporates Feature-Level Fusion of the gray level features and color based features using PCA and LBP methods to create a robust system. The proposed method has proven to be more robust compared to the individual systems using LBP and CLBP. Experiments are conducted on PlantVillage dataset due to its diversified collection of plant leaves. Furthermore, two classifiers are used for classification purposes namely k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). At the end of the empirical evaluations, a comparative study is presented. Keywords: plant disease identification, leaf images, color spaces, Feature-Level Fusion, feature extraction, texture-based features, appearance-based features.