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Discriminating Leaves Affected with Tomato Yellow Leaf Curl through Fluorescence Imaging Using Texture and Leaf Vein Features

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Transactions of the ASABE. 59(6): 1507-1515. (doi: 10.13031/trans.59.11221) @2016
Authors:   Jinzhu Lu, Dong Zhang, Huanyu Jiang
Keywords:   Classification, Disease, Fluorescence image, Leaf vein, Texture.

Abstract. Tomato yellow leaf curl disease (TYLCD), one of the most common diseases in tomato production, spreads rapidly and results in huge yield losses. This study aims to investigate the feasibility of discriminating TYLCD through fluorescence imaging. Fluorescence images (FI) of healthy and infected plants were collected with a high-speed camera, filters (690 nm), and three excitation LEDs (430 nm). Automatic iterative threshold selection and region filling methods were used to separate the leaf pixels of each image from the background. The segmented subimages containing 100% leaf pixels were selected as central images (CI). Leaf vein images (LI) were obtained from CIs through hit-or-miss transformation. In contrast to the vein morphologies of the infected leaves, those of the CIs and LIs of the healthy leaves were uniformly distributed. Gray-level co-occurrence matrix was used to extract texture features (TF) from CIs and leaf vein features (LF) from LIs. Sixteen features were evaluated using the receiver operating characteristic (ROC) curve, and rational features were selected by the area under the curves (AUC). All features were categorized into three groups: TF, LF, and their combination. For each group, features without ROC selection (TF, LF, and TF+LF) and after ROC selection (selected TF, selected LF, and selected features) were used as inputs to three classifiers: k-nearest neighbor (KNN), liner discrimination analysis (LDA), and binary logistic regression (BLR). Finally, six TF and six LF were selected. Without ROC selection, all classifiers performed with accuracies ranging from 95% to 100% in identifying infected leaves. For the TF+LF group, BLR exhibited the highest overall accuracy (100%), whereas LDA showed the lowest. After ROC selection, BLR had significantly increased classification accuracy, with higher accuracy for identifying infected leaves than for healthy leaves. This method could be used with TF and LF extracted from FI to discriminate TYLCD.

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