Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities

This study used hyperspectral imaging technique to classify healthy and gray mold diseased tomato leaves. Hyperspectral images of diseased samples at 24 h, 48 h, 72 h, 96 h and 120 h after inoculation and healthy samples were taken in the wave range of 380–1023 nm. A total of ten pixels from each sample were identified as the region of interest (ROI), and the mean reflectance values of ROI were calculated. The dependent variables of healthy samples were set as 0, and diseased samples were set as 1, 2, 3, 4 and 5 according to infection severities, respectively. K-nearest neighbor (KNN) and C5.0 models were built to classify the samples using the full wave band set. To reduce data volume, features ranking (FR) was used to select sensitive bands. Then, the KNN classification model was built based on just the selected bands. This later procedure of reducing spectral dimensionality and classifying infection stages was defined as FR-KNN. Performances of KNN classifier on all wave bands and FR-KNN were compared. The overall classification results in the testing sets were 61.11% for KNN, 54.17% for C5.0 and 45.83% for FR-KNN model. When differentiating infected samples from control, the testing results were 94.44%, 94.44% and 97.22% for each model, respectively. In addition, early disease detection (1 dpi) obtained the results of 66.67% for KNN, 66.67% for C5.0 and 41.67% for FR-KNN. Therefore, it demonstrated that hyperspectral imaging has the potential to be used for early detection of gray mold disease on tomato leaves.