IDENTIFICATION OF NITROGEN STATUS IN Brassica juncea L.USING COLOR MOMENT GLCM AND BACKPROPAGATION NEURAL NETWORK
Vegetables cultivation using hydroponic is becoming popular now days because of its irrigation and fertilizer efficiency. One type of vegetable which can be cultivated using hydroponic is green mustard (Brassica juncea L.) tosakan variety. This vegetable is harvested in the vegetative phase, approximately aged of 30 days after planting. In addition, during the vegetative phase, this plant requires more nitrogen for growth of vegetative organs. The lack of nitrogen will lead to slow growth and the leaves turn yellow. In this study, non-destructive technology was developed to identify nitrogen status through the image of green mustard leaf by using digital image processing and artificial neural network. The image processing method used was the color moment for color feature extraction, gray level co-occurrence matrix (GLCM) for texture feature extraction and back propagation neural network to identify nitrogen status from the image of leaf. The input image data resulted from acquisition process was RGB color image which was converted to HSV. Prior to the color and texture feature extraction and texture, acquisition image was segmented and cropped to get the leaf image only. Next Step was to conduct training using back propagation neural network with two hidden layer combinations, 20,000 iteration epoch. Accuracy of the test results using those methods was 97.82%. The result indicates those three methods is reliable to identify nitrogen status in the leaf of green mustard.