THE APPLICATION OF LEARNING VECTOR QUANTIZATION METHOD FOR IDENTIFYING THE RICE DISEASE BASED ON THE SHAPE OF LEAF SPOT image processing with

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Ery Murniyasih
luluk suryani

Abstract

This research aims: (1). Make an application to identify types of diseases in rice plants based on the form of rice leaf spots; (2). Apply the Learning Vector Quantization (LVQ) method to the identification of rice plant diseases. In the learning and testing stages of LVQ the image is processed into Grayscale, Thresholding, and segmentation. At the training stage, the LVQ method is used to determine weights, target errors, max epochs, and training rates. The data used as input is an image of the identification of types of diseases of rice plants based on the shape of rice leaf spots with a pixel size of 95x35 and with the BITMAP extension (.bmp). The standard of success of this identification system is to calculate the value of the Termination Error Rate and the level of accuracy in the identification of leaf spot forms. From this simulation an artificial neural network structure was obtained with a learning rate value of 0.02 and an epoch number of 5 times. The system formed is able to recognize images containing leaf spot forms used as weights with an optimum accuracy value of 73.33% with a composition of brown spot disease (BC) 20%, Blast 20% and cercospora blotches 33.33%

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Murniyasih, E., & suryani, luluk. (2020). THE APPLICATION OF LEARNING VECTOR QUANTIZATION METHOD FOR IDENTIFYING THE RICE DISEASE BASED ON THE SHAPE OF LEAF SPOT. Electro Luceat, 6(1), 28-35. https://doi.org/10.32531/jelekn.v6i1.190
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