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Remote sensing is a technology that is capable of overcoming the problems ofmeasurement data for fast and accurate information. One of implementation of remote sensing technology in the field of agriculture is in hyperspectral image data retrieval to find out the condition and age of the rice plant. It is necessary for the estimation of rice yield in order to support Government policy in conducting imports rice to meet food needs in Indonesia. To have a good prediction model in estimation of rice yield that has high accuracy must be preceded by the determination of the phase of the rice plant. The selection of the appropriate classifier must also supported the selection of just the right features to get the optimum accuracy. In this study, we conducted a comparison between Fuzzy Logic and Modified Fuzzy Logic to perform the classification on nine rice growth stages based on hyperspectral image. Modified Fuzzy Logic have the same procedure with Fuzzy Logic but with extra crisp rules given in Fuzzy Rules which is expected to increase the accuracy achievement. In this study, Modified Fuzzy Logic proved to be able to improve the accuracy of up to 10% compared to Fuzzy Logic

zenodo (cern european organization for nuclear research)

KLASIFIKASI FASE PERTUMBUHAN PADI BERDASARKAN CITRA HIPERSPEKTRAL DENGAN MODIFIKASI LOGIKA F U Z Z Y (PADDY GROWTH STAGES CLASSIFICATION BASED ON HYPERSPECTRAL IMAGE USING MODIFIED FUZZY LOGIC)