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Estimation of rice milling degree using image processing and Adaptive Network Based Fuzzy Inference System (ANFIS)
Shiddiq D.M.F.a, Nazaruddin Y.Y.a, Muchtadi F.I.a, Raharja S.b
a Department of Engineering Physics, Bandung Institute of Technology, Indonesia
b Department of Agro Industrial Technology, Bogor Agricultural University, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]This paper describe a development of rice milling degree measurement system based on color analysis of rice sample. Rice Milling Degree is usually defined as the extent to which the bran layers of rice have been removed during the milling process. In Indonesia, rice quality is measured based on National Standard of Indonesia (SNI) of Milled Rice. Rice quality contains 11 variables resulting 5 categorizations of milled rice quality. Determination of rice quality is conducted manually by experienced inspector. This method has limitation in accuracy, objectivity and longtime measurement. This paper presents a measurement system of rice milling degree using image processing. Variety of IR-64 rice which 0%, 50%, 85%, 95% and 100% milling degree is used as sample and its image is taken using flatbed scanner. An RGB analysis then implemented to the sample and showed that the value of RGB is correlated with the value of rice milling degree in the sample. Adaptive Network Based Fuzzy Inference System (ANFIS) model then constructed using RGB value and normalized RGB value for better performance. Validation of ANFIS model using normalized RGB value resulting average error 3,55%. © 2011 IEEE.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Adaptive network based fuzzy inference system,ANFIS,ANFIS model,Average errors,Color analysis,color system,Degree measurements,Flatbed scanners,Indonesia,Measurement system,Milled rice,Milling process,National standard,Rice milling degree,Rice samples[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ANFIS,color system,image processing,Rice milling degree,Rice quality[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICA.2011.6130137[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]