[vc_empty_space][vc_empty_space]
Classification of palm oil fresh fruit bunch using multiband optical sensors
Setiawan A.W.a, Mengko R.a, Putri A.P.H.a, Danudirdjo D.a, Ananda A.R.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, 40132, 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]© 2019 Institute of Advanced Engineering and Science.This study investigated optical sensor system consist of sixteen light emitting diode (LED) in visible/near infrared region to detect palm oil fresh fruit bunch (FFB) quality. Practically, experience grader assessed FFB quality by its ripeness based on external features such as colour and number of detached fruitlets. However, different seed and plantation management resulting in FFB quality variation. Same external features not linearly correlate with FFB oil content that corresponding with industrial needs. The 660 nm LED is choosen to be used to estimate the oil content of FFB. Using linear discriminant analysis (LDA) with Mahalanobis distance, the accuracy of the systems is 79.8% and 88.2%. From 33 FFB oil content measurement, grader misclassified 4 out of 17 FFB as ripe FFB but with low oil content (=17.5%). Classifying model build from FFB from main plantation then tested to evaluate FFB from smallholder. Classification model generated from FFB oil content data showed more accurate result compared to model generated from visual inspection 66.7% compared to 52.1%. Model accuracies attained by Discriminant Analysis (DA) and k-Nearest Neighbors (k-NN) were 79.8% and 80.7%, respectively based on grader evaluation. Model accuracies based on FFB oil content was 88.2% for both classifying algorithms.[/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][/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]Optical sensing,Palm oil content,Ripeness estimation,Visible-near infrared sensor[/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/10.11591/ijece.v9i4.pp2386-2393[/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]