CD Tesis
Pengklasifikasian Tingkat Kematangan Tandan Buah Segar Kelapa Sawit Dari Tanah Gambut Dan Mineral Menggunakan Metode Pencitraan Hiperspektral
Hyperspectral imaging is a non destructive method that has been used to
evaluate internal characteristics of fruits and vegetables. Plant genetics, soil
characteristics, and plant management are some of key factors to define the
quality of oil palm fresh fruit bunches (FFB) produced. This research was aimed
to discriminate the Tenera oil palm FFBs produced by oil palm trees grown from
mineral soil and peat soil using a hyperspectral imaging system which utilized a
Specim V10 spektrograf and Halogen lamp. The discrimination was based on
their ripeness level, mesocarp firmness, and classification using K-mean
clustering. The samples consisted of 61 mineral soil FFBs and 60 peat soil FFBs
with three ripeness levels as unripe, ripe, and overripe. Hyperspectral images
were recorded and processed using Matlab programs. The spectral reflectance
intensities showed the discrimination between both origin soils at wavelength
ranges of 700 nm - 900 nm.
The results also showed higher reflectance intensities of peat soil FFBs
than mineral soil FFBs. Correspondingly, Fruit firmness of peat soil FFBs are
higher than mineral soil FFBs. Mineral Soil FFBs has higher oil and ALB content
than FFB of peat oil palm. While the yield of oil palm FFBs peat is higher than oil
palm FFB from mineral soil. Classification using K- mean clustering between
reflectance intensities and fruit firmness showed significant clusters for three
ripeness levels. These results will be useful for an oil palm FFB sorting machine
based on spectral imaging method.
Keywords : Hyperspectral Imaging, Oil Palm, Fresh Fruit Bunches, Firmness
Level, K-Mean Clustering
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