Pencitraan Hiperspekral untuk Membedakan Asal Tanah Tumbuh Dari Tandan Buah Segar Kelapa Sawit

 Dina Veranita (Universitas Riau, Pekanbaru, Indonesia)
 (*)Minarni Shiddiq Mail (Universitas Riau, Pekanbaru, Indonesia)
 Feri Candra (Universitas Riau, Pekanbaru, Indonesia)
 Saktioto Saktioto (Universitas Riau, Pekanbaru, Indonesia)
 Mohammad Fisal Rabin (Universitas Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: May 22, 2020; Published: July 20, 2020



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. 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. 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


Hyperspectral Imaging, Oil Palm, Fresh Fruit Bunches, Firmness Level, K-Mean Clustering

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