FAKTOR PENENTU SIFAT WARNA TANDAN BUAH SEGAR (TBS) SAWIT UNTUK MEMODELKAN KANDUNGAN MINYAK MENGGUNAKAN EVALUASI NONDESTRUKTIF FOTOGRAMMETRI

Abstract

In this study, the oil palm fresh fruit bunch (FFB) was harvested and its images were recorded in a photographic studio. The bunch was recorded from various distances (2, 7, 10, 15m) using five lighting configuration, i.e. ultraviolet lamp (600 watts), visible lamp (600 and 1000 watt), as well as IR lamp (600 and 1000 watts). The FFB images were processed in order to obtain 15 colour components making up the image, consist of three primary colours (R, G, B) and their transformations (H, S, I, RI, GI, BI, RG, RB, GB, GR, BR, BG). The prediction model of FFB’s oil content was built to evaluate the amount of oil on FFB accurately based on its image. The model was built using deep neural networks, where the colour components served as inputs, and 10 hiden layers were introduced to describe the relationship between all these variables and oil content. Of various recording setup, only four were selected based on their coefficient of correlation, namely: 10m_UV (R2 = 1); 10m_Vis2 (R2 = 1); 10m_IR2 (R2 = 1); and 2m_IR2 (R2 = 0.981). The determinant colour of the FFB’s image which mostly influence the prediction models were the ratio of R to B (RB) for 10m_UV; the value of H and S on 10m_Vis2; the I and S of the 10m_IR2; and RB, H, and B for the 2m_IR2 treatment. Keywords: deep neural network, FFB, nondestructive, oil content, photogrammetry

Plant phenotyping relevance

油ヤシ果房の画像から色特徴を抽出し、深層ニューラルネットワークで油含量を非破壊推定する手法が研究の中心であり、植物器官の形質推定に該当する。

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