Non-Destructive Evaluation Quality of Oil Palm Fresh Fruit Bunch (FFB) (Elaeis guineensis Jacq.) Using Thermal Imaging in the Grading Process - Sirtin, Ardillah Anggraini; Makky, Muhammad; Santosa, Santosa; Cherie, Dinah (Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia, 2025)
Determining the quality of oil palm Fresh Fruit Bunches (FFB) quickly and accurately is very important in the grading process to ensure the quality of production results and the efficiency of the post-harvest process. This study aims to evaluate the quality of oil palm FFB non-destructive using thermal image technology, focusing on two main parameters: moisture content and oil content. The oil palm FFB used was the Tenera variety. Thermal characteristic data were obtained from the RGB pseudo color thermal images and the oil palm FFB temperature. The model obtained using Artificial Neural Network (ANN) showed that the calibration model for moisture content produced a linear regression equation y = 0.9826x + 0.7159 (R² = 0.9827), and for oil content y = 0.9962x + 0.0289 (R² = 0.9973). At the validation stage, the moisture content prediction model gave y = 0.9056x + 10.721 (R² = 0.8908), and oil content y = 0.7683x + 1.6494 (R² = 0.8567). These results indicated that thermal imaging technology has great potential as an efficient and accurate non-destructive method in evaluating the quality of oil palm FFB, especially in supporting a more objective and sustainable grading system.