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Indonesia is the primary producer of palm oil. The edible oil in the palm fruits mesocarp obtained through mechanical extraction, where the quantity and quality of oil regulated by the fruit ripeness upon harvest. When oil forms and accumulate in the mesocarp, it replaces the moisture until the optimum ripeness reached before oil started to deteriorate. However, due to its nature, harvesting the fruits at optimum ripeness is challenging. Since water and oil have different thermal properties, in this study, we developed a Thermal-Vision system to observe the thermal properties of the fruits before harvest. Five harvest windows selected, namely 110-130, 131-150, 151-170, 171-190, and 191-200 days after anthesis (DAA). The recorded images then processed to determine the surface temperature of each fruit. The oil obtained from fruits as quality parameter evaluated. Additionally, Moisture Content (MC) of fruits mesocarp measured. Models were developed using the Multilayer Perceptron Artificial Neural Network algorithm to correlate fruits' thermal properties with measured parameters (Oil Content). The coefficient determination (R2) of FFB ripeness with the OC of 0.9058 and OC with temperature of 0.8039.The models successfully predict with R2 upon calibration was 0.7818 with SEC of 0.0831. While upon validation R2 value was 0.9535 and SEP of 0.0003.

Thermal vision of oil palm fruits under difference ripeness quality