Abstract
Oil palm ripeness’ main evaluation procedure is traditionally accomplished by human vision. However, the dependency on human evaluators to grade the ripeness of oil palm fresh fruit bunches (FFBs) by traditional means could lead to inaccuracy that can cause a reduction in oil palm fruit oil extraction rate (OER). This paper emphasizes the fruit battery method to distinguish oil palm fruit FFB ripeness stages by determining the value of load resistance voltage and its moisture content resolution. In addition, computer vision using a color feature is tested on the same samples to compare the accuracy score using support vector machine (SVM). The accuracy score results of the fruit battery, computer vision, and a combination of both methods’ accuracy scores are evaluated and compared. When the ripe and unripe samples were tested for load resistance voltage ranging from 10 Ω to 10 kΩ, three resistance values were shortlisted and tested for moisture content resolution evaluation. A 1 kΩ load resistance showed the best moisture content resolution, and the results were used for accuracy score evaluation comparison with computer vision. From the results obtained, the accuracy scores for the combination method are the highest, followed by the fruit battery and computer vision methods.
Highlights
Palm oil is the most productive vegetable oil in the world
Marker, and collected, underwere ripe,taken and unripe fruits wereanidentified according to their moisture they were tested with the fruit battery method
This research studied the fruit battery load resistance determination that produces low for high-sensitivity results
Summary
Palm oil is the most productive vegetable oil in the world. Palm oil has proven to be useful and is used in various products such as soap, margarine, cosmetics, and surfactants [1]. Traditional human graders play an important role in distinguishing oil palm ripeness in plantations. During the pre-harvesting stage, human graders evaluate the oil palm fresh fruit bunches (FFBs) based on the number of detached fruits that fell to the ground and the surface color of oil palm FFBs to determine their ripeness stage [2]. For the post-harvesting stage, a human expert inspects the color of the oil palm FFBs’ surface to reject unripe and over-ripe FFBs so that they do not proceed to the oil extraction process [3]. According to Siregar [4], oil extraction rate (OER) decreases by
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