Fruit Battery Method for Oil Palm Fruit Ripeness Sensor and Comparison with Computer Vision Method

Autor: Hiroyuki Wakiwaka, Kunihisa Tashiro, Norhisam Misron, Kaiko Minakata, Hirokazu Nagata, Nor Aziana Aliteh, Kazuki Kobayashi
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Sensors
Volume 20
Issue 3
Sensors (Basel, Switzerland)
Sensors, Vol 20, Iss 3, p 637 (2020)
ISSN: 1424-8220
DOI: 10.3390/s20030637
Popis: Oil palm ripeness&rsquo
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&rsquo
accuracy scores are evaluated and compared. When the ripe and unripe samples were tested for load resistance voltage ranging from 10 &Omega
to 10 k&Omega
three resistance values were shortlisted and tested for moisture content resolution evaluation. A 1 k&Omega
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.
Databáze: OpenAIRE
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