In this research a non-destructive, rapid and cost
effective examination machine for the estimation of the ripeness
fraction, oil content and free fatty acid level in oil palm fresh fruits
bunch was developed. The automatic machine-vision based in-
spection system provided consistency, rapid estimation and accep-
table accuracy results in non-dest
ructive manner.
Fresh
fruits
bunch
samples from Tenera cultivar (7 to 20 years trees) were taken from
Cimulang plantation, Bogor, Indonesia. Two statistical analysis
methods were used: a forward stepwise multiple linear regression
analysis and a multilayer-perceptron artificial neural network
analysis. The best prediction of ripeness and oil content models
were obtained using the latter method, while the best free fatty acid
prediction model was developed by the first method. The models
were then employed in the machine-vision inspection systems of
the machine. The system best prediction accuracy of ripeness, oil
content and free fatty acid models was 93.5, 96.41, and 89.32%,
with standard error of prediction being 0.065, 0.044 and 0.068,
respectively. The system was tested through a series of field tests,
and successfully examined more than 12 t of fruits bunch per hour,
without causing damage.
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