Oil Palm Fruit Classification Using Spectrometer

Saeed, Osama Mohamed Ben; Shariff, Abdul Rashid Mohamed; Mahmud, Ahmad Rodzi B.; Shafri, Helmi Zulhaidi Mohd; Alfatni, Meftah Salem M.; Mustapha, Aouache

doi:10.1166/asl.2013.5004

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been several research studies on potential methods to determine the maturity of palm fruits. In order to determine and assess the level of maturity of oil palm fruits, some researchers have made use of hyperspectral imaging [4,5], fluorescence sensing [6,7], computer vision and an optical sensor [8,9], inductive sensing [10,11], nearinfrared spectroscopy [12], laser imaging [13], and lidar scanning [14].…”

Section: Introductionmentioning

confidence: 99%

Classification of Oil Palm Fresh Fruit Bunches Based on Their Maturity Using Thermal Imaging Technique

et al. 2022

View full text Add to dashboard Cite

The maturity of oil palm Fresh Fruit Bunches (FFB) is considered to be a significant factor that affects the profitability and salability of palm oil FFB. Typical methods of grading FFB consist of physical grading of fresh fruit, which is time-consuming and expensive, and the results are prone to human error. Therefore, this research attempts to formulate a thermal imaging method to indicate the precise maturity of oil palm fruits. A total of 297 oil palm FFBs were collected. The samples were divided into three groups: under-ripe, ripe, and over-ripe. Afterward, all the samples were scanned using a thermal imaging camera to calculate the real temperature of each sample. In order to normalize the measurement, the difference between the average temperature of the palm bunch and the ambient temperature (∆Temp) was considered as the main parameter. The results indicated that the mean ∆Temp of oil palm FFBs decreased consistently from under-ripe to over-ripe. The results of the ANOVA test demonstrated that the observed significance value was less than 0.05 in terms of ∆Temp, so there is a statistically significant difference in the means of all three maturity categories. It can be concluded that ∆Temp is a reliable index to classify the FFBs of oil palm. The classification analysis was conducted using the ∆Temp of the FFBs and its application as an index in Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN), and Kernel Nearest Neighbor (KNN). The highest degrees of overall accuracy (99.1% and 92.5%) were obtained through the ANN method. This study concludes that thermal images can be used as an index of oil palm maturity classification.

show abstract