Classification Of Oil Palm Fruit Ripeness Level Using Artificial Neural Network
Keywords:
Oil Palm Fruit, Ripeness Classification, Neural Network, Image Processing, InceptionV3Abstract
The manual sorting process for determining the ripeness of oil palm fruit is subjective and inefficient, leading to a decline in Crude Palm Oil (CPO) quality and economic losses. This study aims to develop an automatic classification system for oil palm fruit ripeness to address these issues. It employs a digital image processing approach using a Neural Network model. The methodology involves using a pre-trained InceptionV3 model for feature extraction from a dataset of 3,000 fruit images, which are then fed into a custom-designed neural network with three hidden layers, using ReLU as the activation function and Adam as the optimizer. The model successfully classifies the fruits into 'unripe', 'ripe', and 'overripe' categories. The results show a high overall accuracy of 96.56 percent, with an F1-Score of 96.55 percent. The study concludes that the proposed Neural Network model is highly effective and reliable for automating oil palm fruit sorting, offering a feasible solution to improve efficiency and standardization in the palm oil industry
Keywords: Oil Palm Fruit, Ripeness Classification, Neural Network, Image Processing, InceptionV3
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Copyright (c) 2025 Aulia Ichsan Aulia Ichsan, Arni Hura, Supriadi, Muhammad Riza Harmeini
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