Detection of Oil Palm Fruit Ripeness through Image Feature Optimization using Convolutional Neural Network Algorithm - Setiawan, Dedy; Eko Prasetyo Utomo, Pradita; Alfalah, Muksin (Society of Visual Informatics, 2025)
The increase in the need for raw materials for palm oil products in the form of food and non-food is felt by the people of Indonesia and other countries. For this reason, triggering oil palm farmers in Indonesia must be able to maximize their production. Currently, oil palm farmers in Indonesia still need help knowing the level of sustainability of oil palm fruit to maintain their production. This research was conducted to identify the maturity level of oil palm fruit using practical images for oil palm farmers in Indonesia. The Convolutional Neutral Network (CNN) algorithm is the research method used to identify pictures of oil palm fruit. The dataset collection comprised 400 images of oil palm fruits divided into three types of classes, namely images of raw, ripe, and rotten oil palm fruits. The dataset was taken from various internet sources, and photos were taken directly using a mobile phone camera according to a predetermined class. This study found that identifying the maturity level of oil palm fruit using the Convolutional Neural Network (CNN) algorithm obtained a high accuracy of 98% in the training process and 76% in the model testing process. The findings of this study can also inspire further research in optimizing image features and using the Convolutional Neural Network (CNN) algorithm more efficiently. This could include a reduction in model training time, the number of parameters, or the development of other techniques that improve algorithm performance.