Advancing palm oil fruit ripeness classification using transfer learning in deep neural networks

Abstract

The palm oil industry is a significant component of Indonesia’s economy, driven by increasing global demand across various industries. Manual identification of palm oil fruit ripeness is often subjective and labor-intensive, creating a need for a faster and more accurate solution. This study proposes the use of deep learning models based on transfer learning to enhance the classification of palm oil fruit ripeness. Our research evaluates several models, finding that ResNet152V2 achieves the highest performance with superior accuracy and the lowest validation loss. DenseNet201, MobileNet, and InceptionV3 also deliver strong results, each demonstrating an accuracy above 0.99 and a validation loss below 0.04. Cross-validation confirms that ResNet152V2, DenseNet201, and MobileNet maintain high and consistent performance across different folds, showcasing their stability and reliability. This approach provides a promising alternative to manual methods, offering a more efficient and precise means for determining palm oil fruit ripeness, which could significantly benefit the industry by streamlining quality control processes.