Oil Palm Fruit Image Ripeness Classification with Computer Vision using Deep Learning and Visual Attention

Abstract

Oil palm is one of the leading agricultural industries, especially in the South East Asian region. However, oil palm fruit ripeness classification based on computer vision has not gained many satisfactory results. Therefore, most of the ripeness sorting processes are still done manually by labor works. The objective of this research is to develop a model using a residual-based attention mechanism that could recognize the small detail differences between images. Thus, the model could classify oil palm fruit ripeness better. The dataset consists of 400 images with seven levels of ripeness. Since the number of images in the dataset, Ten Crop preprocessing is utilized to augment the data. The experiment showed that the proposed model ResAtt DenseNet model, which uses residual visual attention could improve the F1 Score by 1.1% compared to the highest F1 Score from other models in the experiment of this study.