Cocoa Ripeness Classification Using Vision Transformer

The quality of manual methods for assessing the ripeness of cocoa pods is subjective and varies from one person to another because of the intense labor required and variation of light and background conditions within the field. This research implemented an automated classification approach for cocoa ripeness classification utilizing Vision Transformer (ViT) with Shifted Patch Tokenization (SPT) and Locality Self Attention (LSA) to improve classification accuracy. The model proposed in this research achieved an accuracy of 82.65% and a macro F1 score of 82.71 on the exam with 1,559 images captured under varying illumination backgrounds and complex scenes. The model also proved better than baseline CNN architectures such as VGG, MobileNet, and ResNet in identifying visually progressive stages of ripeness and demonstrated greater generalization in cocoa ripeness classification. The findings of this research indicate the benefits of reducing manual intervention with careful inspection without compromising quality assurance standards in cocoa production. This work demonstrates new ways of applying transformer models to address computer vision problems in agriculture which is a step towards precision and smart farming.