Beyond Black Boxes: An Ontology Based Vision Framework for Transparent Fruit Disease Detection

arXiv:2611.00039v1 Announce Type: cross Abstract: Efficiency and reliability in the timely identification of phytopathogens would help minimize crop loss in agricultural sectors but existing deep learning based methods, although accurate, would require a large annotated dataset and are black-boxed in their operation, thus, restricting flexibility and transparency. The current paper presents a hybrid detection framework that is able to overcome these drawbacks through the unified real-time vision with a semantic knowledge base. Web Ontology Language (OWL) was used to develop a comprehensive ontology of the disease of fruits that formally codifies phytopathological knowledge, such as symptom phenotype, spectral phenotype (HSV), and morphological phenotype. Such knowledge-guided configuration guides the image processing pipeline, making it possible to diagnose using this knowledge under expert specified rules without a large training dataset. The ontology underwent a computationally verified test of its structural soundness and commonsensical practical usefulness and scored an overall of 7.4/10. On the public set of MangoFruitDDS, the diagnostic performance was an F1 Score of 85.00%, precision of 85.00%, and a recall of 85.23%. These findings are very competitive and similar to other known deep learning models like the VGG16 that prove that the knowledge-driven approach provides an accurate, transparent, and scalable solution on automated fruit disease diagnosis.