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