Ontology-Enhanced Disease Detection and Crop Yield Prediction in Agriculture Using ViT

The agricultural sector is evolving through data-driven precision agriculture, with a focus on increasing plant disease detection and yield prediction. To address these issues, this study proposes a novel approach that utilizes Natural Language Processing (NLP), Ontology, and Vision Transformer (ViT) technologies. Traditional farming methods at times struggle to detect diseases and effectively anticipate crop production due to rigid limitations. As a result, this approach makes use of recent advances in technology, such as ViT models for detecting diseases in plant images and NLP techniques for textual analysis. Our technology is built around a specific knowledge base that combines visual and textual data to enable exact disease severity assessment and dependable crop yield prediction. Thorough evaluations show that our model outperforms benchmarks in terms of accuracy, and the addition of an ontology graph is a big step forward in precision agriculture. Our research illustrates the possibility of using new technologies to transform agricultural practices and improve global food security.