PhytoSense AR: Augmented-Reality Plant Disease Diagnosis Using a Field-Adapted Vision Transformer and Colour-Based Lesion Segmentation

Plant diseases cause substantial crop losses, and smallholder farmers often lack timely access to expert diagnosis. Deep-learning classifiers trained on the PlantVillage dataset report near-perfect accuracy, yet they degrade sharply on photographs captured in the field. This paper presents PhytoSense AR, a free, browser-based diagnosis system that overlays disease information directly on the live camera view. The systemcombinesa colourspaceleafandlesionsegmentationstage,basedonHSV thresholdsandtheExcess-Green index, witha VisionTransformer(ViTB/16) classifier covering 38 classes across 14 crops. To narrow the gap between laboratory and field imagery, the classifier was fine-tuned on a mixture of PlantVillage and the PlantDoc field dataset with augmentation that simulates changes in zoom, orientation, illumination and focus, and was then exported to an 8-bit quantised ONNX model of 87 MB that runs on a CPU without PyTorch. On the PlantDoc test set of 236 unseen field images, top-1 accuracy rose from 37.7 % for the originalPlantVillage-onlymodelto76.7%forthedeployedquantisedmodel,whileaccuracyon760held-out PlantVillage images remained at 99.9 %. End-to-end processing, including segmentation and classification, takes about 200 ms per image on a laptop CPU. The system also estimates an infected-area severity and a health score, provides treatment guidance from a curated knowledge base, and offers a multilingual assistant. The results show that domain-adaptive fine-tuning on a modest field dataset, combined with lightweight deployment, makes AR-assisted plant diagnosis practical on commodity hardware