AI Now Spots Grape Diseases Before They Spread

Grape leaf disease recognition is vital for precision agriculture—it enables early diagnosis and better vineyard management. But many deep learning studies rely on a few datasets collected under controlled conditions, which may not reflect real-world challenges like complex backgrounds, variable lighting, occlusion, and different devices. This paper introduces a dataset-centric benchmark for grape leaf disease classification and detection, analyzing public datasets and evaluating models in three settings: image-level classification, region-level classification, and object detection. Results show near-saturated performance on some controlled or derivative datasets, greater difficulty on heterogeneous datasets, and sharp drops in cross-dataset performance—especially for object detection. The key takeaway: shared disease labels don't necessarily define equivalent recognition tasks, and realistic field evaluation plus external validation are essential.