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AgriGround is introduced, a large-scale pixel-grounded agricultural multimodal instruction-tuning dataset containing over 500K images and 11M instruction-following samples spanning plant disease analysis, crop and weed identification, insect pest recognition, and fine-grained botanical understanding.
Uncertainty in multimodal models isn't just a number; it can fundamentally alter decision-making quality under complex evidence conditions.
Automating tomato harvesting just got a whole lot easier: this model achieves state-of-the-art ripeness detection and picking point localization with significantly fewer parameters than other YOLO models.