A crop disease classifier achieves 95% accuracy on the dataset it was trained on. Deploy it in a different field — different camera, different lighting, different cultivar, different soil color in the background — and accuracy drops to 79%. The model didn't fail at classification. It failed at generalization. And in agricultural AI, every deployment is a different field.
The AgrI Challenge (arXiv:2603.07356) makes this problem the competition. Multiple teams collect agricultural image datasets independently — different equipment, timing, locations, protocols. Each team's dataset is treated as a distinct domain. Two validation protocols measure the gap:
Train-on-One-Team-Only (TOTO): train on one team's data, test on another's. The gap reveals how much a single collection procedure biases the model. DenseNet121 showed a 16.2% validation-test gap — the model learned the collection conditions, not the disease.
Leave-One-Team-Out (LOTO): train on all teams except one, test on the held-out team. Multi-source training reduced the gap to 2.8%. Diversity in the training data, not model sophistication, closed the gap.
The structural insight: the typical agricultural CV pipeline — one team collects data, one team trains a model, one team publishes a benchmark — encodes a single distribution as ground truth. Cross-team validation reveals that the distribution itself is an artifact of the collection process. The “real” distribution is the union of all possible collection conditions, which no single team can sample. The competition protocol doesn't test models. It tests data.