Cross-regional Crop Recommendation under Distributional Shift: Ood Quantification and Agronomic Correspondence Assessment in Kurukshetra, Haryana

Background: Cross-regional deployment of crop recommendation models is challenging because differences in soil and climatic conditions can cause distributional shifts between training and deployment domains. This study assessed the cross-regional behaviour of Random Forest (RF), Multi-Layer Perceptron (MLP) and TabNet under deployment in Kurukshetra, Haryana. Methods: Three classifiers were evaluated on a 3,000-sample Kurukshetra deployment dataset constructed from field-survey soil measurements and NASA POWER meteorological records, with controlled Gaussian perturbation of meteorological features used to expand the dataset, using a 33,000-sample, 33-crop benchmark dataset. A feature-level OOD percentage (OOD%) metric quantified domain mismatch. Top-K (K=5) ranking retained the five most probable crop candidates per sample, followed by weighted consensus. Model confidence, inter-model agreement, season-wise recommendations and correspondence with Haryana agricultural sources were assessed. Result: RF achieved the highest benchmark performance (CV F1=96.55%, Test F1=96.93%, AUC=0.9995), followed by MLP (95.31% Test F1) and TabNet (95.07%). Severe mismatch occurred for nitrogen (48.3%) and potassium (38.6%), whereas temperature and humidity remained within the benchmark range. MLP and TabNet showed higher mean top-1 confidence (0.974 and 0.955) than RF (0.346), while agreement among all three models was 13.9%. Weighted consensus ranked sugarcane, chillies, maize, wheat, cabbage and sunflower highest; five of six consensus recommendations showed manual agronomic correspondence with published regional agricultural evidence (83.3%). Season-wise analysis identified violations associated with the absence of seasonal constraints in the training data, supporting explicit OOD assessment and multi-model consensus for cautious, region-specific crop recommendation under distributional shift.