Contrastive Self-Supervised Pretraining for Artificial Intelligence Agriculture Weed Detection Under Label Scarcity: A Computer Transfer-Robustness Evaluation

Weed detection models for precision agriculture are typically trained on labelled field imagery that is expensive to obtain and geographically narrow, so that a model fitted in one region may fail silently when deployed in another. Contrastive self-supervised pretraining has been proposed as a remedy because it learns representations from unlabelled images, but the conditions under which the resulting representations transfer robustly across domains remain poorly characterised. We define a representation-level robustness functional that separates invariance to nuisance transformations from sensitivity to class-discriminative structure, and we derive a bound on target-domain error in terms of source-domain error, representation stability and a domain-divergence term. The bound implies that contrastive pretraining helps only when the augmentation distribution used during pretraining overlaps with the transformation set that distinguishes source from target domains; otherwise the learned invariances suppress exactly the features the downstream task requires. We operationalise the framework as a transfer-robustness evaluation protocol with three components: a label-budget sweep, a domain-shift ladder and a representation-stability diagnostic. The protocol is specified as a design template rather than executed on new field data, and we report the analytical operating characteristics of the evaluation under a declared variance structure. The main contribution is a precise statement of when contrastive pretraining is expected to improve weed-detection transfer and when it is expected to fail, together with an evaluation design that makes that expectation testable.