Graph-Ordered Curriculum Scheduling for Artificial Intelligence Agriculture Crop Phenology Classification Under Uneven Ground-Truth Coverage: A Computer Label-Efficiency Regret Bound

Crop phenology classifiers trained on unevenly labelled agricultural ground truth face a label-efficiency problem that is not captured by standard supervised learning theory: the order in which labelled samples are presented interacts with the graph structure of the label-coverage pattern, and the resulting regret depends on both. The framework yields a label-efficiency regret bound that decomposes into a graph-ordered covering term and a curriculum-ordering term, and it is evaluated through a replication-based replication of an ordinary least squares estimator on a declared data-generating process with 600 replications of 500 observations each. The replication characterises the behaviour of the estimator under the stated process and provides a calibration check on the bound's assumptions. The contribution is a bound that makes explicit how the topology of ground-truth coverage, rather than only its volume, governs the label efficiency of phenology classifiers, and a set of conditions under which graph-ordered curricula are provably preferable to volume-matched random ordering.