Energy-Budget-Constrained Inference for Computer Agriculture Frost Warning Under Sparse Weather-Station Telemetry: An Artificial Intelligence Latency-Accuracy Frontier Evaluation

Frost warning for smallholder agriculture in data-sparse regions must operate within a fixed energy budget per inference and a sparse telemetry stream, yet the trade-off between latency and accuracy under these constraints remains poorly characterised. The framework is instantiated on a real reanalysis record: NASA POWER monthly air temperature at 2 m for Accra, Ghana (5.6°N, 0.19°W), 2000–2023, comprising 312 monthly observations with a mean of 26.521 °C, a standard deviation of 1.24 °C and a warming trend of 0.1622 °C per decade. The analysis derives a closed-form frontier relating classification accuracy to the number of telemetry samples consumed and the energy expended per sample, and shows that the marginal accuracy return to additional samples collapses once the sampling interval falls below the decorrelation time of the temperature process. A threshold theorem identifies the energy budget below which no classifier can exceed chance accuracy, and a corollary bounds the achievable warning lead time. The contribution is a transferable design rule: for a given energy budget, the optimal sampling policy is not the highest-frequency policy but the one whose interval matches the autocorrelation length of the target variable. The framework is methodological and does not report field deployment; it provides the formal apparatus and the empirical calibration needed before such a deployment is attempted.