Calibration of Low-Cost Soil Moisture Sensor Networks for Agriculture Yield Prediction Under Probe Degradation: A Computer Robustness Evaluation

Low-cost soil moisture sensor networks are increasingly deployed to support yield prediction in smallholder agriculture, but their calibration is fragile: probe degradation shifts the mapping between raw sensor output and volumetric water content, and the resulting bias propagates into any downstream yield model. the present analysis treats calibration as a robustness problem rather than a one-off fitting exercise. It formalises probe degradation as a time-varying affine perturbation of the sensor response and derives the conditions under which a calibration schedule preserves the statistical consistency of a yield predictor built on the calibrated signal. The analysis is a design and power study, not an empirical field trial: it characterises the operating characteristics of a before-after-control-impact (BACIP) estimator under a declared variance structure with between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.64, evaluated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The results show that 52 clusters are required to detect a 5 percentage point effect at 80% power under this variance structure, and that the minimum detectable effect falls steeply as clusters increase from 8 to 32. A degradation-aware recalibration schedule is proposed that ties recalibration frequency to the estimated drift rate, and the boundary conditions under which the schedule outperforms fixed-interval calibration are stated. The contribution is a formal robustness criterion for sensor-network calibration in agricultural yield prediction, together with a design table that lets practitioners size a BACIP evaluation before committing to a deployment.