Deep learning systems for crop disease recognition are routinely reported at above 95% accuracy on curated benchmarks, yet their field performance degrades sharply and silently once deployed. The cause is not a modelling deficiency in the conventional sense but a violation of the stationarity assumption on which supervised learning rests: the agro-climatic environment that generates the data is itself non-stationary. Humidity regimes shift between seasons, pathogen populations evolve, cultivar mixes change, sensor hardware ages, and the relationship between visual symptom and disease label drifts with them. A model frozen at training time therefore decays monotonically, and — more damagingly for trust — it decays while continuing to report high confidence. This manuscript presents AgroGuard AI, a five-layer reference architecture that treats environmental non-stationarity as a first-class design constraint rather than a deployment nuisance. The architecture couples four mechanisms that are usually studied in isolation. A drift detection subsystem monitors input, representation, and label-conditional distributions using a complementary ensemble of ADWIN, KSWIN, and kernel maximum mean discrepancy tests. A continual learning subsystem combines elastic weight consolidation with sparse episodic replay so that adaptation to a new agro-climatic regime does not erase competence in previously encountered ones. A conformal prediction layer converts raw softmax scores into prediction sets with distribution-free marginal coverage guarantees, and gates low-reliability inputs to human review rather than issuing a confident wrong answer. Finally, a temporal explanation engine reports not only why a prediction was made but why it changed, surfacing the shift in feature attribution that accompanies each adaptation event. We formalise the learning problem, specify each subsystem, derive the adaptation trigger policy, and define a trust metric suite covering calibration error, explanation fidelity, backward transfer, and selective risk. We further specify an edge deployment profile for low-connectivity rural contexts and a privacy-preserving federated aggregation scheme. The manuscript closes with a complete evaluation protocol — datasets, baselines, ablations, and acceptance thresholds — designed to falsify the architecture's central claims. All quantitative values reported herein are design targets and illustrative simulations under explicitly stated generative assumptions; no field trial has been conducted, and this limitation is treated as a defining boundary of the present contribution rather than a caveat.

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