AgroGuard AI: A Trustworthy Adaptive Architecture for Crop Disease Intelligence under Environmental Non-Stationarity
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 superv...