Explainable federated learning for spatio-temporal stress inference in distributed agricultural monitoring

Abstract

Agricultural crops experience environmental and physiological stressors that evolve across space and time. These processes are only indirectly observable through imaging and are frequently monitored across geographically distributed sites with heterogeneous conditions and restricted data sharing. This study formulates stress monitoring as a distributed inference problem and introduces a federated framework to estimate latent stress trajectories from site-local observations. Each site performs local inference of temporally evolving stress representations, while federated aggregation coordinates learning without exchanging raw images. An explainability constraint promotes spatial coherence and biological plausibility of inferred stress indicators. External intervention analysis evaluates sensitivity of the latent inference process to physiologically induced modulation of stress dynamics. Hydrogen-rich water serves as a controlled intervention, enabling quantitative assessment of stress attenuation without modifying sensing or inference procedures. Evaluation across distributed plantation sites demonstrates stable operation under non-identically distributed conditions, with inter-site variance below 0.07 and temporal coherence above 0.85. Federated coordination converges within approximately 15 communication rounds and reduces cross-site parameter divergence by 45.5% relative to isolated optimization (p<0.01, n=10). Mean inferred stress decreases from 0.45 to 0.36 following intervention (p<0.01), accompanied by a 31.8% reduction in temporal fluctuation. Average inference time remains below 0.45 s per sequence, with communication limited to 3.6 MB per round. Federated inference framework for latent stress dynamics under non-IID biological data Hydrogen-rich water modeled as a controlled intervention for stress modulation analysis Quantitative reduction in cross-site inference variance under distributed learning Explainability-constrained inference ensuring spatially coherent stress representations Low-latency distributed inference suitable for real-world field deployment