Pre-symptomatic crop intelligence: a closed-loop framework for anticipatory, confidence-aware decision-making in site-specific crop protection

Abstract Purpose Symptom-triggered crop protection acts only after damage is committed, and the intervention window has narrowed. This review reframes pre-symptomatic sensing from a detection problem into a closed decision loop, establishing the physiological lead time of a signal, set against its detection confidence and the latency of the response it can trigger, as the organizing principle for anticipatory, site-specific decision-making. Methods A systematic-narrative synthesis was conducted across major bibliographic databases through June 2026. Studies reporting pre-symptomatic capability under field or realistic conditions were retained and coded onto a coupled lead-time × confidence × actionability framework spanning sensing, inference, and actuation. Results Optical modalities were found to dominate the evidence base, while electrophysiological and volatile signals extended achievable lead time. Single modalities were insufficient to separate biotic from abiotic stress, motivating heterogeneous fusion. Edge inference and temporal onset forecasting remained immature, detection confidence was rarely quantified, and the sensing-to-actuation loop was seldom closed. Reported performance degraded sharply from laboratory to field, particularly in perennial and smallholder systems. Conclusions A unifying Pre-Symptomatic Crop Intelligence framework is proposed, governed by the principle that system value is bounded by the weakest of lead time, detection confidence, and response latency; priorities identified include lead-time-labeled benchmarks, uncertainty-aware inference, field-robust fusion, and economic evaluation for perennial crops.