Pest Detection in Edible Crops at the Edge: An Implementation-Focused Review of Vision, Spectroscopy, and Sensors

Early pest detection in edible crops demands sensing solutions that can run at the edge under tight power, budget, and maintenance constraints. This review synthesizes peer-reviewed work (2015-2025) on three modality families-vision/AI, spectroscopy/imaging spectroscopy, and indirect sensors-restricted to edible crops and studies reporting some implementation or testing (n = 178; IEEE Xplore and Scopus). Each article was scored with a modality-aware performance-cost-implementability (PCI) rubric using category-specific weights, and the inter-reviewer reliability was quantified with weighted Cohen's κ. We translated the evidence into compact decision maps for common deployment profiles (low-power rapid rollout; high-accuracy cost-flexible; and block-scale scouting). Across the corpus, vision/AI and well-engineered sensor systems more often reached deployment-leaning PCI (≥3.5: 32.0% and 33.3%, respectively) than spectroscopy (18.2%); the median PCI was 3.20 (AI), 3.17 (sensors), and 2.60 (spectroscopy). A Pareto analysis highlighted detector/attention models near (P,C,I)≈(4,5,4); sensor nodes spanning balanced (4,4,4) and ultra-lean (2,5,4) trade-offs; and the spectroscopy split between the early-warning strength (5,4,3) and portability (4,3,4). The inter-rater agreement was substantial for sensors and spectroscopy (pooled quadratic κ = 0.73-0.83; up to 0.93 by dimension) and modest for imaging/AI (PA vs. Author 2: κquadratic=0.30-0.44), supporting rubric stability with adjacency-dominated disagreements. The decision maps operationalize these findings, helping practitioners select a fit-for-purpose modality and encouraging a minimum PCI metadata set to enable reproducible, deployment-oriented comparisons.