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

  • Dennys Jhon Báez-Sánchez
  • , Julio Montesdeoca
  • , Brayan Saldarriaga-Mesa
  • , Gaston Gaspoz
  • , Santiago Tosetti
  • , Flavio Capraro

Producción científica: Contribución a una revistaCríticarevisión exhaustiva

Resumen

Highlights: What are the main findings? We introduced a modality-aware PCI rubric (performance–cost–implementability) with inter-rater (Formula presented.) to compare vision/AI, spectroscopy, and indirect sensor systems for pest detection in edible crops. We derived compact decision maps that translate PCI evidence into field-ready choices under the constraints of power, cost, maintenance, connectivity, and required action granularity. What is the implication of the main finding? Practitioners can choose fit-for-purpose sensing modalities beyond accuracy-only benchmarks, improving the time to deployment. Reporting a minimum PCI metadata set enables reproducible, deployment-oriented comparisons across future studies. 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 (Formula presented.). 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 (Formula presented.) ; sensor nodes spanning balanced (Formula presented.) and ultra-lean (Formula presented.) trade-offs; and the spectroscopy split between the early-warning strength (Formula presented.) and portability (Formula presented.). The inter-rater agreement was substantial for sensors and spectroscopy (pooled quadratic (Formula presented.) = 0.73–0.83; up to 0.93 by dimension) and modest for imaging/AI (PA vs. Author 2: (Formula presented.) – (Formula presented.)), 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.

Idioma originalInglés
Número de artículo6620
PublicaciónSensors
Volumen25
N.º21
DOI
EstadoPublicada - nov. 2025

Nota bibliográfica

Publisher Copyright:
© 2025 by the authors.

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