Beyond Algorithmic Accuracy: Field Readiness, Ecological Intelligence and Real-World Validation of Artificial Intelligence for Insect Pest Management.
- 1Vocational School of Agriculture, Dicle University, Diyarbakır 21280, Türkiye.
- 2Department of Plant Protection, Faculty of Agriculture, Dicle University, Diyarbakır 21280, Türkiye.
Abstract
Artificial intelligence (AI) is rapidly reshaping insect pest identification, monitoring, and decision support, yet field maturity is often judged from algorithmic accuracy on curated or internally split datasets. Such evidence is insufficient for integrated pest management (IPM), where decisions depend on pest abundance, crop phenology, natural enemies, weather, economic thresholds, treatment costs, and asymmetric error consequences. This critical review synthesizes the literature on computer vision, automated counting, smart traps, Internet of Things (IoT), edge AI, multimodal sensing, population forecasting, uncertainty-aware AI, decision support, and precision intervention. We examine the laboratory-to-field generalization gap, small-object and long-tailed recognition, ecological incompleteness of pest-only datasets, sensor and trap bias, domain shift, calibration, human oversight, and operational constraints. We propose layered evaluation metrics spanning technical, quantitative, ecological, generalization, operational, decision, and outcome performance, and introduce a six-level Pest-AI Readiness Framework (PARF), from laboratory recognition to closed-loop precision management. We argue that future pest-AI systems should integrate visual observations with weather, crop phenology, trap history, and natural-enemy information, quantify uncertainty, support expert escalation, and undergo prospective external validation to generate timely, selective, and ecologically sound IPM decisions.
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BibTeX
@article{akbal2026beyond,
title = {{Beyond Algorithmic Accuracy: Field Readiness, Ecological Intelligence and Real-World Validation of Artificial Intelligence for Insect Pest Management.}},
author = {Eda Budak Akbal and Erol Bayhan},
journal = {Insects},
year = {2026},
month = sep,
doi = {10.3390/insects17101009},
url = {https://europepmc.org/article/MED/42940919},
note = {CC BY},
}
RIS
TY - JOUR
TI - Beyond Algorithmic Accuracy: Field Readiness, Ecological Intelligence and Real-World Validation of Artificial Intelligence for Insect Pest Management.
AU - Eda Budak Akbal
AU - Erol Bayhan
PY - 2026
DA - 2026/09/30
JO - Insects
DO - 10.3390/insects17101009
UR - https://europepmc.org/article/MED/42940919
AB - Artificial intelligence (AI) is rapidly reshaping insect pest identification, monitoring, and decision support, yet field maturity is often judged from algorithmic accuracy on curated or internally split datasets. Such evidence is insufficient for integrated pest management (IPM), where decisions depend on pest abundance, crop phenology, natural enemies, weather, economic thresholds, treatment costs, and asymmetric error consequences. This critical review synthesizes the literature on computer vision, automated counting, smart traps, Internet of Things (IoT), edge AI, multimodal sensing, population forecasting, uncertainty-aware AI, decision support, and precision intervention. We examine the laboratory-to-field generalization gap, small-object and long-tailed recognition, ecological incompleteness of pest-only datasets, sensor and trap bias, domain shift, calibration, human oversight, and operational constraints. We propose layered evaluation metrics spanning technical, quantitative, ecological, generalization, operational, decision, and outcome performance, and introduce a six-level Pest-AI Readiness Framework (PARF), from laboratory recognition to closed-loop precision management. We argue that future pest-AI systems should integrate visual observations with weather, crop phenology, trap history, and natural-enemy information, quantify uncertainty, support expert escalation, and undergo prospective external validation to generate timely, selective, and ecologically sound IPM decisions.
SN - 2075-4450
ER -