Artificial Intelligence in Entomology: Smart Detection, Identification and Management of Insect Pests
DOI:
https://doi.org/10.55544/jrasb.5.4.14Keywords:
Artificial Intelligence, Entomology, Insect Pest Detection, Pest Identification, Machine Learning, Deep Learning, Computer Vision, Automated Insect Traps, Optical Sensing, Precision Agriculture, Smart Pest Management, Image Recognition, Automated Surveillance, Uncertainty Estimation, Integrated Pest Management (IPM), Agricultural Monitoring, Insect Classification, Pest Prediction, Crop Protection, Digital EntomologyAbstract
Artificial intelligence can expand the frequency and scale of insect surveillance, but its agricultural value depends on the decisions supported by its predictions. This review examines image recognition, automated traps, optical sensing, uncertainty estimation and their integration into pest management. Published studies demonstrate progress in identifying insects under controlled and field conditions, including recognition across thousands of species and evaluation on independent greenhouse observations. However, image classification accuracy, trap counts and crop protection outcomes represent different quantities. Dataset imbalance, unfamiliar species, repeated observations and changing environmental conditions complicate their interpretation. A critical synthesis identifies three requirements for dependable deployment: taxonomically defensible reference data, validation across sites and seasons, and explicit links between observations and management thresholds. Six original figures distinguish reported measurements from conceptual and mathematical illustrations. Developments available through 2026 support systems that combine automated surveillance with expert verification and ecological knowledge. Evidence of sustained economic and environmental benefit remains more limited than evidence of recognition performance.
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