Pest Monitoring with AI-Enabled Camera-Based Pheromone Traps in Orchards with Different Climatic and Topographic Characteristics

Year 2025, Volume: 9 Issue: Special, 162 - 171, 28.12.2025

Medet İtmeç , Barış Zorlu

https://doi.org/10.31015/2025.si.24

Abstract

Intensive pesticide use in conventional orcharding threatens ecosystem stability, food safety, and export potential, making accurate and timely pest monitoring indispensable for sustainable agriculture. Advanced digital tools, particularly artificial intelligence (AI)-supported pheromone traps, provide a promising alternative to conventional monitoring methods by enabling continuous, automated, and labor-efficient surveillance. This study aimed to evaluate the effectiveness of AI-enabled camera-based pheromone traps for detecting orchard pests under diverse climatic and topographic conditions in Turkey. Experiments were conducted in peach, pomegranate, and citrus orchards (2 ha each) located in Mersin, Konya, and Manisa, representing Mediterranean, continental, and transitional climates. Pheromone traps (iMETOS iSCOUT®), integrated with the FieldClimate platform, automatically captured high-resolution images of insects up to three times daily, which were processed through AI-based algorithms for pest identification and counting. Comparative analyses revealed significant regional variation in pest populations (p<0.05). For example, whitefly densities in citrus orchards averaged 170.7±66.3 in Mersin, 90.4±23.8 in Konya, and 140.5±55.2 in Manisa (p<0.001). Similarly, Mediterranean fruit fly densities peaked at 270.3±84.4 in Mersin compared with 76.3±21.3 in Konya and 185.3±74.0 in Manisa (p<0.001). Pest activity in Mersin spanned nearly the entire year, while Konya’s continental climate restricted populations to short summer periods, and Manisa exhibited intermediate, prolonged pest presence. In conclusion, AI-enabled traps provided robust, location-specific monitoring of pest dynamics, delivering reliable early-warning data to optimize pesticide applications. This approach reduces unnecessary spraying, mitigates environmental contamination, and supports region-specific integrated pest management strategies.

Keywords

AI Based Detection , Integrated Pest Management , Pheromone Traps , Precision Agriculture

Thanks

We would like to thank Fikrîye Koç, owner of Metos Türkiye, for her valuable contributions to our research.

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