Real-Field CNN Detection of the Olive Fruit Fly: Towards Smarter Pest Monitoring in Olive Orchards

Real-Field CNN Detection of the Olive Fruit Fly: Towards Smarter Pest Monitoring in Olive Orchards | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Real-Field CNN Detection of the Olive Fruit Fly: Towards Smarter Pest Monitoring in Olive Orchards Tomislav Kos, Anđelo Zdrilić, Ana Gašparović Pinto, Šimun Kolega, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7741828/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The olive fruit fly (Bactrocera oleae Rossi, 1790) is the most damaging pest of olive cultivation, threatening both yield and quality of olive oil across the Mediterranean. Early and accurate detection of adult flies is a cornerstone of integrated pest management, yet conventional monitoring methods based on manual trap inspection remain slow and labor-intensive. In this study, we developed and validated a convolutional neural network (CNN) model trained exclusively on a real-field dataset of 4,278 images collected in olive orchards in Zadar County, Croatia. The model was designed to distinguish B. oleae adults from other insects captured on adhesive traps. It achieved a mean average precision (mAP) of 0.74, with particularly strong performance for the olive fly class (AP = 0.81) and high accuracy at Intersection over Union (IoU) = 50% (AP50 = 0.95). Our results demonstrate that CNN-based detection models trained on field data can provide fast, reliable, and scalable pest monitoring solutions. This approach holds promise for reducing the reliance on manual inspections and supports the development of more sustainable and precise pest management strategies in olive production. Bactrocera oleae Rossi 1790 Oleae europaea L. integrated pest management (IPM) precision agriculture convolutional neural networks (CNNs) Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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