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An intelligent system for detecting Mediterranean fruit fly

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dc.contributor.author Uzun, Yusuf
dc.contributor.author Tolun, Mehmet Resit
dc.contributor.author Eyyuboglu, Halil Tanyer
dc.contributor.author Sarı, Filiz
dc.date.accessioned 2024-02-28T12:17:00Z
dc.date.available 2024-02-28T12:17:00Z
dc.date.issued 2022-09-09
dc.identifier.citation Uzun, Yusuf;...et.al. (2022). "An intelligent system for detecting Mediterranean fruit fly", Journal of Agricultural Engineering, Vol.53, No.3. tr_TR
dc.identifier.issn 19747071
dc.identifier.uri http://hdl.handle.net/20.500.12416/7340
dc.description.abstract Nowadays, the most critical agriculture-related problem is the harm caused to fruit, vegetable, nut, and flower crops by harmful pests, particularly the Mediterranean fruit fly, Ceratitis capitata, named Medfly. Medfly's existence in agricultural fields must be monitored systematically for effective combat against it. Special traps are utilised in the field to catch Medflies which will reveal their presence and applying pesticides at the right time will help reduce their population. A technologically supported automated remote monitoring system should eliminate frequent site visits as a more economical solution. This paper develops a deep learning system that can detect Medfly images on a picture and count their numbers. A particular trap equipped with an integrated camera that can take photos of the sticky band where Medflies are caught daily is utilised. Obtained pictures are then transmitted by an electronic circuit containing a SIM card to the central server where the object detection algorithm runs. This study employs a faster region-based convolutional neural network (Faster R-CNN) model in identifying trapped Medflies. When Medflies or other insects stick on the trap's sticky band, they spend extraordinary effort trying to release themselves in a panic until they die. Therefore, their shape is badly distorted as their bodies, wings, and legs are buckled. The challenge is that the deep learning system should detect these Medflies of distorted shape with high accuracy. Therefore, it is crucial to utilise pictures containing trapped Medfly images with distorted shapes for training and validation. In this paper, the success rate in identifying Medflies when other insects are also present is approximately 94%, achieved by the deep learning system training process, owing to the considerable amount of purpose-specific photographic data. This rate may be seen as quite favourable when compared to the success rates provided in the literature. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.4081/jae.2022.1381 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Automatic Pest Monitoring tr_TR
dc.subject Deep Learning tr_TR
dc.subject E-Trap tr_TR
dc.subject Faster R-CNN tr_TR
dc.subject Fight Against Mediterranean Fruit Fly tr_TR
dc.subject Pesticide Optimisation İn Agriculture tr_TR
dc.title An intelligent system for detecting Mediterranean fruit fly tr_TR
dc.type article tr_TR
dc.relation.journal Journal of Agricultural Engineering tr_TR
dc.identifier.volume 53 tr_TR
dc.identifier.issue 3 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Yazılım Mühendisliği Bölümü tr_TR


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