Tree fruit load calculation with image processing techniques

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dc.authorid0000-0001-7911-6388
dc.authorid0000-0001-8863-8348
dc.contributor.authorAral, Merve
dc.contributor.authorMisk, Nada
dc.contributor.authorSilahtaroğlu, Gökhan
dc.date.accessioned2024-06-10T07:26:19Z
dc.date.available2024-06-10T07:26:19Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü
dc.description.abstractTurkey holds a significant position in global olive production, with olives being a crucial component of its agricultural industry. The fruit load on trees directly correlates with olive tree yield, which in turn determines productivity. The Tabit Smart Agriculture R&D Center, located in the Koçarlı district of Aydın within Turkey’s Aegean region, conducted a study using the YOLOv3 Convolutional Neural Network model to estimate olive tree loads. The primary aim of this research was to offer a more precise and objective perspective on olive harvesting, moving away from subjective assumptions based on predictions. Olive trees, playing a significant role in Turkey’s agricultural output, are cultivated across various regions in the country. However, olive sales in Turkey still rely on approximations. To tackle this, image processing techniques were employed to introduce a more technological and practical approach to agricultural applications, particularly in estimating olive tree loads. Throughout the study, real-time datasets were generated by capturing images of olive trees at the Tabit Smart Agriculture R&D Center in Aydın. The focus was on accurately detecting and counting olives. After 6000 iterations, the obtained results were as follows: mAP 61%, Precision 70%, Recall 45%. The results of the study proved that the agricultural industry can actively shape the trajectory of future farming practices by adeptly embracing image processing techniques and deep learning models.
dc.identifier.citationAral, M., Misk, N. ve Silahtaroğlu, G. (2024). Tree fruit load calculation with image processing techniques. International Conference on Emerging Trends and Applications in Artificial Intelligence, ICETAI 2023 içinde 960, (137-147. ss.). İstanbul, September 8-9, 2023. http://dx.doi.org/10.1007/978-3-031-56728-5_12
dc.identifier.doi10.1007/978-3-031-56728-5_12
dc.identifier.endpage147
dc.identifier.isbn9783031567278
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85193583513
dc.identifier.scopusqualityQ4
dc.identifier.startpage137
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-031-56728-5_12
dc.identifier.urihttps://hdl.handle.net/20.500.12511/12601
dc.identifier.volume960
dc.indekslendigikaynakScopus
dc.institutionauthorAral, Merve
dc.institutionauthorMisk, Nada
dc.institutionauthorSilahtaroğlu, Gökhan
dc.language.isoen
dc.relation.ispartofInternational Conference on Emerging Trends and Applications in Artificial Intelligence, ICETAI 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDeep Learning
dc.subjectImage Processing
dc.subjectOlive Trees
dc.subjectYOLOV3
dc.titleTree fruit load calculation with image processing techniques
dc.typeConference Object

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