Predicting Phenological Stages with Deep Neural Network Models to Increase Efficiency in Tomato Production

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dc.contributor.authorAzizoğlu, Fatma
dc.contributor.authorAzizoğlu, Gökhan
dc.contributor.authorToprak, Ahmet Nusret
dc.contributor.authorSağlam, Cevdet
dc.date.accessioned2024-10-26T17:51:08Z
dc.date.available2024-10-26T17:51:08Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.descriptionBerdan Civata B.C.; et al.; Figes; Koluman; Loodos; Tarsus University
dc.description32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 -- 15 May 2024 through 18 May 2024 -- Mersin -- 201235
dc.description.abstractNowadays, tomatoes are one of the most widely cultivated crops globally and domestically. However, insufficient nourishment during the phenological stages of tomato seedlings can negatively impact their productivity. This study examines the accuracy of VGG19, ResNet101, and MobileNetV2 models in predicting the phenological stages of tomato plants. The SVM algorithm is used to classify the features obtained using these architectures, and the performance of the resulting models is evaluated. MobileNetV2+SVM has shown significantly superior performance compared to other models, with an accuracy rate of 98.75%. The MobileNetV2+SVM's lightweight structure and computational efficiency demonstrate potential for high-accuracy classification even in resource-constrained environments. The characteristics of this model make it well-suited for use in agricultural and robotic applications. The high accuracy rates enable the precise application of the nutrient solution to each tomato seedling's phenological stage, boosting agricultural productivity. © 2024 IEEE.
dc.description.sponsorshipBilimsel ve Teknolojik Araştırma Projelerini Destekleme Programı kapsamında desteklenmektedir
dc.identifier.doi10.1109/SIU61531.2024.10600745
dc.identifier.isbn979-835038896-1
dc.identifier.scopus2-s2.0-85200836959
dc.identifier.urihttps://doi.org/10.1109/SIU61531.2024.10600745
dc.identifier.urihttps://hdl.handle.net/20.500.12418/26043
dc.identifier.wosWOS:001297894700026
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectagricultural productivity; deep learning; plant stage prediction; smart agriculture; unmanned aerial vehicle
dc.titlePredicting Phenological Stages with Deep Neural Network Models to Increase Efficiency in Tomato Production
dc.title.alternativeDomates Üretiminde Verimliliği Arttırmak için Derin Sinir Ağı Modelleri ile Fenolojik Evre Tahmini
dc.typeConference Object

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