A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture

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dc.authorid0000-0002-6870-6558
dc.contributor.authorGülmez, Burak
dc.contributor.authorid222298
dc.date.accessioned2025-03-19T07:01:42Z
dc.date.available2025-03-19T07:01:42Z
dc.date.issued2024-09
dc.departmentFakülteler, Mühendislik, Mimarlık ve Tasarım Fakültesi, Endüstri Mühendisliği Bölümü
dc.descriptionScience Citation Index Expanded (SCI-EXPANDED)
dc.description.abstractThis review paper investigates the utilization of Convolutional Neural Networks (CNNs) for disease detection in potato agriculture, highlighting their pivotal role in efficiently analyzing large-scale agricultural datasets. The datasets used, preprocessing methodologies applied, specific data collection zones, and the efficacy of prominent algorithms like ResNet, VGG, and MobileNet variants for disease classification are scrutinized. Additionally, various hyperparameter optimization techniques such as grid search, random search, genetic algorithms, and Bayesian optimization are examined, and their impact on model performance is assessed. Challenges including dataset scarcity, variability in disease symptoms, and the generalization of models across diverse environmental conditions are addressed in the discussion section. Opportunities for advancing CNN-based disease detection, including the integration of multi-spectral imaging and remote sensing data, and the implementation of federated learning for collaborative model training, are explored. Future directions propose research into robust transfer learning techniques and the deployment of CNNs in real-time monitoring systems for proactive disease management in potato agriculture. Current knowledge is consolidated, research gaps are identified, and avenues for future research in CNN-based disease detection strategies to sustain potato farming effectively are proposed by this review. This study paves the way for future advancements in AI-driven disease detection, potentially revolutionizing agricultural practices and enhancing food security. Also, it aims to guide future research and development efforts in CNN-based disease detection for potato agriculture, potentially leading to improved crop management practices, increased yields, and enhanced food security.
dc.identifier.citationGülmez, B. (2024). A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture. Potato Research, 1-35. https://doi.org/10.1007/s11540-024-09786-1
dc.identifier.doi10.1007/s11540-024-09786-1
dc.identifier.eissn1871-4528
dc.identifier.endpage35
dc.identifier.issn0014-3065
dc.identifier.startpage1
dc.identifier.urihttps://dspace.mudanya.edu.tr/handle/20.500.14362/301
dc.identifier.wosWOS:001297783900001
dc.identifier.wosqualityQ1
dc.institutionauthorGülmez, Burak
dc.language.isoen
dc.publisherSpringer
dc.relation.journalPotato Research
dc.relation.publicationcategoryMakale- Uluslararası- Hakemli Dergi- Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial intelligence
dc.subjectComputer vision
dc.subjectConvolutional Neural Networks
dc.subjectPotato disease detection
dc.titleA Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture
dc.typeMakale
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