A hybrid deep learning approach combining neutrosophic set theory and multi-axis vision transformer for nutrient deficiency classification in plants

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dc.contributor.authorSert, Eser
dc.contributor.authorKiziloluk, Soner
dc.date.accessioned2026-06-19T06:39:59Z
dc.date.available2026-06-19T06:39:59Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractPlant nutrient deficiencies in agricultural production, especially nitrogen and potassium deficiencies, threaten global food security. The fact that traditional detection methods are time-consuming, costly, and require expertise increases the need for automatic and reliable classification systems. Current deep learning models lack the ability to assess the reliability of their predictions, which can lead to misclassifications in low-quality or ambiguous images. In order to overcome these limitations, an innovative approach that combines Neutrosophic Set theory with the Multi-Axis Vision Transformer (MaxViT), one of the latest Vision Transformer (ViT) models, namely Neutrosophic-MaxViT, is proposed in this study. The proposed model increases the explicability and reliability of classification results by integrating MaxViT's hybrid architecture, which includes local convolutions and global attention mechanisms, with the truth (T), indeterminacy (I), and falsity (F) components of Neutrosophic Set theory. In addition, a special Neutrosophic Entropy Loss Function has been developed that optimizes the T, I, and F components. The performance of the model was evaluated on the Early Nutritional Stress Detection of Plants (EarlyNSD) dataset, which includes healthy, nitrogen, and potassium-deficient conditions of ashgourd, bittergourd, and snakegourd plants. The experimental results show that the Neutrosophic-MaxViT model outperforms advanced models, including MaxViT, Regular Networks Y (RegNetY), Convolutional Neural Network Next (ConvNeXt), and ViT-Tiny, achieving 95.31 % accuracy and a macro F1-score of 0.9529. This study contributes to artificial intelligence-based agricultural solutions, enhancing productivity and offering a reliable framework for sustainable practices.
dc.identifier.doi10.1016/j.engappai.2025.112705
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.orcid0000-0002-0381-9631
dc.identifier.scopus2-s2.0-105019050015
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2025.112705
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5895
dc.identifier.volume162
dc.identifier.wosWOS:001597689300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofEngineering Applications of Artificial Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260612
dc.subjectNeutrosophic Set Theory
dc.subjectMulti-Axis Vision Transformer
dc.subjectPlant Nutrient Deficiency
dc.subjectVision Transformer
dc.subjectDeep Learning
dc.titleA hybrid deep learning approach combining neutrosophic set theory and multi-axis vision transformer for nutrient deficiency classification in plants
dc.typeArticle

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