Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource K.S., Shanthini; George, Sudhish N.; O.V., Athul Chandran; K.M., Jinumol; P., Keerthana; Francis, Jobin; George, Sony Title A name given to the resource NorBlueNet: Hyperspectral imaging-based hybrid CNN-transformer model for non-destructive SSC analysis in Norwegian wild blueberries Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Computers and Electronics in Agriculture;Volume;235;Issue;;Article No.;110340; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.compag.2025.110340" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.compag.2025.110340</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002221359?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002221359?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant K.S. S., Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, India; George S.N., Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, India; O.V. A.C., Department of Applied Electronics and Instrumentation Engineering, Government Engineering College Kozhikode, Kerala, India; K.M. J., Department of Applied Electronics and Instrumentation Engineering, Government Engineering College Kozhikode, Kerala, India; P. K., Department of Applied Electronics and Instrumentation Engineering, Government Engineering College Kozhikode, Kerala, India; Francis J., Department of Computer Science, Christ University Bangalore, Karnataka, India; George S., Department of Computer Science, Norwegian University of Science and Technology Gjik, Norway Description An account of the resource Soluble solids content (SSC) is a vital parameter in blueberries, reflecting the concentration of dissolved sugars (primarily fructose and glucose) and directly influencing the fruit's sweetness, flavour, and ripeness. As part of this study, Norwegian wild blueberries were carefully hand-picked from a forest in Norway and subsequently imaged using a hyperspectral camera to capture their detailed spectral characteristics. This study introduces NorBlueNet, a hybrid CNN-transformer architecture, for accurately predicting SSC in wild blueberries through hyperspectral imaging and deep learning. This hybrid architecture combines CNN layers for local feature extraction and spatial hierarchy representation, followed by transformer layers that capture global relationships and long-range dependencies. The hybrid approach combines the computational advantages of CNNs with the advanced attention mechanisms of transformers, achieving enhanced accuracy while maintaining computational efficiency. A comprehensive evaluation is conducted by comparing the proposed model with two additional deep learning models on the custom dataset. The results indicate that the NorBlueNet achieves the highest prediction accuracy, with an R2 = 0.98, RMSE = 0.0136, and RPD = 9.3759 thereby demonstrating its superior performance. To foster community engagement, collaboration and facilitate re-implementation of our work, we have made our code available at:https://github.com/NorBlueNet. 2025 Subject The topic of the resource Fruit quality analysis; Hyperspectral images; Non-destructive analysis; SSC prediction model Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 1681699; CODEN: CEAGE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online