NorBlueNet: Hyperspectral imaging-based hybrid CNN-transformer model for non-destructive SSC analysis in Norwegian wild blueberries
subject: Fruit quality analysis; Hyperspectral images; Non-destructive analysis; SSC prediction model
description: 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
creator: K.S., Shanthini; George, Sudhish N.; O.V., Athul Chandran; K.M., Jinumol; P., Keerthana; Francis, Jobin; George, Sony
source: Computers and Electronics in Agriculture;Volume;235;Issue;;Article No.;110340;
publisher: Elsevier B.V.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 1681699; CODEN: CEAGE
format: online
language: English
type: Article
coverage: 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