Multispectral Vision Transformers for Non-Destructive Quality Assessment of Fresh Produce Under Dynamic Environmental Conditions
Keywords:
multispectral imaging; vision transformers; fresh produce quality; non-destructive assessment; dynamic environments; system architectureAbstract
The assessment of fresh produce quality remains a central challenge in postharvest supply chains because visible appearance alone cannot reliably capture internal ripeness, firmness, moisture content, and incipient defects. Multispectral imaging provides richer spectral information, but its practical adoption is constrained by dynamic environmental conditions, hardware heterogeneity, and the need for robust interpretation. This paper presents a system-level examination of multispectral vision transformers for non-destructive quality assessment of fresh produce under variable illumination, temperature, humidity, and handling conditions. Rather than focusing on a single algorithmic innovation, the work analyzes the architectural trade-offs of transformer-based spectral-spatial representation, including global attention, hierarchical tokenization, and transfer learning, in relation to conventional convolutional and spectral processing approaches. The discussion addresses infrastructure requirements, data governance, dataset bias, calibration drift, edge deployment, and sustainability. It considers how self-attention can integrate spectral and spatial context without requiring explicit feature engineering, while also noting computational costs and the need for curated spectral datasets. The paper further examines robustness mechanisms including spectral normalization, domain adaptation, and uncertainty-aware decision making. Cross-domain lessons from remote sensing, biomedical imaging, and precision agriculture are used to contextualize design choices. The analysis suggests that vision transformers can serve as an enabling backbone for resilient quality assessment systems, provided that institutional and technical safeguards are aligned with operational realities. A coordinated approach spanning sensing, model governance, and supply chain integration is necessary to translate laboratory performance into dependable field deployment.
References
1. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations.
2. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
3. Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121–1142.
4. Qin, J., Chao, K., Kim, M. S., Lu, R., & Burks, T. F. (2013). Hyperspectral and multispectral imaging for evaluating food safety and quality. Journal of Food Engineering, 118(2), 157–171.
5. Nicolai, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., & Lammertyn, J. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46(2), 99–118.
6. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).
7. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 234–241). Springer.
8. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. In European Conference on Computer Vision (pp. 213–229). Springer.
9. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10012–10022).
10. Tang, G., Zheng, I., Teal, P. D., Zhang, F. L., & Doronin, A. (2026, March). Real-time monitoring of dynamic reflectance using a compact multispectral imaging and machine learning system. In Dynamics and Fluctuations in Biomedical Photonics XXIII (Vol. 13850, pp. 4-11). SPIE.
11. Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., & Jégou, H. (2021). Training data-efficient image transformers & distillation through attention. In International Conference on Machine Learning (pp. 10347–10357). PMLR.
12. Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151, 61–69.
13. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90.
14. Hong, D., Gao, L., Yao, J., Zhang, B., Plaza, A., & Chanussot, J. (2022). SpectralFormer: Rethinking hyperspectral image classification with transformers. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–15.
15. Paoletti, M. E., Haut, J. M., Plaza, J., & Plaza, A. (2019). Deep learning classifiers for hyperspectral imaging: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 279–317.
16. Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31–48.
17. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big data in smart farming–a review. Agricultural Systems, 153, 69–80.
18. Barbedo, J. G. A. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering, 180, 96–107.
19. Mishra, P., Asaari, M. S. M., Herrero-Langreo, A., Lohumi, S., Diezma, B., & Scheunders, P. (2017). Close range hyperspectral imaging of plants: A review. Biosystems Engineering, 164, 49–67.
20. Van der Meer, F. D., van der Werff, H. M. A., van Ruitenbeek, F. J. A., Hecker, C. A., Bakker, W. H., Noomen, M. F., van der Meijde, M., Carranza, E. J. M., de Smeth, J. B., & Woldai, T. (2012). Multi- and hyperspectral geologic remote sensing: A review. International Journal of Applied Earth Observation and Geoinformation, 14(1), 112–128.
21. van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709.
22. Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., Gao, J., & Zhang, L. (2020). Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241, 111716.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Clinical and Translational Medicine
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
