Citation

Tan, Mas Ira Syafila Mohd Hilmi and Wong, Lai Kuan and Loh, Yuen Peng and Pee, Chih Yang (2026) Unlocking the potential of 1D to 2D transformation in visible and near-infrared (VIS/NIR) spectroscopy for improved plant disease and stress detection: A review. Microchemical Journal, 223. p. 116932. ISSN 0026265X

Text Unlocking the potential of 1D to 2D transformation in visible and near-infrared (VIS_NIR) spectroscopy for improved plant disease and stress detection_ A review - ScienceDirect.pdf - Published Version Restricted to Repository staff only Download (308kB)

Abstract

Visible and near-infrared (VIS–NIR) spectroscopy has been widely recognized for non-destructive plant disease detection and species classification, traditionally utilizing one-dimensional (1D) spectral data. However, the potential of transforming 1D spectral data into two-dimensional (2D) formats such as images, spectrograms, and wavelets remains largely underexplored. This review was performed through a structured literature search of major scientific databases, applying predefined inclusion and exclusion criteria to identify studies on VIS–NIR spectroscopy and 1D-to-2D spectral transformation techniques. This review explores the various transformation techniques with their integration with machine learning and deep learning algorithms, highlighting the ability of 2D spectral data to significantly enhance classification accuracy, capture complex features, and reveal intricate patterns that are not discernible in 1D data. By examining a variety of transformation techniques and their applications in plant disease detection, soil classification, and other agricultural domains, this paper demonstrates how 2D spectral representations enhance model accuracy and interpretability. In addition, it discusses the practical implications of integrating 2D VIS–NIR spectroscopy into precision agriculture workflows, emphasizing its potential for field-deployable real-time diagnostics. Overall, this review highlights 2D transformation as a promising advancement over traditional 1D chemometric techniques, unlocking new opportunities in agriculture and smart farming.

Item Type:	Article
Uncontrolled Keywords:	Plant disease, plant stress
Subjects:	S Agriculture > SB Plant culture
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Mar 2026 02:06
Last Modified:	02 Mar 2026 02:06
URII:	http://shdl.mmu.edu.my/id/eprint/15402

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