# Advancing Visible Spectroscopy through Integrated Machine Learning and Image Processing Techniques
## 2 Literature Review
[21] developed MPR for haze removal from images, enhancing methods and addressing challenges in environmental monitoring and medical diagnostics using advanced spectroscopy and imaging technologies. [PERSON] et al. [22] reported that the machine learning-assisted fluorescence hyperspectral technique was beneficial for classifying rice varieties. [PERSON] et al. 's [23] literature review on machine learning methods for cancer classification using Raman spectroscopy data highlighted the popularity of deep learning models, the need to address methodological challenges, and the need for benchmark datasets. [PERSON] [24] discussed the future scope of deep learning in the field of electron microscopy, pointing towards its potential for significant advancements. [PERSON] et al. [25] utilised machine learning and genetic programming to analyse spectroscopic data, providing insights into material composition and enabling informed decision-making through interpretable rules. In a related study, [PERSON] et al. [26] presented a methodology for dermatological disease detection using image processing and machine learning, showcasing the potential of these techniques in improving diagnostic accuracy and efficiency in dermatology. [PERSON] et al. [27] focused on image processing and machine learning for the morphological analysis of blood cells, highlighting the utility of these techniques in automating cell image analysis and supporting clinical diagnosis. [PERSON] et al. [28] machine learning method interprets vibrational spectroscopy data, creating interpretable rules for explanatory analysis. [PERSON] et al. [29] developed a method for the speciation of arsenic using liquid phase microextraction coupled with flame atomic absorption spectroscopy, demonstrating its applicability in environmental and biological sample analysis. Similarly, [PERSON] and [PERSON] [30] proposed a flow injection micelle-mediated methodology for the determination of lead using electrothermal atomic absorption spectrometry, showcasing its sensitivity and accuracy in trace lead detection. [PERSON] et al. 's [31] study utilised image-based modelling and deep learning to predict the mechanical properties of heterogeneous materials, highlightingthe potential of deep learning to establish implicit mappings between macroscale and mesoscale structures, providing insights for material behaviour optimisation. Additionally, [PERSON] et al. [32] explored the performance of classification models for toxins based on Raman spectroscopy using machine learning algorithms. Their study demonstrated the effectiveness of preprocessing methods and classification models in accurately identifying protein toxins, offering promising avenues for toxin detection and public health protection. In recent years, advancements in imaging technology revolutionised various industries, including agriculture, food processing, and pharmaceuticals. For instance, [PERSON] et al. [33] introduced the Spectral Image Processing System (SIPS), facilitating the interactive visualisation and analysis of imaging spectrometer data. [PERSON] et al. [34] investigated the identification of the geographic origin of peaches using visible-near infrared spectroscopy, fluorescence spectroscopy, and image processing technology. Similarly, [PERSON] et al. [35] compared external bulk video imaging (BVI) with focused beam reflectance measurement (FBRM) and ultraviolet-visible (UV/vis) spectroscopy for identifying metastable zones in crystallisation processes. Furthermore, [PERSON] and [PERSON] [36] evaluated processed image data obtained from different commercial cameras for visible spectroscopy applications. [PERSON] et al. [37] provided a comprehensive review of wavelength selection techniques for hyperspectral image processing in the food industry. [PERSON] et al.