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The application of hyperspectral imaging (HSI) has gained significant importance in the past decade, particulary in the context of food analysis, including potatoes. However, the current literature lacks a comprehensive systematic review of the application of this technique in potato cultivation. Therefore, the aim of this work was to conduct a systematized review by analysing the most relevant compounds, diseases and stress factors in potatoes using hyperspectral imaging. For this purpose, scientific studies were retrieved through a systematic keyword search in Web of Science and Scopus databases. Studies were only included in the review if they provided at least one set of quantitative data. As a result, a total of 52 unique studies were included in the review. Eligible studies were assigned an in-house developed quality scale identifying them as high, medium or low risk. In most cases the studies were rated as low risk. Finally, a comprehensive overview of the HSI applications in potatoes was performed. It has been observed that most of the selected studies obtained better results using linear methods. In addition, a meta-analysis of studies based on regression and classification was attempted but was not possible as not enough studies were found for a specific variable.
The application of hyperspectral imaging (HSI) has gained significant importance in the past decade, particulary in the context of food analysis, including potatoes. However, the current literature lacks a comprehensive systematic review of the application of this technique in potato cultivation. Therefore, the aim of this work was to conduct a systematized review by analysing the most relevant compounds, diseases and stress factors in potatoes using hyperspectral imaging. For this purpose, scientific studies were retrieved through a systematic keyword search in Web of Science and Scopus databases. Studies were only included in the review if they provided at least one set of quantitative data. As a result, a total of 52 unique studies were included in the review. Eligible studies were assigned an in-house developed quality scale identifying them as high, medium or low risk. In most cases the studies were rated as low risk. Finally, a comprehensive overview of the HSI applications in potatoes was performed. It has been observed that most of the selected studies obtained better results using linear methods. In addition, a meta-analysis of studies based on regression and classification was attempted but was not possible as not enough studies were found for a specific variable.
Over the past two decades, hyperspectral imaging has become popular for non-destructive assessment of food quality, safety, and crop monitoring. Imaging delivers spatial information to complement the spectral information provided by spectroscopy. The key challenge with hyperspectral image data is the high dimensionality. Each image captures hundreds of wavelength bands. Reducing the number of wavelengths to an optimal subset is essential for speed and robustness due to the high multicollinearity between bands. However, there is yet to be a consensus on the best methods to find optimal subsets of wavelengths to predict attributes of samples. A systematic review procedure was developed and applied to review published research on hyperspectral imaging and wavelength selection. The review population included studies from all disciplines retrieved from the Scopus database that provided empirical results from hyperspectral images and applied wavelength selection. We found that 799 studies satisfied the defined inclusion criteria and investigated trends in their study design, wavelength selection, and machine learning techniques. For further analysis, we considered a subset of 71 studies published in English that incorporated spatial/texture features to understand how previous works combined spatial features with wavelength selection. This review ranks the wavelength selection techniques from each study to generate a table of the comparative performance of each selection method. Based on these findings, we suggest that future studies include spatial feature extraction methods to improve the predictive performance and compare them to a broader range of wavelength selection techniques, especially when proposing novel methods.
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