# Rapid Estimation of Moisture Content in Unpeeled Potato Tubers Using Hyperspectral Imaging
###### Abstract
The authors stated that the scanning technology of HSI is more accurate than NIRS. The authors added that HSI can scan thousands to millions of pixels within an image compared to NIRS, which scans just one point. Due to the fact that they extract spectra sets made up of hundreds of bands across pictures with high spatial resolution, hyperspectral sensors typically produce enormous volumes of data [14]. Remote sensing, especially unmanned aerial vehicle combined with an HSI sensor, is used in various areas, such as observing crop cultivation stages, estimation of parameters (e.g., carotenoid content, moisture content, and biomass), and disease monitoring, as they have a wider spectral range and narrow bands. [PERSON] et al. [16] predicted the chlorophyll concentration and NDRE of spring wheat using Rikola hyperspectral HSI with UAV, which outperformed color infrared (CIR) imagery. [PERSON] et al. [17; 18] measured the chlorophyll of barley using a snapshot hyperspectral camera on a UAV. HSI is an analytical technique combining the benefit of spectroscopy and digital imaging for obtaining both spectral information of samples in the visible region and near-infrared region and spatial information. HSI has been used in agriculture for determining product quality, including physical, chemical, and biological attributes. As with near-infrared spectroscopy, this technique is rapid, nondestructive, and efficient. HSI is simple to handle, which helps in processing and analyzing the data and acquiring important information required to establish the correlation between desired quality parameters and hyperspectral image data. There have been numerous previous applications of HSI in determining the quality of agricultural produce, such as: estimation of moisture content in banana [19], glucose estimation in carrot and banana [20; 21], estimation of moisture content, firmness and pH values in apple [7], and prediction of moisture content in peanut [22]. In the case of potatoes, studies investigating quality assessment using HSI include estimation of moisture content with and without skin [23], starch, soluble sugars and amino acids estimation [24], estimation of starch content [25], evaluating sugar content [26], biomass estimation [27], detection of nitrogen stress [28], nondestructive detection of blackspot [29], classification of bruised potato [30], precautionary analysis of sprouting potato eyes [31], prediction of soluble solid content in sweet potato [32], determination of moisture content and chromacity during convective hot air drying process [4], prediction of optimal cooking time for boiled potatoes [33], detecting water stress in potatoes [34], prediction of moisture content and freezeable water content of purple-fleshed sweet potato slices [35], and prediction of color and water content of fresh cut potato slices [36]. However, there are limited studies using HSI and machine learning algorithm for the estimation of moisture content in potatoes. Therefore, this study demonstrates the potential use of HSI using machine learning for estimating the moisture content of unpeeled whole potato tubers. The ultimate aim would be to implement the HSI technique for infield operations using remote sensing sensors to reduce post-harvest loss of potato tubers.