Determination of Internal Quality Indices in Oriental Melon Using Snapshot-Type Hyperspectral Image and Machine Learning Model

Abstract

In this study, we aimed to develop a prediction model of the solid solutions concentration (SSC) and moisture content (MC) in oriental melon with snapshot-type hyperspectral imagery (Visible (VIS): 460–600 nm, 16 bands; Red-Near infrared (Red-NIR): 600–860 nm, 15 bands) using a machine learning model. The oriental melons were cultivated in a hydroponic greenhouse, Republic of Korea, and a total of 91 oriental melons that were harvested from March to April of 2022 were used as samples. The SSC and MC of the oriental melons were measured using destructive methods after taking hyperspectral imagery of the oriental melons. The reflectance spectrum obtained from the hyperspectral imagery was processed by the standard normal variate (SNV) method. Variable importance in projection (VIP) scores were used to select the bands related to SSC and MC. As a result, ten (609, 736, 561, 849, 818, 489, 754, 526, 683, and 597 nm) and six (609, 736, 561, 818, 849, and 489 nm) bands were selected for the SSC and MC, respectively. Four machine learning models, support vector regression (SVR), ridge regression (RR), K-nearest neighbors regression (K-NNR), and random forest regression (RFR), were used to develop models to predict SSC and MC, and their performances were compared. The SVR showed the best performance for predicting both the SSC and MC of the oriental melons. The SVR model achieved a relatively high accuracy with R2 values of 0.86 and 0.74 and RMSE values of 1.06 and 1.05 for SSC and MC, respectively. However, it will be necessary to carry out more experiments under various conditions, such as differing maturities of fruits and varying light sources and environments, to achieve more comprehensive predictions and apply them to monitoring robots in the future. Nevertheless, it is considered that the snapshot-type hyperspectral imagery aided by SVR would be a useful tool to predict the SSC and MC of oriental melon. In addition, if the maturity classification model for the oriental melon can be applied to fields, it could lead to less labor and result in high-quality oriental melon production.

Prediction of Soluble-Solid Content in Citrus Fruit Using Visible–Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm

“…In total, 648 HSI data points were obtained. Dark and white reference images were obtained to correct the noise generated by the device and scattered light in the citrus sample hyperspectral images [ 18 ]. Dark reference images were obtained without light source exposure, and white reference images were obtained using diffuse reflectance standards (Labsphere, North Sutton, NH, USA).…”

“…The actual SSC in the citrus fruits was compared with those predicted from the calibration (cross-validation) or independent validation datasets using the PLSR models. The performance of each model was evaluated by estimating the coefficient of determination of the calibration set (R c 2 ), the cross-validation set (R v 2 ), and the prediction set (R p 2 ), as well as root mean square error of calibration (RMSEC), cross-validation set (RMSEV), and prediction (RMSEP), in addition to the optimal factor (F) [ 18 , 27 ]. The model with the highest R v 2 and lowest RMSEV values was selected as the optimal model.…”

“…2 ), the cross-validation set (R v 2 ), and the prediction set (R p 2 ), as well as root mean square error of calibration (RMSEC), cross-validation set (RMSEV), and prediction (RMSEP), in addition to the optimal factor (F) [18,27]. The model with the highest R v 2 and lowest RMSEV values was selected as the optimal model.…”

Hyperspectral imaging‐based erythema classification in atopic dermatitis

“…To correct the effects of noise generated from the device, such as pattern noise and signal changes between images due to uneven lighting in the acquired raw HS image, white and dark reference images were acquired, and the reflectance was calculated. 18 A dark reference image was acquired when the camera shutter of the HSI system was closed to account for the internal noise caused by the dark current, and a white reference image was obtained using a 95% white reference panel (SG3151-U, IMEC, Leuven, Belgium) to consider the distribution of light immediately after imaging the target area. Subsequently, the relative reflectance image I ref was obtained using Equation (1).…”

Non-destructive determination of ripening in melon fruit using time-resolved spectroscopy

“…Thus, spectroscopic techniques being fast, simple, and costeffective, have been widely studied. In melons, VisNIR spec troscopy (Sanchez et al, 2014;Lu et al, 2015;Khurnpoon and Sirisomboon, 2018;Zeb et al, 2021), computer vision (Calixto et al, 2022 and hyperspec tral imaging (Sun et al, 2017;Cho et al, 2022) were studied with less or more successful results for pre dicting soluble solids, moisture, pulp color, and firm ness and for classifying fruit according to different sweetness degree or as suitable or not for harvesting. Among spectroscopic techniques, timeresolved reflectance spectroscopy (TRS) is gaining increasing interest in assessing fruit quality.…”