Non-Destructive Detection of Strawberry Quality Using Multi-Features of Hyperspectral Imaging and Multivariate Methods

Abstract

Soluble solid content (SSC), pH, and vitamin C (VC) are considered as key parameters for strawberry quality. Spectral, color, and textural features from hyperspectral reflectance imaging of 400–1000 nm was to develop the non-destructive detection approaches for SSC, pH, and VC of strawberries by integrating various multivariate methods as partial least-squares regression (PLSR), support vector regression, and locally weighted regression (LWR). SSC, pH, and VC of 120 strawberries were statistically analyzed to facilitate the partitioning of data sets, which helped optimize the model. PLSR, with spectral and color features, obtained the optimal prediction of SSC with determination coefficient of prediction (Rp2) of 0.9370 and the root mean square error of prediction (RMSEP) of 0.1145. Through spectral features, the best prediction for pH was obtained by LWR with Rp2 = 0.8493 and RMSEP = 0.0501. Combination of spectral and textural features with PLSR provided the best results of VC with Rp2 = 0.8769 and RMSEP = 0.0279. Competitive adaptive reweighted sampling and uninformative variable elimination (UVE) were used to select important variables from the above features. Based on the important variables, the accuracy of SSC, pH, and VC prediction both gain the promotion. Finally, the distribution maps of SSC, pH, and VC over time were generated, and the change trend of three quality parameters was observed. Thus, the proposed method can nondestructively and accurately determine SSC, pH, and VC of strawberries and is expected to design and construct the simple sensors for the above quality parameters of strawberries.

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

“…Figure 8 shows the results of the 1D ResNet for SSC determination. The results showed that the performances of 1D ResNet for SSC determination were not good enough compared with previous studies (Amodio et al, 2017;Chen et al, 2017;Shen et al, 2018;Mancini et al, 2020;Weng et al, 2020). The R 2 of the three sets were all over 0.55, indicating that the improvements on the performances should be conducted in future studies.…”

“…Shao et al (2020) used hyperspectral imaging to evaluate the strawberry ripeness. Weng et al (2020) used hyperspectral imaging to determine the soluble solid content (SSC), pH, and vitamin C in strawberry. Hyperspectral images can provide spectral and image information.…”

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

“…Unlike many researchers who only used the spectral information directly as input variables, Weng et al [91] extracted the spectral information about optimal wavelengths, 9 color features obtained from color histograms and moments, and 36 textural features simultaneously from the hyperspectral images for the detection of soluble solid content (SSC), pH, and vitamin C. Spectral and color features achieved the best prediction for SSC, with an R 2 coefficient of 0.94. In terms of pH, optimal prediction was obtained using spectral features only, with an R 2 of 0.85.…”

A Machine Learning Approach to Predict the Average Localization Error With Applications to Wireless Sensor Networks

“…The training samples influence the SVR model's fitting performance since the SVR algorithm is sensitive to the interference in the training data. Besides, SVR is useful in resolving high dimensional features regression problem, and well-function if the feature metrics is larger than the size of the sample [43]. In this study, we have extracted four features, namely anchor ratio, transmission range, node density and the number of iterations from modified CS algorithm simulation.…”