Assessment of Strawberry Ripeness Using Hyperspectral Imaging

Abstract

Portable hyperspectral imaging was used for field and indoor spectra acquisition of the strawberries at three ripeness stages: ripe, mid-ripe and unripe. The mean spectra were pre-processed by multiplicative scatter correction (MSC). Principal component analysis (PCA) was employed to generate score scatter plots and visualize score images for differentiating specific grouping of samples. Three methods, including X-loading weight, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were applied to extract the effective wavelengths. Two classifiers, partial least squares – discriminant analysis (PLS-DA) and least squares – support vector machine (LS-SVM) were used for ripeness assessment. The results showed that the overall accuracy of all classifiers for field assessment ranged from 91.7% to 96.7%, slightly lower than for indoor assessment. Furthermore, the LS-SVM model combined with effective wavelengths with the CARS method performed better for field assessment of strawberry ripeness, providing an accuracy of 96.7%. It can be concluded that hyperspectral imaging can be used for real-time assessment of strawberry ripeness in the field.

Keywords:

• Field assessment
• hyperspectral imaging
• ripeness detection
• strawberry

Additional information

Funding

The study was supported by the National Natural Science Foundation of China (Grants 31701325 and 31671632).