Identification of Damage in Pear Using Hyperspectral Imaging Technology

Abstract

Crown pears are an important economic crop, but their quality and economy are seriously affected by the different levels of damage. To improve the overall quality of crown pears, sorting of crown pears with different levels of damage is required. However, there are some shortcomings in the traditional detection methods, such as low efficiency and large error. Therefore, the hyperspectral technology was used to discriminate between sound and 3 different levels of damage (defined as level I, II, and III damage, respectively) of crown pears in this study. To improve the discriminatory accuracy of the model, absorbance (A) spectra and Kubelka–Munk (K-M) spectra were added to reflectance (R) spectra. The three spectra were pretreated; then, the partial least squares discriminant analysis (PLS-DA) model and the support vector machine (SVM) model were established to discriminate the crown pears with different levels of damage. The results of the discriminant model show that the discrimination accuracy of the SVM based on R, A, and K-M spectra is higher than that of PLS-DA of them; the A-RAW-SVM model has the best discrimination performance with an overall discrimination accuracy of 100% for the test and 98.98% for calibration sets, respectively. Finally, the spectra were selected by the competitive adaptive reweighted sampling (CARS) and the uninformative variables elimination (UVE) to obtain the characteristic wavelengths, and the SVM models were built based on the filtered R, A, and K-M. Their discrimination results show that the A-RAW-CARS-SVM model has the best discrimination ability, and the discrimination accuracies of the test and calibration sets of the model are 96.88% and 100%, respectively. The results show that the best discrimination of different levels of damage of crown pears is the SVM model based on a spectra. This study provides a theoretical basis and experimental basis for detecting the damage of crown pears using hyperspectral.

Detection of early bruises on apples using near‐infrared camera imaging technology combined with adaptive threshold segmentation algorithm

“…In recent years, the rapid development of machine learning, especially deep learning, has provided powerful tools and methods for solving practical problems in various fields. Traditional machine learning methods, such as support vector machines (Li et al, 2022; Su et al, 2022), random forests (Feng et al, 2022), k‐nearest neighbors (Nturambirwe et al, 2021), deep learning methods (Liu et al, 2022), and so forth, such as target detection algorithms (Yao et al, 2021; Yuan et al, 2022), semantic segmentation algorithm (Liang et al, 2022), and so forth, combined with machine vision systems have been widely used in the field of fruit bruise detection and have achieved significant results.…”