Classification Learning of Latent Bruise Damage to Apples Using Shortwave Infrared Hyperspectral Imaging

Abstract

Bruise damage is a very commonly occurring defect in apple fruit which facilitates disease occurrence and spread, leads to fruit deterioration and can greatly contribute to postharvest loss. The detection of bruises at their earliest stage of development can be advantageous for screening purposes. An experiment to induce soft bruises in Golden Delicious apples was conducted by applying impact energy at different levels, which allowed to investigate the detectability of bruises at their latent stage. The existence of bruises that were rather invisible to the naked eye and to a digital camera was proven by reconstruction of hyperspectral images of bruised apples, based on effective wavelengths and data dimensionality reduced hyperspectrograms. Machine learning classifiers, namely ensemble subspace discriminant (ESD), k-nearest neighbors (KNN), support vector machine (SVM) and linear discriminant analysis (LDA) were used to build models for detecting bruises at their latent stage, to study the influence of time after bruise occurrence on detection performance and to model quantitative aspects of bruises (severity), spanning from latent to visible bruises. Over all classifiers, detection models had a higher performance than quantitative ones. Given its highest speed in prediction and high classification performance, SVM was rated most recommendable for detection tasks. However, ESD models had the highest classification accuracy in quantitative (>85%) models and were found to be relatively better suited for such a multiple category classification problem than the rest.

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.…”

Comparison Analysis of Machine Learning Classification on Apple Fruit Quality

“…The classification accuracy of the given model is 85%. In another research hyperspectral imaging [17] was used to detect bruise damage defect in apple fruit which is the most common defect in apples. The bruise cannot be detected by human vision and digital cameras.…”

Detection of skin defects in loquats based on grayscale features combined with reflectance, absorbance, and Kubelka–Munk spectra

“…Hyperspectral imaging technology, as a fast and nondestructive detection technology, has been widely used in fruit detection with the advantages of the integration of spectra and image 4,5 . However, in previous studies, hyperspectral imaging has generally used its reflectance spectral parameters to identify specific damage in fruits, including bruise damage in apple, 6 chilling injury in green bell peppers, 7 fungal infection in peach, 8 and tetranychus urticae in citrus leaves 9 . Xie et al 10 used spectral reflectance information of citrus and KNN algorithm combined with selected characteristic wavelengths, to establish a citrus black spot category discrimination model, with a classification accuracy of 100%.…”