Classification of early mechanical damage over time in pears based on hyperspectral imaging and transfer learning

Abstract

Mechanical damage of fresh fruit caused by compression and collision during harvesting and transportation is an urgent problem in the agricultural industry. The purpose of this work was to detect early mechanical damage of pears using hyperspectral imaging technology and advanced modeling techniques of transfer learning and convolutional neural networks. The visible/near-infrared hyperspectral imaging system was applied to obtain the intact and damaged pears at three time points (2, 12, and 24 h) after compression or collision damage. After the hyperspectral images were preprocessed and feature-extracted, ImageNet was used to pre-train ConvNeXt network, and then, transfer learning strategy was applied from compression damage to collision damage to build the T_ConvNeXt model for classification. The results showed that the test set accuracy of fine-tuned ConvNeXt model was 96.88% for compression damage time. For the classification of collision damage time, the test set accuracy of T_ConvNeXt network reached 96.61% and was 3.64% higher than the fine-tuned ConvNeXt network. The number of training samples was proportionally reduced to verify the superiority of the T_ConvNeXt model, and the model was compared with conventional machine learning algorithms. This study achieved the classification of mechanical damage over time and achieved a generalized model for different damage types. The accurate prediction of pear damage time is crucial for determining proper storage conditions and shelf-life time.

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“…Following preprocessing and feature extraction from hyperspectral images of pears, Liu et al employed transfer learning to train the ConvNeXt model, successfully detecting early mechanical damage in pears [ 24 ]. Li et al enhanced ConvNeXt and devised a classification model for ginseng grades, addressing the issue of low variability between features in ginseng grade classification [ 25 ].…”

Deep transfer learning rolling bearing fault diagnosis method based on convolutional neural network feature fusion

“…Synchrosqueezed wavelet transforms (SWTs) are chosen as the time-frequency analysis method for this study due to their high resolution and denoising capability [35,36]. Among them, ConvNeXt is chosen as the time-frequency graph feature extraction network for this paper due to its excellent feature extraction capability and training efficiency [37,38]. Then, transfer learning-based fine-tuning methods can be used to effectively reduce the dependence of the model on labeled samples in the target domain.…”