Apple Watercore Grade Classification Method Based on ConvNeXt and Visible/Near-Infrared Spectroscopy

Chunlin Zhao, Zhipeng Yin, Yushuo Tan (), Wenbin Zhang (), Panpan Guo, Yaxing Ma, Haijian Wu, Ding Hu and Quan Lu
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Chunlin Zhao: Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650093, China
Zhipeng Yin: Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650093, China
Yushuo Tan: Modern Postal College, ShiJiaZhuang Posts and Telecommunications Technical College, Shijiazhuang 050021, China
Wenbin Zhang: Faculty of Mechanical and Electrical Engineering, Kunming University, Kunming 650214, China
Panpan Guo: School of Rail Transportation, Soochow University, Suzhou 215131, China
Yaxing Ma: Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650093, China
Haijian Wu: Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650093, China
Ding Hu: Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650093, China
Quan Lu: Ninglang Hengtai Agricultural Investment and Development Co., Ltd., Lijiang 674300, China

Agriculture, 2025, vol. 15, issue 7, 1-20

Abstract: To address the issues of insufficient rigor in existing methods for quantifying apple watercore severity and the complexity and low accuracy of traditional classification models, this study proposes a method for watercore quantification and a classification model based on a deep convolutional neural network. Initially, visible/near-infrared transmission spectral data of apple samples were collected. The apples were then sliced into 4.5 mm thick sections using a specialized tool, and image data of each slice were captured. Using BiSeNet and RIFE algorithms, a three-dimensional model of the watercore regions was constructed from the apple slices to calculate the watercore severity, which was subsequently categorized into five distinct levels. Next, methods such as the Gramian Angular Summation Field (GASF), Gram Angular Difference Field (GADF), and Markov Transition Field (MTF) were applied to transform the one-dimensional spectral data into two-dimensional images. These images served as input for training and prediction using the ConvNeXt deep convolutional neural network. The results indicated that the GADF method yielded the best performance, achieving a test set accuracy of 98.73%. Furthermore, the study contrasted the classification and prediction of watercore apples using traditional methods with the existing quantification approaches for watercore levels. The comparative results demonstrated that the proposed GADF-ConvNeXt model is more straightforward and efficient, achieving superior performance in classifying watercore grades. Furthermore, the newly proposed quantification method for watercore levels proved to be more effective.

Keywords: watercore apples; visible/near-infrared spectroscopy; deep convolutional neural networks; image recognition; Gramian angular field (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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