Identification of apple watercore based on ConvNeXt and Vis/NIR spectra

IF 3.1 3区 物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION
Chunlin Zhao , Zhipeng Yin , Wenbin Zhang , Panpan Guo , Yaxing Ma
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引用次数: 0

Abstract

This paper proposes a method for discriminating between normal apples and watercore apples using visible/near-infrared spectroscopy technology, which combines Gramian Angular Fields (GAF) encoding technology and the ConvNeXt deep learning network. The existing feature extraction methods for visible/near-infrared spectroscopy data do not perform deeper information mining on the extracted features, which results in the quality of the established model being entirely determined by the extracted features. Additionally, the process of building a visible/near-infrared spectroscopy data classification model is complex and time-consuming, and the accuracy of the established model is not high. To address these issues, the experimental visible/near-infrared spectroscopy data of apples was first transformed into two-dimensional images using Gramian Angular Summation Fields (GASF) and Gramian Angular Difference Fields (GADF) with sizes of 64, 128, 256, and 512. These images were then input into the ConvNeXt network, and the performance of different encoding methods and sizes was compared. The results showed that, under the conditions provided in this paper, the GADF encoding method with a size of 256 achieved the highest classification accuracy of 98.48%. Next, ResNet, EfficientNet, and RegNet deep learning networks were selected to classify the encoded images under the same conditions. The results above indicate that the apple variety discrimination method based on GAF encoding technology and ConvNeXt network combined with visible/near-infrared spectroscopy technology can achieve deep information mining of visible/near-infrared spectroscopy data and provide a relatively simple method for establishing qualitative classification models of visible/near-infrared spectroscopy. This method has a relatively excellent discrimination effect between normal apples and watercore apples.
基于 ConvNeXt 和可见光/近红外光谱鉴定苹果水核
本文提出了一种利用可见光/近红外光谱技术区分正常苹果和水核苹果的方法,该方法结合了格拉米安角场(GAF)编码技术和 ConvNeXt 深度学习网络。现有的可见光/近红外光谱数据特征提取方法无法对提取的特征进行更深层次的信息挖掘,这导致所建立模型的质量完全由提取的特征决定。此外,可见光/近红外光谱数据分类模型的建立过程复杂耗时,建立的模型准确率不高。为解决这些问题,首先使用大小为 64、128、256 和 512 的革兰氏角求和场(GASF)和革兰氏角差场(GADF)将苹果的可见光/近红外光谱实验数据转换为二维图像。然后将这些图像输入 ConvNeXt 网络,比较不同编码方法和大小的性能。结果表明,在本文提供的条件下,大小为 256 的 GADF 编码方法的分类准确率最高,达到 98.48%。接下来,在相同条件下,选择 ResNet、EfficientNet 和 RegNet 深度学习网络对编码后的图像进行分类。上述结果表明,基于 GAF 编码技术和 ConvNeXt 网络的苹果品种判别方法结合可见光/近红外光谱技术,可以实现对可见光/近红外光谱数据的深度信息挖掘,为建立可见光/近红外光谱定性分类模型提供了一种相对简单的方法。该方法对正常苹果和水核苹果具有较好的鉴别效果。
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来源期刊
CiteScore
5.70
自引率
12.10%
发文量
400
审稿时长
67 days
期刊介绍: The Journal covers the entire field of infrared physics and technology: theory, experiment, application, devices and instrumentation. Infrared'' is defined as covering the near, mid and far infrared (terahertz) regions from 0.75um (750nm) to 1mm (300GHz.) Submissions in the 300GHz to 100GHz region may be accepted at the editors discretion if their content is relevant to shorter wavelengths. Submissions must be primarily concerned with and directly relevant to this spectral region. Its core topics can be summarized as the generation, propagation and detection, of infrared radiation; the associated optics, materials and devices; and its use in all fields of science, industry, engineering and medicine. Infrared techniques occur in many different fields, notably spectroscopy and interferometry; material characterization and processing; atmospheric physics, astronomy and space research. Scientific aspects include lasers, quantum optics, quantum electronics, image processing and semiconductor physics. Some important applications are medical diagnostics and treatment, industrial inspection and environmental monitoring.
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