Non-destructive estimation for Kyoho grape shelf-life using Vis/NIR hyperspectral imaging and deep learning algorithm

IF 3.1 3区 物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION
Min Xu , Jun Sun , Jiehong Cheng , Kunshan Yao , Lei Shi , Xin Zhou
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引用次数: 0

Abstract

Grape shelf-life estimation is a substantial challenge for the grape industry. The objective of this study is to investigate the potential of grape shelf-life estimation using HSI technique and a deep learning algorithm. The visible and near-infrared (400.68–1001.61 nm) hyperspectral reflectance images data of grape samples was acquired and preprocessed with different spectral preprocessing methods. Additionally, a stacked denoising autoencoder (SDAE)-based deep learning algorithm was developed to extract deep features from pixel-level hyperspectral data of grapes, and then these features were used as inputs to establish support vector machine (SVM) models for estimating grape shelf-life. Furthermore, SVM, one-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) models were used as traditional machine learning and end to end models for comparison. The results demonstrated that the SDAE-SVM model achieved reasonable recognition accuracy of 100 % and 98.125 % for the shelf-life of grapes in the training and test sets, respectively. The overall results suggested that SDAE-based deep learning method can be used as a powerful tool to deal with large-scale hyperspectral data as well as this research confirms the feasibility of non-destructive estimation for grapes shelf-life by the combination of HSI technique and deep learning method, which would provide a valuable guidance for shelf-life estimation of other postharvest fruit.

Abstract Image

利用可见光/近红外高光谱成像和深度学习算法对 Kyoho 葡萄的货架期进行非破坏性评估
葡萄货架期估算是葡萄产业面临的一项重大挑战。本研究旨在利用高光谱技术和深度学习算法研究葡萄货架期估算的潜力。研究人员获取了葡萄样本的可见光和近红外(400.68-1001.61 nm)高光谱反射率图像数据,并采用不同的光谱预处理方法对其进行了预处理。此外,还开发了基于堆叠去噪自动编码器(SDAE)的深度学习算法,从像素级葡萄高光谱数据中提取深度特征,然后将这些特征作为输入建立支持向量机(SVM)模型,用于估计葡萄的货架期。此外,还将 SVM、一维卷积神经网络(1D CNN)和长短期记忆(LSTM)模型作为传统机器学习和端到端模型进行了比较。结果表明,SDAE-SVM 模型对训练集和测试集中葡萄保质期的识别准确率分别达到了 100 % 和 98.125 %。总体结果表明,基于 SDAE 的深度学习方法可作为处理大规模高光谱数据的有力工具,该研究也证实了通过结合 HSI 技术和深度学习方法对葡萄货架期进行无损估计的可行性,这将为其他采后水果的货架期估计提供有价值的指导。
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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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